diff --git a/.all-contributorsrc b/.all-contributorsrc
index 8a41f9cbe..7f624a7d6 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -260,6 +260,15 @@
       "contributions": [
         "code"
       ]
+    },
+    {
+      "login": "Vid201",
+      "name": "Vid Kersic",
+      "avatar_url": "https://avatars.githubusercontent.com/u/38610409?v=4",
+      "profile": "https://github.com/Vid201",
+      "contributions": [
+        "code"
+      ]
     }
   ],
   "contributorsPerLine": 7,
diff --git a/.tool-versions b/.tool-versions
new file mode 100644
index 000000000..21cfc8077
--- /dev/null
+++ b/.tool-versions
@@ -0,0 +1 @@
+scarb 2.4.0
diff --git a/README.md b/README.md
index d0b3055e4..1f48201d3 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,7 @@
 
 # Orion: An Open-source Framework for Validity and ZK ML ✨
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-28-orange.svg?style=flat-square)](#contributors-)
+[![All Contributors](https://img.shields.io/badge/all_contributors-29-orange.svg?style=flat-square)](#contributors-)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 Orion is an open-source, community-driven framework dedicated to Provable Machine Learning. It provides essential components and a new ONNX runtime for building verifiable Machine Learning models using [STARKs](https://starkware.co/stark/).
@@ -104,6 +104,9 @@ Thanks goes to these wonderful people:
       <td align="center" valign="top" width="14.28%"><a href="https://www.bilginkocak.com/"><img src="https://avatars.githubusercontent.com/u/30844607?v=4?s=100" width="100px;" alt="Bilgin Koçak"/><br /><sub><b>Bilgin Koçak</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=bilgin-kocak" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://t.me/notaihe"><img src="https://avatars.githubusercontent.com/u/22559023?v=4?s=100" width="100px;" alt="akhercha"/><br /><sub><b>akhercha</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=akhercha" title="Code">💻</a></td>
     </tr>
+    <tr>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/Vid201"><img src="https://avatars.githubusercontent.com/u/38610409?v=4?s=100" width="100px;" alt="Vid Kersic"/><br /><sub><b>Vid Kersic</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=Vid201" title="Code">💻</a></td>
+    </tr>
   </tbody>
 </table>
 
diff --git a/Scarb.toml b/Scarb.toml
index 6ebf1fd1f..cf6169b1f 100644
--- a/Scarb.toml
+++ b/Scarb.toml
@@ -1,6 +1,6 @@
 [package]
 name = "orion"
-version = "0.1.9"
+version = "0.2.0"
 cairo-version = "2.4.0"
 edition = "2023_10"
 description = "ONNX Runtime in Cairo for verifiable ML inference using STARK"
diff --git a/docgen/src/main.rs b/docgen/src/main.rs
index 20426c832..ed2a69460 100644
--- a/docgen/src/main.rs
+++ b/docgen/src/main.rs
@@ -19,6 +19,14 @@ fn main() {
     doc_trait(trait_path, doc_path, label);
     doc_functions(trait_path, doc_path, trait_name, label);
 
+    // SEQUENCE DOC
+    let trait_path = "src/operators/sequence/core.cairo";
+    let doc_path = "docs/framework/operators/sequence";
+    let label = "sequence";
+    let trait_name = "SequenceTrait";
+    doc_trait(trait_path, doc_path, label);
+    doc_functions(trait_path, doc_path, trait_name, label);
+
     // FIXED POINT DOC
     let trait_path = "src/numbers/fixed_point/core.cairo";
     let doc_path = "docs/framework/numbers/fixed-point";
@@ -66,6 +74,22 @@ fn main() {
     let trait_name: &str = "LinearRegressorTrait";
     doc_trait(trait_path, doc_path, label);
     doc_functions(trait_path, doc_path, trait_name, label);
+
+    // LINEAR CLASSIFIER DOC
+    let trait_path = "src/operators/ml/linear/linear_classifier.cairo";
+    let doc_path = "docs/framework/operators/machine-learning/linear-classifier";
+    let label = "linear_classifier";
+    let trait_name: &str = "LinearClassifierTrait";
+    doc_trait(trait_path, doc_path, label);
+    doc_functions(trait_path, doc_path, trait_name, label);
+
+    // SVM REGRESSOR DOC
+    let trait_path = "src/operators/ml/svm/svm_regressor.cairo";
+    let doc_path = "docs/framework/operators/machine-learning/svm-regressor";
+    let label = "svm_regressor";
+    let trait_name: &str = "SVMRegressorTrait";
+    doc_trait(trait_path, doc_path, label);
+    doc_functions(trait_path, doc_path, trait_name, label);
 }
 
 fn doc_trait(trait_path: &str, doc_path: &str, label: &str) {
diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md
index 9c22caa01..a7ec58d5f 100644
--- a/docs/CHANGELOG.md
+++ b/docs/CHANGELOG.md
@@ -4,7 +4,11 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased] - 2023-12-25
 
+## Added
+- Compress Operator.
+  
 ## [Unreleased] - 2023-12-14
 
 ## Added
diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
index 62ae2a2b3..f956159ec 100644
--- a/docs/SUMMARY.md
+++ b/docs/SUMMARY.md
@@ -100,6 +100,7 @@
     * [tensor.bitwise\_and](framework/operators/tensor/tensor.bitwise\_and.md)
     * [tensor.bitwise\_xor](framework/operators/tensor/tensor.bitwise\_xor.md)
     * [tensor.bitwise\_or](framework/operators/tensor/tensor.bitwise\_or.md)
+    * [tensor.resize](framework/operators/tensor/tensor.resize.md)
     * [tensor.round](framework/operators/tensor/tensor.round.md)
     * [tensor.scatter](framework/operators/tensor/tensor.scatter.md)
     * [tensor.array\_feature\_extractor](framework/operators/tensor/tensor.array\_feature\_extractor.md)
@@ -110,28 +111,24 @@
     * [tensor.reduce\_prod](framework/operators/tensor/tensor.reduce\_prod.md)
     * [tensor.gather\_elements](framework/operators/tensor/tensor.gather\_elements.md)
     * [tensor.gather\_nd](framework/operators/tensor/tensor.gather\_nd.md)
-    * [tensor.sequence\_length](framework/operators/tensor/tensor.sequence\_length.md)
-    * [tensor.sequence\_at](framework/operators/tensor/tensor.sequence\_at.md)
     * [tensor.reduce\_min](framework/operators/tensor/tensor.reduce\_min.md)
-    * [tensor.sequence\_construct](framework/operators/tensor/tensor.sequence\_construct.md)
     * [tensor.shrink](framework/operators/tensor/tensor.shrink.md)
-    * [tensor.sequence\_empty](framework/operators/tensor/tensor.sequence\_empty.md)
     * [tensor.reduce\_mean](framework/operators/tensor/tensor.reduce\_mean.md)
     * [tensor.pow](framework/operators/tensor/tensor.pow.md)
-    * [tensor.sequence\_erase](framework/operators/tensor/tensor.sequence\_erase.md)
-    * [tensor.sequence\_insert](framework/operators/tensor/tensor.sequence\_insert.md)
-    * [tensor.concat\_from\_sequence](framework/operators/tensor/tensor.concat\_from\_sequence.md)
     * [tensor.is\_nan](framework/operators/tensor/tensor.is\_nan.md)
     * [tensor.is\_inf](framework/operators/tensor/tensor.is\_inf.md)
     * [tensor.not](framework/operators/tensor/tensor.not.md)
     * [tensor.erf](framework/operators/tensor/tensor.erf.md)
     * [tensor.reduce\_log\_sum](framework/operators/tensor/tensor.reduce\_log\_sum.md)
     * [tensor.unique](framework/operators/tensor/tensor.unique.md)
+    * [tensor.compress](framework/operators/tensor/tensor.compress.md)
+    * [tensor.layer_normalization](framework/operators/tensor/tensor.layer_normalization.md)
   * [Neural Network](framework/operators/neural-network/README.md)
     * [nn.relu](framework/operators/neural-network/nn.relu.md)
     * [nn.leaky\_relu](framework/operators/neural-network/nn.leaky\_relu.md)
     * [nn.sigmoid](framework/operators/neural-network/nn.sigmoid.md)
     * [nn.softmax](framework/operators/neural-network/nn.softmax.md)
+    * [nn.softmax_zero](framework/operators/neural-network/nn.softmax_zero.md)
     * [nn.logsoftmax](framework/operators/neural-network/nn.logsoftmax.md)
     * [nn.softsign](framework/operators/neural-network/nn.softsign.md)
     * [nn.softplus](framework/operators/neural-network/nn.softplus.md)
@@ -142,6 +139,24 @@
   * [Machine Learning](framework/operators/machine-learning/README.md)
     * [Tree Ensemble Classifier](framework/operators/machine-learning/tree-ensemble-classifier/README.md)
       * [tree\_ensemble\_classifier.predict](framework/operators/machine-learning/tree-ensemble-classifier/tree\_ensemble\_classifier.predict.md)
+    * [Tree Ensemble Regressor](framework/operators/machine-learning/tree-ensemble-regressor/README.md)
+      * [tree\_ensemble\_regressor.predict](framework/operators/machine-learning/tree-ensemble-regressor/tree\_ensemble\_regressor.predict.md)
+    * [Linear Classifier](framework/operators/machine-learning/linear-classifier/README.md)
+      * [linear\_classifier.predict](framework/operators/machine-learning/linear-classifier/linear\_classifier.predict.md)
+    * [Linear Regressor](framework/operators/machine-learning/linear-regressor/README.md)
+      * [linear\_regressor.predict](framework/operators/machine-learning/linear-regressor/linear\_regressor.predict.md)
+    * [SVM Regressor](framework/operators/machine-learning/svm-regressor/README.md)
+      * [svm\_regressor.predict](framework/operators/machine-learning/svm-regressor/svm\_regressor.predict.md)
+    * [Sequence](framework/operators/sequence/README.md)
+      * [sequence.sequence\_construct](framework/operators/sequence/sequence.sequence\_construct.md)
+      * [sequence.sequence\_empty](framework/operators/sequence/sequence.sequence\_empty.md)
+      * [sequence.sequence\_length](framework/operators/sequence/sequence.sequence\_length.md)
+      * [sequence.sequence\_at](framework/operators/sequence/sequence.sequence\_at.md)
+      * [sequence.sequence\_empty](framework/operators/sequence/sequence.sequence\_empty.md)
+      * [sequence.sequence\_erase](framework/operators/sequence/sequence.sequence\_erase.md)
+      * [sequence.sequence\_insert](framework/operators/sequence/sequence.sequence\_insert.md)
+      * [sequence.concat\_from\_sequence](framework/operators/sequence/sequence.concat\_from\_sequence.md)
+
 
 ## 🏛 Hub
 
diff --git a/docs/academy/tutorials/provable-mlr-forecasting-aaves-lifetime-repayments.md b/docs/academy/tutorials/provable-mlr-forecasting-aaves-lifetime-repayments.md
index 54b35181e..68c4daac6 100644
--- a/docs/academy/tutorials/provable-mlr-forecasting-aaves-lifetime-repayments.md
+++ b/docs/academy/tutorials/provable-mlr-forecasting-aaves-lifetime-repayments.md
@@ -205,7 +205,7 @@ X_min = np.min(X_original, axis=0)
 X_max = np.max(X_original, axis=0)
 X_range = X_max - X_min
 df_forecast_data_normalized =    (df_forecast_data - X_min) / X_range
-# tranpose the matrix and add bias 
+# transpose the matrix and add bias 
 df_forecast_data_normalized_transposed= df_forecast_data_normalized.T
 df_forecast_data_normalized_transposed_with_bias = np.vstack((df_forecast_data_normalized_transposed, np.ones(df_forecast_data_normalized_transposed.shape[1])))
 #normalized forecasts 
@@ -913,7 +913,7 @@ fn normalize_user_x_inputs(
             shape: array![data_len].span(), data: x_range.span()
         );
 
-        // for normalizing 2D user inputed feature vals
+        // for normalizing 2D user inputted feature vals
         if x_inputs.shape.len() > 1 {
             let mut j: u32 = 0;
             loop {
@@ -1069,7 +1069,7 @@ let mut rescale_forecasts = rescale_predictions(forecast_results, main_y_vals);
 
 Our model will get tested under the `multiple_linear_regression_test()` function which will follow these steps:
 
-1. Data retrival: The function initiates by fetching the AAVE dataset's x and y values.
+1. Data retrieval: The function initiates by fetching the AAVE dataset's x and y values.
 2. Dataset construction and normalization: A new Dataset object gets initialized by passing the x and y variables. It is then normalized using the built-in `normalize_dataset()` method.
 3. Model fitting: Using the `MultipleLinearRegression` function we fit the normalized dataset and compute the regression coefficients.
 4. Computing accuracy of the model: To calculate the accuracy we utilize the `predict` method to compute the dot product between the model's regression coefficients and the x values. We then compute the R-squared score to measure the accuracy of our model.
diff --git a/docs/framework/compatibility.md b/docs/framework/compatibility.md
index 68cd44241..0e0e5be17 100644
--- a/docs/framework/compatibility.md
+++ b/docs/framework/compatibility.md
@@ -37,6 +37,7 @@ You can see below the list of current supported ONNX Operators:
 |      [ThresholdedRelu](operators/neural-network/nn.thresholded\_relu.md)      | :white\_check\_mark: |
 |               [Sigmoid](operators/neural-network/nn.sigmoid.md)               | :white\_check\_mark: |
 |               [Softmax](operators/neural-network/nn.softmax.md)               | :white\_check\_mark: |
+|           [Softmax_zero](operators/neural-network/nn.softmax_zero.md)         | :white\_check\_mark: |
 |            [LogSoftmax](operators/neural-network/nn.logsoftmax.md)            | :white\_check\_mark: |
 |              [Softsign](operators/neural-network/nn.softsign.md)              | :white\_check\_mark: |
 |              [Softplus](operators/neural-network/nn.softplus.md)              | :white\_check\_mark: |
@@ -77,6 +78,7 @@ You can see below the list of current supported ONNX Operators:
 |             [BitwiseAnd](operators/tensor/tensor.bitwise_and.md)              | :white\_check\_mark: |
 |              [BitwiseOr](operators/tensor/tensor.bitwise_or.md)               | :white\_check\_mark: |
 |             [BitwiseXor](operators/tensor/tensor.bitwise_xor.md)              | :white\_check\_mark: |
+|                  [Resize](operators/tensor/tensor.resize.md)                  | :white\_check\_mark: |
 |                   [Round](operators/tensor/tensor.round.md)                   | :white\_check\_mark: |
 |           [MaxInTensor](operators/tensor/tensor.max\_in\_tensor.md)           | :white\_check\_mark: |
 |                     [Max](operators/tensor/tensor.max.md)                     | :white\_check\_mark: |
@@ -89,20 +91,22 @@ You can see below the list of current supported ONNX Operators:
 |               [ReduceL1](operators/tensor/tensor.reduce\_l1.md)               | :white\_check\_mark: |
 |               [ReduceL2](operators/tensor/tensor.reduce\_l2.md)               | :white\_check\_mark: |
 |         [GatherElements](operators/tensor/tensor.gather/_elements.md)         | :white\_check\_mark: |
-|         [SequenceLength](operators/tensor/tensor.sequence\_length.md)         | :white\_check\_mark: |
-|             [SequenceAt](operators/tensor/tensor.sequence\_at.md)             | :white\_check\_mark: |
-|      [SequenceConstruct](operators/tensor/tensor.sequence\_construct.md)      | :white\_check\_mark: |
+|       [SequenceLength](operators/sequence/sequence.sequence\_length.md)       | :white\_check\_mark: |
+|           [SequenceAt](operators/sequence/sequence.sequence\_at.md)           | :white\_check\_mark: |
+|    [SequenceConstruct](operators/sequence/sequence.sequence\_construct.md)    | :white\_check\_mark: |
 |                  [Shrink](operators/tensor/tensor.shrink.md)                  | :white\_check\_mark: |
-|          [SequenceEmpty](operators/tensor/tensor.sequence\_empty.md)          | :white\_check\_mark: |
+|        [SequenceEmpty](operators/sequence/sequence.sequence\_empty.md)        | :white\_check\_mark: |
 |               [ReduceL2](operators/tensor/tensor.reduce\_l2.md)               | :white\_check\_mark: |
-|          [SequenceErase](operators/tensor/tensor.sequence\_erase.md)          | :white\_check\_mark: |
-|         [SequenceInsert](operators/tensor/tensor.sequence\_insert.md)         | :white\_check\_mark: |
-|    [ConcatFromSequence](operators/tensor/tensor.concat\_from\_sequence.md)    | :white\_check\_mark: |
+|        [SequenceErase](operators/sequence/sequence.sequence\_erase.md)        | :white\_check\_mark: |
+|       [SequenceInsert](operators/sequence/sequence.sequence\_insert.md)       | :white\_check\_mark: |
+|  [ConcatFromSequence](operators/sequence/sequence.concat\_from\_sequence.md)  | :white\_check\_mark: |
 |                  [IsNaN](operators/tensor/tensor.is\_nan.md)                  | :white\_check\_mark: |
 |                  [IsInf](operators/tensor/tensor.is\_inf.md)                  | :white\_check\_mark: |
 |                     [Not](operators/tensor/tensor.not.md)                     | :white\_check\_mark: |
 |              [GatherND](operators/tensor/tensor.gather/_nd.md)                | :white\_check\_mark: |
 |       [ReduceLogSum](operators/tensor/tensor.reduce\_log\_sum.md)             | :white\_check\_mark: |
-|       [Erf](operators/tensor/tensor.erf.md)                                   | :white\_check\_mark: |
+|               [Erf](operators/tensor/tensor.erf.md)                           | :white\_check\_mark: |
+|        [Compress](operators/tensor/tensor.compress.md)                        | :white\_check\_mark: |
+|     [Layer_normalization](operators/tensor/tensor.layer_normalization.md)     | :white\_check\_mark: |
 
-Current Operators support: **96/156 (62%)**
+Current Operators support: **97/156 (62%)**
diff --git a/docs/framework/operators/machine-learning/linear-classifier/README.md b/docs/framework/operators/machine-learning/linear-classifier/README.md
new file mode 100644
index 000000000..7323f8b7f
--- /dev/null
+++ b/docs/framework/operators/machine-learning/linear-classifier/README.md
@@ -0,0 +1,23 @@
+# Linear Classifier
+
+`LinearClassifierTrait` provides a trait definition for linear classification problem.
+
+```rust
+use orion::operators::ml::LinearClassificationTrait;
+```
+
+### Data types
+
+Orion supports currently only fixed point data types for `LinearClassificationTrait`.
+
+| Data type            | dtype                                                         |
+| -------------------- | ------------------------------------------------------------- |
+| Fixed point (signed) | `LinearClassifierTrait<FP8x23 \| FP16x16 \| FP64x64 \| FP32x32>` |
+
+
+***
+
+| function | description |
+| --- | --- |
+| [`linear_classifier.predict`](linear_classifier.predict.md) | Performs the linear classification. |
+
diff --git a/docs/framework/operators/machine-learning/linear-classifier/linear_classifier.predict.md b/docs/framework/operators/machine-learning/linear-classifier/linear_classifier.predict.md
new file mode 100644
index 000000000..aec154f68
--- /dev/null
+++ b/docs/framework/operators/machine-learning/linear-classifier/linear_classifier.predict.md
@@ -0,0 +1,100 @@
+# LinearClassifierTrait::predict
+
+```rust 
+   fn predict(ref self: LinearClassifier<T>, X: Tensor<T>) -> Tensor<T>;
+```
+
+Linear Classifier. Performs the linear classification.
+
+## Args
+
+* `self`: LinearClassifier<T> - A LinearClassifier object.
+* `X`:  Input 2D tensor.
+
+## Returns
+
+* Tensor<T> containing the linear classification evaluation of the input X.
+
+## Type Constraints
+
+`LinearClassifier` and `X` must be fixed points
+
+## Examples
+
+```rust
+use orion::numbers::FP16x16;
+use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor, U32Tensor};
+
+use orion::operators::ml::linear::linear_classifier::{
+    LinearClassifierTrait, POST_TRANSFORM, LinearClassifier
+};
+
+fn linear_classifier_helper(
+    post_transform: POST_TRANSFORM
+) -> (LinearClassifier<FP16x16>, Tensor<FP16x16>) {
+
+    let classlabels: Span<usize> = array![0, 1, 2].span();
+    let classlabels = Option::Some(classlabels);
+
+    let classlabels_strings: Option<Span<FP16x16>> = Option::None;
+
+    let coefficients: Span<FP16x16> = array![
+        FP16x16 { mag: 38011, sign: true },
+        FP16x16 { mag: 19005, sign: true },
+        FP16x16 { mag: 5898, sign: true },
+        FP16x16 { mag: 38011, sign: false },
+        FP16x16 { mag: 19005, sign: false },
+        FP16x16 { mag: 5898, sign: false }, 
+    ]
+        .span();
+
+    let intercepts: Span<FP16x16> = array![
+        FP16x16 { mag: 176947, sign: false },
+        FP16x16 { mag: 176947, sign: true },
+        FP16x16 { mag: 32768, sign: false },
+    ]
+        .span();
+    let intercepts = Option::Some(intercepts);
+
+    let multi_class: usize = 0;
+
+    let mut classifier: LinearClassifier<FP16x16> = LinearClassifier {
+        classlabels,
+        coefficients,
+        intercepts,
+        multi_class,
+        post_transform
+        };
+
+    let mut X: Tensor<FP16x16> = TensorTrait::new(
+        array![3, 2].span(),
+        array![
+            FP16x16 { mag: 0, sign: false },
+            FP16x16 { mag: 65536, sign: false },
+            FP16x16 { mag: 131072, sign: false },
+            FP16x16 { mag: 196608, sign: false },
+            FP16x16 { mag: 262144, sign: false },
+            FP16x16 { mag: 327680, sign: false },
+        ]
+            .span()
+    );
+
+    (classifier, X)
+}
+
+fn linear_classifier_multi_softmax() -> (Span<usize>, Tensor<FP16x16>) {
+    let (mut classifier, X) = linear_classifier_helper(POST_TRANSFORM::SOFTMAX);
+
+    let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+
+    (labels, scores)
+}
+
+>>> 
+([0, 2, 2],
+ [
+    [0.852656, 0.009192, 0.138152],
+    [0.318722, 0.05216, 0.629118],
+    [0.036323, 0.090237, 0.87344]
+ ])
+```
\ No newline at end of file
diff --git a/docs/framework/operators/machine-learning/svm-regressor/README.md b/docs/framework/operators/machine-learning/svm-regressor/README.md
new file mode 100644
index 000000000..f659cbbd1
--- /dev/null
+++ b/docs/framework/operators/machine-learning/svm-regressor/README.md
@@ -0,0 +1,23 @@
+# SVM Regressor
+
+`SVMRegressorTrait` provides a trait definition for svm regression problem.
+
+```rust
+use orion::operators::ml::SVMRegressorTrait;
+```
+
+### Data types
+
+Orion supports currently only fixed point data types for `SVMRegressorTrait`.
+
+| Data type            | dtype                                                         |
+| -------------------- | ------------------------------------------------------------- |
+| Fixed point (signed) | `SVMRegressorTrait<FP8x23 \| FP16x16 \| FP64x64 \| FP32x32>` |
+
+
+***
+
+| function | description |
+| --- | --- |
+| [`svm_regressor.predict`](svm_regressor.predict.md) | Returns the regressed values for each input in N. |
+
diff --git a/docs/framework/operators/machine-learning/svm-regressor/svm_regressor.predict.md b/docs/framework/operators/machine-learning/svm-regressor/svm_regressor.predict.md
new file mode 100644
index 000000000..68be4c922
--- /dev/null
+++ b/docs/framework/operators/machine-learning/svm-regressor/svm_regressor.predict.md
@@ -0,0 +1,111 @@
+# SVMRegressorTrait::predict
+
+```rust 
+   fn predict(ref self: SVMRegressor<T>, X: Tensor<T>) -> Tensor<T>;
+```
+
+Support Vector Machine regression prediction and one-class SVM anomaly detection.
+
+## Args
+
+* `self`: SVMRegressor<T> - A SVMRegressor object.
+* `X`:  Input 2D tensor.
+
+## Returns
+
+* Tensor<T> containing the Support Vector Machine regression prediction and one-class SVM anomaly detection of the input X.
+
+## Type Constraints
+
+`SVMRegressor` and `X` must be fixed points
+
+## Examples
+
+```rust
+use orion::numbers::FP16x16;
+use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor, U32Tensor};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+
+use orion::operators::ml::svm::svm_regressor::{SVMRegressorTrait, POST_TRANSFORM, SVMRegressor};
+use orion::operators::ml::svm::core::{KERNEL_TYPE};
+
+fn example_svm_regressor_linear() -> Tensor<FP16x16> {
+    let coefficients: Span<FP16x16> = array![
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 65536, sign: true },
+        FP16x16 { mag: 54959, sign: false },
+        FP16x16 { mag: 54959, sign: true },
+        FP16x16 { mag: 29299, sign: false },
+        FP16x16 { mag: 65536, sign: true },
+        FP16x16 { mag: 36236, sign: false }
+    ]
+        .span();
+    let n_supports: usize = 7;
+    let one_class: usize = 0;
+    let rho: Span<FP16x16> = array![FP16x16 { mag: 35788, sign: false }].span();
+    let support_vectors: Span<FP16x16> = array![
+        FP16x16 { mag: 8421, sign: true },
+        FP16x16 { mag: 5842, sign: false },
+        FP16x16 { mag: 4510, sign: false },
+        FP16x16 { mag: 5202, sign: true },
+        FP16x16 { mag: 14783, sign: true },
+        FP16x16 { mag: 17380, sign: true },
+        FP16x16 { mag: 60595, sign: false },
+        FP16x16 { mag: 1674, sign: true },
+        FP16x16 { mag: 38669, sign: true },
+        FP16x16 { mag: 63803, sign: false },
+        FP16x16 { mag: 87720, sign: true },
+        FP16x16 { mag: 22236, sign: false },
+        FP16x16 { mag: 61816, sign: false },
+        FP16x16 { mag: 34267, sign: true },
+        FP16x16 { mag: 36418, sign: false },
+        FP16x16 { mag: 27471, sign: false },
+        FP16x16 { mag: 28421, sign: false },
+        FP16x16 { mag: 69270, sign: true },
+        FP16x16 { mag: 152819, sign: false },
+        FP16x16 { mag: 4065, sign: false },
+        FP16x16 { mag: 62274, sign: true }
+    ]
+        .span();
+    let post_transform = POST_TRANSFORM::NONE;
+    let kernel_params: Span<FP16x16> = array![
+        FP16x16 { mag: 27812, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 196608, sign: false }
+    ]
+        .span();
+    let kernel_type = KERNEL_TYPE::LINEAR;
+
+    let mut regressor: SVMRegressor<FP16x16> = SVMRegressor {
+        coefficients,
+        kernel_params,
+        kernel_type,
+        n_supports,
+        one_class,
+        post_transform,
+        rho,
+        support_vectors,
+    };
+
+    let mut X: Tensor<FP16x16> = TensorTrait::new(
+        array![3, 3].span(),
+        array![
+            FP16x16 { mag: 32768, sign: true },
+            FP16x16 { mag: 26214, sign: true },
+            FP16x16 { mag: 19660, sign: true },
+            FP16x16 { mag: 13107, sign: true },
+            FP16x16 { mag: 6553, sign: true },
+            FP16x16 { mag: 0, sign: false },
+            FP16x16 { mag: 6553, sign: false },
+            FP16x16 { mag: 13107, sign: false },
+            FP16x16 { mag: 19660, sign: false },
+        ]
+            .span()
+    );
+
+    return SVMRegressorTrait::predict(ref regressor, X);
+}
+
+>>> [[-0.468206], [0.227487], [0.92318]]
+```
+
diff --git a/docs/framework/operators/machine-learning/tree-ensemble-regressor/README.md b/docs/framework/operators/machine-learning/tree-ensemble-regressor/README.md
index cbd3747cf..ae21bfcb1 100644
--- a/docs/framework/operators/machine-learning/tree-ensemble-regressor/README.md
+++ b/docs/framework/operators/machine-learning/tree-ensemble-regressor/README.md
@@ -14,6 +14,9 @@ Orion supports currently only fixed point data types for `TreeEnsembleRegressorT
 | -------------------- | ------------------------------------------------------------- |
 | Fixed point (signed) | `TreeRegressorTrait<FP8x23 \| FP16x16 \| FP64x64 \| FP32x32>` |
 
+### How to construct `TreeEnsembleRegressor` 
+
+You can utilize [this notebook](https://colab.research.google.com/drive/1zZC0tM7I5Mt542_cBsxaWcGPWzgxybGs?usp=sharing#scrollTo=VkXxLxDejrf3) to translate parameters from your ONNX TreeEnsembleRegressor model into Cairo code. Efforts are underway to integrate this functionality into Giza-CLI, aiming to enhance the user experience.
 
 ***
 
diff --git a/docs/framework/operators/neural-network/README.md b/docs/framework/operators/neural-network/README.md
index cd1c92f8d..8343d0c90 100644
--- a/docs/framework/operators/neural-network/README.md
+++ b/docs/framework/operators/neural-network/README.md
@@ -27,6 +27,7 @@ Orion supports currently these `NN` types.
 | [`nn.leaky_relu`](nn.leaky\_relu.md) | Applies the leaky rectified linear unit (Leaky ReLU) activation function element-wise. |
 | [`nn.sigmoid`](nn.sigmoid.md) | Applies the Sigmoid function to an n-dimensional input tensor. |
 | [`nn.softmax`](nn.softmax.md) | Computes softmax activations. |
+| [`nn.softmax_zero`](nn.softmax\_zero.md) | Computes softmax zero. |
 | [`nn.logsoftmax`](nn.logsoftmax.md) | Applies the natural log to Softmax function to an n-dimensional input Tensor. |
 | [`nn.softsign`](nn.softsign.md) | Applies the Softsign function element-wise. |
 | [`nn.softplus`](nn.softplus.md) | Applies the Softplus function element-wise. |
diff --git a/docs/framework/operators/neural-network/nn.softmax_zero.md b/docs/framework/operators/neural-network/nn.softmax_zero.md
new file mode 100644
index 000000000..f5cc9159b
--- /dev/null
+++ b/docs/framework/operators/neural-network/nn.softmax_zero.md
@@ -0,0 +1,64 @@
+# NNTrait::softmax_zero
+
+```rust 
+   fn softmax_zero(tensor: @Tensor<T>, axis: usize) -> Tensor<T>;
+```
+
+Applies the Softmax zero function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range \[0,1] and sum to 1 while keeping the zero elements to zero.
+
+The softmax zero on the set $\mathbf{x} = (x_1, ..., x_n)$ is given by :
+
+$$
+\text{softmax zero}(x_i) = \begin{cases}
+0 & \qquad x_i = 0 \\
+\frac{e^{x_i}}{ \sum_{x \in {S}} e^{x}} & \qquad \text{otherwise} 
+\end{cases}
+$$
+where $S$ in a subset of $\mathbf{x}$ given by 
+
+$$
+ \  S = \{ (x_1, \ldots, x_k) \mid 1 \leq k \leq n, x_j \neq 0 \text{ for } 1 \leq j \leq k \}
+$$
+
+## Args
+
+* `tensor`(`@Tensor<T>`) - The input tensor.
+* `axis`(`usize`) - The axis along which to compute the softmax zero.
+
+## Returns
+
+A Tensor of fixed point numbers with the same shape than the input Tensor.
+
+## Type Constraints
+
+Constrain input and output types to fixed point tensors.
+
+## Examples
+
+```rust
+use core::array::{ArrayTrait, SpanTrait};
+
+use orion::operators::tensor::{TensorTrait, Tensor, FP8x23Tensor};
+use orion::operators::nn::{NNTrait, FP8x23NN};
+use orion::numbers::{FP8x23, FixedTrait};
+
+use core::debug::PrintTrait;
+
+fn softmax_zero_example() -> Tensor<FP8x23> {
+    let tensor = TensorTrait::<FP8x23>::new(
+        shape: array![2, 2].span(),
+        data: array![
+            FixedTrait::new(0, false),
+            FixedTrait::new(8388608, false),
+            FixedTrait::new(16777216, false),
+            FixedTrait::new(25165824, false),
+        ]
+            .span(),
+    );
+
+    return NNTrait::softmax_zero(@tensor, 1);
+}
+>>> [[0,0x800000],[2256043,6132564]]
+    // The fixed point representation of
+    // [[0, 1],[0.2689, 0.7311]]
+```
diff --git a/docs/framework/operators/sequence/README.md b/docs/framework/operators/sequence/README.md
new file mode 100644
index 000000000..6b3599e84
--- /dev/null
+++ b/docs/framework/operators/sequence/README.md
@@ -0,0 +1,32 @@
+# Sequence
+
+A Sequence represents an array of tensors.
+
+```rust
+use orion::operators::sequence;
+```
+
+### Data types
+
+Orion supports currently these `Sequence` types.
+
+| Data type                 | dtype                                                    |
+| ------------------------- | -------------------------------------------------------- |
+| 32-bit integer (signed)   | `Array<Tensor<i32>>`                                     |
+| 8-bit integer (signed)    | `Array<Tensor<i8>>`                                      |
+| 32-bit integer (unsigned) | `Array<Tensor<u32>>`                                     |
+| Fixed point (signed)      | `Array<Tensor<FP8x23 \| FP16x16 \| FP32x32 \| FP64x64>>` |
+
+### Sequence**Trait**
+
+`SequenceTrait` defines the operations that can be performed on a Sequence of tensors.
+
+| function | description |
+| --- | --- |
+| [`sequence.sequence_construct`](sequence.sequence\_construct.md) | Constructs a tensor sequence containing the input tensors. |
+| [`sequence.sequence_empty`](sequence.sequence\_empty.md) | Returns an empty tensor sequence. |
+| [`sequence.sequence_length`](sequence.sequence\_length.md) | Returns the length of the input sequence. |
+| [`sequence.sequence_insert`](sequence.sequence\_insert.md) | Insert a tensor into a sequence. |
+| [`sequence.sequence_at`](sequence.sequence\_at.md) | Outputs the tensor at the specified position in the input sequence. |
+| [`sequence.concat_from_sequence`](sequence.concat\_from\_sequence.md) | Concatenate a sequence of tensors into a single tensor. |
+
diff --git a/docs/framework/operators/tensor/tensor.concat_from_sequence.md b/docs/framework/operators/sequence/sequence.concat_from_sequence.md
similarity index 100%
rename from docs/framework/operators/tensor/tensor.concat_from_sequence.md
rename to docs/framework/operators/sequence/sequence.concat_from_sequence.md
diff --git a/docs/framework/operators/tensor/tensor.sequence_at.md b/docs/framework/operators/sequence/sequence.sequence_at.md
similarity index 100%
rename from docs/framework/operators/tensor/tensor.sequence_at.md
rename to docs/framework/operators/sequence/sequence.sequence_at.md
diff --git a/docs/framework/operators/tensor/tensor.sequence_construct.md b/docs/framework/operators/sequence/sequence.sequence_construct.md
similarity index 78%
rename from docs/framework/operators/tensor/tensor.sequence_construct.md
rename to docs/framework/operators/sequence/sequence.sequence_construct.md
index d5e627bd1..92f005020 100644
--- a/docs/framework/operators/tensor/tensor.sequence_construct.md
+++ b/docs/framework/operators/sequence/sequence.sequence_construct.md
@@ -1,4 +1,4 @@
-## tensor.sequence_construct
+## sequence.sequence_construct
 
 ```rust 
    fn sequence_construct(tensors: Array<Tensor<T>>) -> Array<Tensor<T>>;
@@ -21,14 +21,15 @@ A tensor sequence `Array<Tensor<T>>` containing the input tensors.
 ## Examples
 
 ```rust
-use core::array::{ArrayTrait, SpanTrait};
+use array::{ArrayTrait, SpanTrait};
 
 use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};
+use orion::operators::sequence::SequenceTrait;
 
 fn sequence_construct_example() -> Array<Tensor<usize>> {
     let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span());
     let tensor2 = TensorTrait::new(shape: array![2, 2].span(), data: array![4, 5, 6, 7].span());
-    let result = TensorTrait::sequence_construct(tensors: array![tensor1, tensor2]);
+    let result = SequenceTrait::sequence_construct(tensors: array![tensor1, tensor2]);
     return result;
 }
 >>> [[0, 1, 2, 3], [4, 5, 6, 7]]
diff --git a/docs/framework/operators/tensor/tensor.sequence_empty.md b/docs/framework/operators/sequence/sequence.sequence_empty.md
similarity index 73%
rename from docs/framework/operators/tensor/tensor.sequence_empty.md
rename to docs/framework/operators/sequence/sequence.sequence_empty.md
index 60ea380e5..8c2568759 100644
--- a/docs/framework/operators/tensor/tensor.sequence_empty.md
+++ b/docs/framework/operators/sequence/sequence.sequence_empty.md
@@ -1,4 +1,4 @@
-# tensor.sequence_empty
+## sequence.sequence_empty
 
 ```rust
    fn sequence_empty() -> Array<Tensor<T>>;
@@ -17,16 +17,17 @@ An empty `Array<Tensor<T>>` instance.
 Let's create a new empty sequence.
 
 ```rust
-use core::array::{ArrayTrait, SpanTrait};
+use array::{ArrayTrait, SpanTrait};
 
 use orion::operators::tensor::{
     TensorTrait, // we import the trait
     Tensor, // we import the type
     U32Tensor // we import the implementation. 
 };
+use orion::operators::sequence::SequenceTrait;
 
 fn sequence_empty_example() -> Array<Tensor<u32>> {
-    let sequence = TensorTrait::sequence_empty();
+    let sequence = SequenceTrait::sequence_empty();
 
     return sequence;
 }
diff --git a/docs/framework/operators/tensor/tensor.sequence_erase.md b/docs/framework/operators/sequence/sequence.sequence_erase.md
similarity index 100%
rename from docs/framework/operators/tensor/tensor.sequence_erase.md
rename to docs/framework/operators/sequence/sequence.sequence_erase.md
diff --git a/docs/framework/operators/tensor/tensor.sequence_insert.md b/docs/framework/operators/sequence/sequence.sequence_insert.md
similarity index 100%
rename from docs/framework/operators/tensor/tensor.sequence_insert.md
rename to docs/framework/operators/sequence/sequence.sequence_insert.md
diff --git a/docs/framework/operators/tensor/tensor.sequence_length.md b/docs/framework/operators/sequence/sequence.sequence_length.md
similarity index 100%
rename from docs/framework/operators/tensor/tensor.sequence_length.md
rename to docs/framework/operators/sequence/sequence.sequence_length.md
diff --git a/docs/framework/operators/tensor/README.md b/docs/framework/operators/tensor/README.md
index 777e00b78..281135f63 100644
--- a/docs/framework/operators/tensor/README.md
+++ b/docs/framework/operators/tensor/README.md
@@ -98,6 +98,7 @@ use orion::operators::tensor::TensorTrait;
 | [`tensor.bitwise_and`](tensor.bitwise\_and.md) | Computes the bitwise AND of two tensors element-wise. |
 | [`tensor.bitwise_xor`](tensor.bitwise\_xor.md) | Computes the bitwise XOR of two tensors element-wise. |
 | [`tensor.bitwise_or`](tensor.bitwise\_or.md) | Computes the bitwise OR of two tensors element-wise. |
+| [`tensor.resize`](tensor.resize.md) | Resizes the input tensor. |
 | [`tensor.round`](tensor.round.md) | Computes the round value of all elements in the input tensor. |
 | [`tensor.reduce_l1`](tensor.reduce\_l1.md) | Computes the L1 norm of the input tensor's elements along the provided axes. |
 | [`tensor.trilu`](tensor.trilu.md) | Returns the upper or lower triangular part of a tensor or a batch of 2D matrices. |
@@ -106,25 +107,19 @@ use orion::operators::tensor::TensorTrait;
 | [`tensor.reduce_l2`](tensor.reduce\_l2.md) | Computes the L2 norm of the input tensor's elements along the provided axes. |
 | [`tensor.gather_elements`](tensor.gather\_elements.md) | GatherElements is an indexing operation that produces its output by indexing into the input data tensor at index positions determined by elements of the indices tensor. |
 | [`tensor.reduce_min`](tensor.reduce\_min.md) | Computes the min of the input tensor's elements along the provided axes. |
-| [`tensor.sequence_empty`](tensor.sequence\_empty.md) | Returns an empty tensor sequence. |
-| [`tensor.sequence_length`](tensor.sequence\_length.md) | Returns the length of the input sequence. |
-| [`tensor.sequence_insert`](tensor.sequence\_insert.md) | Insert a tensor into a sequence. |
-| [`tensor.sequence_at`](tensor.sequence\_at.md) | Outputs the tensor at the specified position in the input sequence. |
-| [`tensor.sequence_construct`](tensor.sequence\_construct.md) | Constructs a tensor sequence containing the input tensors. |
-| [`tensor.shrink`](tensor.shrink.md) | Shrinks the input tensor element-wise to the output tensor. |
 | [`tensor.reduce_mean`](tensor.reduce\_mean.md) | Computes the mean of the input tensor's elements along the provided axes. |
 | [`tensor.pow`](tensor.pow.md) | Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function f(x) = x^exponent, is applied to the data tensor elementwise. |
-| [`tensor.sequence_empty`](tensor.sequence\_empty.md) | Returns an empty tensor sequence. |
 | [`tensor.binarizer`](tensor.binarizer.md) | Maps the values of a tensor element-wise to 0 or 1 based on the comparison against a threshold value. |
 | [`tensor.array_feature_extractor`](tensor.array\_feature\_extractor.md) | Selects elements of the input tensor based on the indices passed applied to the last tensor axis. |
 | [`tensor.reduce_min`](tensor.reduce\_min.md) | Computes the min of the input tensor's elements along the provided axes. |
-| [`tensor.concat_from_sequence`](tensor.concat\_from\_sequence.md) | Concatenate a sequence of tensors into a single tensor. |
 | [`tensor.is_nan`](tensor.is\_nan.md) | Returns which elements of the input are NaN. |
 | [`tensor.is_inf`](tensor.is\_inf.md) | Maps infinity to true and other values to false. |
 | [`tensor.not`](tensor.not.md) | Computes the logical negation of all elements in the input tensor. |
 | [`tensor.gather_nd`](tensor.gather\_nd.md) | Given data tensor of rank r >= 1, indices tensor of rank q >= 1, and batch_dims integer b, this operator gathers slices of data into an output tensor of rank q + r - indices_shape[-1] - 1 - b. |
 | [`tensor.reduce_log_sum`](tensor.reduce\_log\_sum.md) | Computes the log sum of the input tensor's elements along the provided axes.  |
 | [`tensor.erf`](tensor.erf.md) | Computes the error function of the given input tensor element-wise. |
+| [`tensor.layer_normalization`](tensor.layer\_normalization.md) | computes the layer normalization of the input tensor. |
+| [`tensor.split`](tensor.split.md) | Split a tensor into a list of tensors, along the specified ‘axis’.  |
 
 ## Arithmetic Operations
 
diff --git a/docs/framework/operators/tensor/tensor.compress.md b/docs/framework/operators/tensor/tensor.compress.md
new file mode 100644
index 000000000..59cb043b3
--- /dev/null
+++ b/docs/framework/operators/tensor/tensor.compress.md
@@ -0,0 +1,39 @@
+# tensor.compress
+
+```rust 
+   fn compress(self: @Tensor<T>, condition: Tensor<T>, axis: Option<usize>) -> Tensor<T>;
+```
+
+Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index. In case axis is not provided, input is flattened before elements are selected.
+
+## Args
+
+* `self`(`@Tensor<T>`) - The input tensor.
+* `condition`(`Tensor<T>`) - Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.
+* `axis`(`Option<usize>`) - (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
+
+## Panics
+
+* Panics if condition rank is not equal to 1.
+
+## Returns 
+
+A new `Tensor<T>` .
+fn compress_example() -> Tensor<u32> {
+    let tensor = TensorTrait::<u32>::new(
+        shape: array![3, 2].span(), 
+        data: array![[1, 2], [3, 4], [5, 6]].span(), 
+    );
+    let condition = TensorTrait::<u32>::new(
+        shape: array![3].span(), 
+        data: array![0, 1, 1].span(), 
+    );
+
+    return tensor.compress(
+        condition: condition, 
+        axis: Option::Some((0)), 
+    );
+}
+>>> [[3, 4],
+     [5, 6]]
+```
diff --git a/docs/framework/operators/tensor/tensor.erf.md b/docs/framework/operators/tensor/tensor.erf.md
index 19ce86a94..384a941d0 100644
--- a/docs/framework/operators/tensor/tensor.erf.md
+++ b/docs/framework/operators/tensor/tensor.erf.md
@@ -6,10 +6,6 @@
 
 Computes the mean of the input tensor's elements along the provided axes.
 
-## Args
-
-* `self`(`@Tensor<T>`) - The input tensor.
-
 ## Returns
 
 A new `Tensor<T>` of the same shape as the input tensor with 
diff --git a/docs/framework/operators/tensor/tensor.gather_nd.md b/docs/framework/operators/tensor/tensor.gather_nd.md
index 021d4f235..a922b41ad 100644
--- a/docs/framework/operators/tensor/tensor.gather_nd.md
+++ b/docs/framework/operators/tensor/tensor.gather_nd.md
@@ -21,14 +21,6 @@ Given data tensor of rank r >= 1, indices tensor of rank q >= 1, and batch_dims
 ## Returns 
 
 A new `Tensor<T>` .
-
-## Example
-
-```rust
-use array::{ArrayTrait, SpanTrait};
-
-use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};
-
 fn gather_nd_example() -> Tensor<u32> {
     let tensor = TensorTrait::<u32>::new(
         shape: array![2, 2].span(), 
@@ -41,7 +33,7 @@ fn gather_nd_example() -> Tensor<u32> {
 
     return tensor.gather_nd(
         indices: indices, 
-        axis: Option::None((0)), 
+        axis: Option::Some((0)), 
     );
 }
 >>> [[0, 1],
diff --git a/docs/framework/operators/tensor/tensor.layer_normalization.md b/docs/framework/operators/tensor/tensor.layer_normalization.md
new file mode 100644
index 000000000..e52476b1c
--- /dev/null
+++ b/docs/framework/operators/tensor/tensor.layer_normalization.md
@@ -0,0 +1,89 @@
+# tensor.layer_normalization
+
+```rust 
+   fn layer_normalization(
+    self: @Tensor<T>,
+    scale: @Tensor<T>,
+    B: Option<@Tensor<T>>,
+    axis: Option<i32>,
+    epsilon: Option<T>,
+    stash_type: Option<usize>,
+) -> (Tensor<T>, Tensor<T>, Tensor<T>);
+```
+
+Layer normalization of the input, in two stages.
+The first stage is standardization, which makes the normalized elements have zero mean and unit variances.
+The second stage then scales and shifts the outcome of the first stage 
+## Args
+
+* `self`(`@Tensor<T>`) - The input tensor.
+* `scale`(`@Tensor<T>,`) - Scale tensor.
+* `B`(`Option<@Tensor<T>>`) - Bias tensor. 
+* `axis`(`Option<i32>`) (default is -1) - The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r). Negative value means counting dimensions from the back.
+* `epsilon`(`Option<T>`) (default is 0) - The epsilon value to use to avoid division by zero.
+* `stash_type`(`Option<usize>`) - Precise the computation precision - unused the precision is defined by the type of the tensor.
+## Panics
+
+* Panics if condition rank is not equal to 1.
+
+## Returns 
+
+A new normalized tensor`Tensor<T>`.
+A tensor containing the mean `Tensor<T>`.
+A tensor containing the inverse standard deviation `Tensor<T>`.
+
+## Example
+
+```rust
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn layer_normalization_example() -> (Tensor<FP16x16>, Tensor<FP16x16>, Tensor<FP16x16>) {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 41143, sign: true });
+    data.append(FP16x16 { mag: 51803, sign: false });
+    data.append(FP16x16 { mag: 113556, sign: false });
+    data.append(FP16x16 { mag: 64774, sign: false });
+    data.append(FP16x16 { mag: 866, sign: false });
+    data.append(FP16x16 { mag: 698, sign: true });
+    data.append(FP16x16 { mag: 106500, sign: false });
+    data.append(FP16x16 { mag: 98929, sign: false });
+    data.append(FP16x16 { mag: 7551, sign: false });
+    data.append(FP16x16 { mag: 30689, sign: true });
+    data.append(FP16x16 { mag: 38325, sign: false });
+    data.append(FP16x16 { mag: 48164, sign: false });
+    let X = TensorTrait::new(shape.span(), data.span());
+
+    let shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 49855, sign: false });
+    data.append(FP16x16 { mag: 150787, sign: false });
+    data.append(FP16x16 { mag: 83498, sign: true });
+    data.append(FP16x16 { mag: 30346, sign: false });
+    let scale = TensorTrait::new(shape.span(), data.span());
+
+     
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 54864, sign: true });
+    data.append(FP16x16 { mag: 50952, sign: false });
+    data.append(FP16x16 { mag: 8870, sign: true });
+    data.append(FP16x16 { mag: 23216, sign: true });
+    let bias = TensorTrait::new(shape.span(), data.span());
+
+    return X.layer_normalization(@scale,Option::Some(@bias),Option::None,Option::None,Option::None);
+}
+>>> [[-0.48926553  1.0185822  -0.02138367 -0.39223218]
+     [-0.7945549   0.99696046  0.04332176 -0.412645  ]
+     [-0.5664707   0.7491956  -0.7896356  -0.5320859 ]]
+
+``` 
diff --git a/docs/framework/operators/tensor/tensor.resize.md b/docs/framework/operators/tensor/tensor.resize.md
new file mode 100644
index 000000000..35164970d
--- /dev/null
+++ b/docs/framework/operators/tensor/tensor.resize.md
@@ -0,0 +1,229 @@
+#tensor.resize
+
+```rust
+    fn resize(
+        self: @Tensor<T>,
+        roi: Option<Tensor<T>>,
+        scales: Option<Span<T>>,
+        sizes: Option<Span<usize>>,
+        antialias: Option<usize>,
+        axes: Option<Span<usize>>,
+        coordinate_transformation_mode: Option<orion::operators::tensor::math::resize::TRANSFORMATION_MODE>,
+        cubic_coeff_a: Option<T>,
+        exclude_outside: Option<bool>,
+        extrapolation_value: Option<T>,
+        keep_aspect_ratio_policy: Option<orion::operators::tensor::math::resize::KEEP_ASPECT_RATIO_POLICY>,
+        mode: Option<orion::operators::tensor::math::resize::MODE>,
+        nearest_mode: Option<orion::operators::tensor::math::resize::NEAREST_MODE>,
+    ) -> Tensor<T>;
+```
+
+Resizes the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood in the input tensor. 
+
+## Args
+
+* `self`(`@Tensor<T>`) - The input tensor.
+* `roi` (`Option<Tensor<T>>`) (optional) - 1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X or the length of axes, if provided. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"
+* `scales` (`Option<Tensor<T>>`) (optional) - The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X' or the length of 'axes', if provided. One and only one of 'scales' and 'sizes' MUST be specified.
+* `sizes` (`Option<Tensor<usize>>`) (optional) - Target size of the output tensor. Its interpretation depends on the 'keep_aspect_ratio_policy' value. The number of elements of 'sizes' should be the same as the rank of input 'X', or the length of 'axes', if provided. One and only one of 'scales' and 'sizes' MUST be specified.  
+* `antialias` (`Option<usize>`) (default is 0) - If set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale).
+* `axes`(`Option<Span<usize>>`) - If provided, it specifies a subset of axes that 'roi', 'scales' and 'sizes' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). 
+* `coordinate_transformation_mode` (`Option<TRANSFORMATION_MODE>`) (default is half_pixel) - This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. 
+* `cubic_coeff_a` (`Option<T>`) (default is -0.75) - The coefficient 'a' used in cubic interpolation.
+* `exclude_outside` (`Option<bool>`) (default is false) - If set to true, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. 
+* `extrapolation_value` (`Option<T>`) (default is 0.0) - When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. 
+* `keep_aspect_ratio_policy` (`Option<KEEP_ASPECT_RATIO_POLICY>`) (default is stretch) - This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used. 
+* `mode` (`Option<MODE>`) (default is nearest) - Three interpolation modes: "nearest", "linear" and "cubic".
+* `nearest_mode` (`Option<NEAREST_MODE>`) (default is round_prefer_floor) - Four modes: "round_prefer_floor" (as known as round half down), "round_prefer_ceil" (as known as round half up), "floor", "ceil". Only used by nearest interpolation. 
+
+## Panics
+
+* Panics if both scales and sizes are `Option::None`.
+* Panics if roi is `Option::None` for the coordinate_transformation_mode `tf_crop_and_resize`.
+* Panics if antialias is not `Option::None` for mode `nearest`.
+
+## Returns
+
+A new resized `Tensor<T>` of the dimension given by output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) is scale is specified, or output_size if size is specified (note that some value of the parameter `keep_aspect_ratio_policy` can change sizes and therefore the dimension of the output tensor) 
+
+## Example
+
+```rust
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor, FP16x16Tensor, FP16x16TensorPartialEq};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+use orion::numbers::{FP16x16, FP16x16Impl, FixedTrait};
+use core::debug::PrintTrait;
+
+fn example_resize_downsample_scales_linear() -> Tensor<FP16x16>{
+    let mut data = TensorTrait::<
+        FP16x16
+    >::new(
+        shape: array![1, 1, 2, 4].span(),
+        data: array![
+            FixedTrait::<FP16x16>::new(65536, false),   //1
+            FixedTrait::<FP16x16>::new(131072, false),  //2
+            FixedTrait::<FP16x16>::new(196608, false),  //3
+            FixedTrait::<FP16x16>::new(262144, false),  //4
+            FixedTrait::<FP16x16>::new(327680, false),  //5
+            FixedTrait::<FP16x16>::new(393216, false),  //6
+            FixedTrait::<FP16x16>::new(458752, false),  //7
+            FixedTrait::<FP16x16>::new(524288, false),  //8
+        ]
+            .span(),
+    );
+    let mut scales = array![
+        FixedTrait::<FP16x16>::new(65536, false),  //1
+        FixedTrait::<FP16x16>::new(65536, false),   
+        FixedTrait::<FP16x16>::new(39322, false),  //0.6
+        FixedTrait::<FP16x16>::new(39322, false)
+    ]
+        .span();
+
+    let scales = Option::Some(scales);
+
+    return data.resize(
+        Option::None,
+        scales,
+        Option::None,
+        Option::None,
+        Option::None,
+        Option::None,
+        Option::None,
+        Option::None,
+        Option::None,
+        Option::None,
+        Option::Some(MODE::LINEAR),
+        Option::None,
+    );
+
+}
+>>> [[[[2.6666665 4.3333331]]]]
+
+
+
+fn example_resize_tf_crop_and_resize_extrapolation_value() -> Tensor<FP16x16> {
+    let mut data = TensorTrait::<
+        FP16x16
+    >::new(
+        shape: array![1, 1, 4, 4].span(),
+        data: array![
+            FixedTrait::<FP16x16>::new(65536, false),
+            FixedTrait::<FP16x16>::new(131072, false),
+            FixedTrait::<FP16x16>::new(196608, false),
+            FixedTrait::<FP16x16>::new(262144, false),
+            FixedTrait::<FP16x16>::new(327680, false),
+            FixedTrait::<FP16x16>::new(393216, false),
+            FixedTrait::<FP16x16>::new(458752, false),
+            FixedTrait::<FP16x16>::new(524288, false),
+            FixedTrait::<FP16x16>::new(589824, false),
+            FixedTrait::<FP16x16>::new(655360, false),
+            FixedTrait::<FP16x16>::new(720896, false),
+            FixedTrait::<FP16x16>::new(786432, false),
+            FixedTrait::<FP16x16>::new(851968, false),
+            FixedTrait::<FP16x16>::new(917504, false),
+            FixedTrait::<FP16x16>::new(983040, false),
+            FixedTrait::<FP16x16>::new(1048576, false),
+        ]
+            .span(),
+    );
+
+    let mut roi = TensorTrait::<
+        FP16x16
+    >::new(
+        shape: array![8].span(),
+        data: array![
+            FixedTrait::<FP16x16>::new(0, false),
+            FixedTrait::<FP16x16>::new(0, false),
+            FixedTrait::<FP16x16>::new(26214, false),
+            FixedTrait::<FP16x16>::new(39322, false),
+            FixedTrait::<FP16x16>::new(65536, false),
+            FixedTrait::<FP16x16>::new(65536, false),
+            FixedTrait::<FP16x16>::new(78643, false),
+            FixedTrait::<FP16x16>::new(111411, false),
+        ]
+            .span(),
+    );
+    let roi = Option::Some(roi);
+
+    let mut sizes = array![1, 1, 3, 3].span();
+    let sizes = Option::Some(sizes);
+
+    let extrapolation_value = Option::Some(FixedTrait::<FP16x16>::new(655360, false));
+
+    return data.resize(
+        roi,
+        Option::None,
+        sizes,
+        Option::None,
+        Option::None,
+        Option::Some(TRANSFORMATION_MODE::TF_CROP_AND_RESIZE),
+        Option::None,
+        Option::None,
+        extrapolation_value,
+        Option::None,
+        Option::Some(MODE::LINEAR),
+        Option::None,
+    );
+
+}
+>>> [[[[ 7.6000004 10.        10.       ]
+    [12.400001  10.        10.       ]
+    [10.        10.        10.       ]]]]
+
+
+
+fn example_resize_downsample_sizes_cubic_antialias() -> Tensor<FP16x16> {
+    let mut data = TensorTrait::<
+        FP16x16
+    >::new(
+        shape: array![1, 1, 4, 4].span(),
+        data: array![
+            FixedTrait::<FP16x16>::new(65536, false),
+            FixedTrait::<FP16x16>::new(131072, false),
+            FixedTrait::<FP16x16>::new(196608, false),
+            FixedTrait::<FP16x16>::new(262144, false),
+            FixedTrait::<FP16x16>::new(327680, false),
+            FixedTrait::<FP16x16>::new(393216, false),
+            FixedTrait::<FP16x16>::new(458752, false),
+            FixedTrait::<FP16x16>::new(524288, false),
+            FixedTrait::<FP16x16>::new(589824, false),
+            FixedTrait::<FP16x16>::new(655360, false),
+            FixedTrait::<FP16x16>::new(720896, false),
+            FixedTrait::<FP16x16>::new(786432, false),
+            FixedTrait::<FP16x16>::new(851968, false),
+            FixedTrait::<FP16x16>::new(917504, false),
+            FixedTrait::<FP16x16>::new(983040, false),
+            FixedTrait::<FP16x16>::new(1048576, false),
+        ]
+            .span(),
+    );
+
+    let antialias = Option::Some(1);
+
+    let mut sizes = array![1, 1, 3, 3].span();
+    let sizes = Option::Some(sizes);
+
+    return data.resize(
+        Option::None,
+        Option::None,
+        sizes,
+        antialias,
+        Option::None,
+        Option::None,
+        Option::None,
+        Option::None,
+        Option::None,
+        Option::None,
+        Option::Some(MODE::CUBIC),
+        Option::None,
+    );
+}
+
+>>> [[[[ 1.7750092  3.1200073  4.4650054]
+    [ 7.1550016  8.5        9.844998 ]
+    [12.534994  13.8799925 15.224991 ]]]]
+
+```
diff --git a/docs/framework/operators/tensor/tensor.split.md b/docs/framework/operators/tensor/tensor.split.md
new file mode 100644
index 000000000..26b4a546f
--- /dev/null
+++ b/docs/framework/operators/tensor/tensor.split.md
@@ -0,0 +1,47 @@
+# tensor.split
+
+```rust 
+   fn split(self: @Tensor<T>, axis: usize, num_outputs: Option<usize>, split: Option<Tensor<usize>>
+   ) -> Array<Tensor<T>>;
+```
+
+Split a tensor into a list of tensors, along the specified ‘axis’
+
+
+* `self`(`@Tensor<T>`) - The input tensor.
+* `axis`(`usize`) - The axis along which to split on.
+* `num_outputs `(Option<usize>) - Number of outputs to split parts of the tensor into. 
+* `split  `(Option<Tensor<usize>>) - Optional length of each output.
+
+## Panics
+
+* Panics if the 'axis' accepted range is not [-rank, rank-1] where r = rank(input).
+* Panics if the 'split' values not >= 0. Sum of the values is not equal to the dim value at ‘axis’ specified.
+* Panics if the input 'split' or the attribute 'num_outputs' both are specified or not.
+
+## Returns
+
+One or more outputs forming list of tensors after splitting.
+
+## Examples
+
+```rust
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};
+use core::option::OptionTrait;
+fn split_tensor_example() -> Array<Tensor<u32>> {
+    let tensor: Tensor<u32> = TensorTrait::<u32>::new(
+        shape: array![2,4].span(), 
+        data: array![
+            0, 1, 2, 3, 4, 5, 6, 7
+            ].span(),
+    );
+    let num_outputs = Option::Some(2);
+    // split = Option::Some(array![1, 1].span());
+    let split_num: Option<Tensor<usize>> = Option::None(());
+    // We can call `split` function as follows.
+    return tensor.split(0, num_outputs, split_num);
+}
+>>> [[0,1],[4,5]]
+    [[2,3],[6,7]]
+```
diff --git a/nodegen/helpers.py b/nodegen/helpers.py
index cf876ccc0..03fae966c 100644
--- a/nodegen/helpers.py
+++ b/nodegen/helpers.py
@@ -10,6 +10,8 @@
 class FixedImpl(Enum):
     FP8x23 = 'FP8x23'
     FP16x16 = 'FP16x16'
+    FP64x64 = 'FP64x64'
+    
 
 
 def to_fp(x: np.ndarray, fp_impl: FixedImpl):
@@ -18,15 +20,19 @@ def to_fp(x: np.ndarray, fp_impl: FixedImpl):
             return (x * 2**23).astype(np.int64)
         case FixedImpl.FP16x16:
             return (x * 2**16).astype(np.int64)
+        case FixedImpl.FP64x64:
+            return (x * 2**64)
 
 
 class Dtype(Enum):
     FP8x23 = 'FP8x23'
     FP16x16 = 'FP16x16'
+    FP64x64 = 'FP64x64'
     I8 = 'i8'
     I32 = 'i32'
     U32 = 'u32'
     BOOL = 'bool'
+    COMPLEX64 = 'complex64'
 
 
 class Tensor:
@@ -42,6 +48,7 @@ def __init__(self, dtype: Dtype, shape: tuple, data: np.ndarray):
 class Trait(Enum):
     TENSOR = 'TENSOR'
     NN = 'NN'
+    SEQUENCE = 'SEQUENCE'
 
 
 def make_test(inputs: list[Tensor | Sequence], output: Tensor | Sequence, func_sig: str, name: str, trait: Trait = Trait.TENSOR):
@@ -166,8 +173,15 @@ def get_data_statement(data: np.ndarray, dtype: Dtype) -> list[str]:
             return ["FP8x23 { "+f"mag: {abs(int(x))}, sign: {str(x < 0).lower()} "+"}" for x in data.flatten()]
         case Dtype.FP16x16:
             return ["FP16x16 { "+f"mag: {abs(int(x))}, sign: {str(x < 0).lower()} "+"}" for x in data.flatten()]
+        case Dtype.FP64x64:
+            return ["FP64x64 { "+f"mag: {abs(int(x))}, sign: {str(x < 0).lower()} "+"}" for x in data.flatten()]
         case Dtype.BOOL:
             return [str(x).lower() for x in data.flatten()]
+        case Dtype.COMPLEX64:
+            return ["complex64 { "+"real: FP64x64 { "+f"mag: {abs(int(np.real(x)))}, sign: {str(np.real(x) < 0).lower()} "+"} , img: FP64x64 { "+f"mag: {abs(int(np.imag(x)))}, sign: {str(np.imag(x) < 0).lower()} "+"} }" for x in data.flatten()]
+        
+
+
 
 
 def get_data_statement_for_sequences(data: Sequence, dtype: Dtype) -> list[list[str]]:
@@ -186,7 +200,13 @@ def get_test_refs(dtype: Dtype, trait: Trait) -> list[str]:
     if trait == Trait.NN and dtype == Dtype.BOOL:
         raise Exception("NN trait does not support bool dtype")
 
-    dtype_ref = dtype_to_nn[dtype] if trait == Trait.NN else dtype_to_tensor[dtype]
+    if trait == Trait.NN:
+        dtype_ref = dtype_to_nn[dtype]
+    elif trait == Trait.SEQUENCE:
+        dtype_ref = dtype_to_sequence[dtype]
+    else:
+        dtype_ref = dtype_to_tensor[dtype]
+
     refs = [
         *trait_to_ref[trait],
         *dtype_ref,
@@ -217,6 +237,10 @@ def find_all_types(tensors: list[Tensor | Sequence]) -> list[Dtype]:
         "orion::numbers::FixedTrait",
         "orion::operators::nn::NNTrait",
     ],
+    Trait.SEQUENCE: [
+        "array::{ArrayTrait, SpanTrait}",
+        "orion::operators::sequence::SequenceTrait",
+    ],
 }
 
 
@@ -227,6 +251,7 @@ def find_all_types(tensors: list[Tensor | Sequence]) -> list[Dtype]:
     Dtype.FP8x23: ["orion::operators::tensor::FP8x23Tensor",],
     Dtype.FP16x16: ["orion::operators::tensor::FP16x16Tensor",],
     Dtype.BOOL: ["orion::operators::tensor::BoolTensor",],
+    Dtype.COMPLEX64: ["orion::operators::tensor::Complex64Tensor",],
 }
 
 
@@ -239,6 +264,15 @@ def find_all_types(tensors: list[Tensor | Sequence]) -> list[Dtype]:
 }
 
 
+dtype_to_sequence = {
+    Dtype.U32: ["orion::operators::sequence::U32Sequence",],
+    Dtype.I32: ["orion::operators::sequence::I32Sequence",],
+    Dtype.I8: ["orion::operators::sequence::I8Sequence",],
+    Dtype.FP8x23: ["orion::operators::sequence::FP8x23Sequence",],
+    Dtype.FP16x16: ["orion::operators::sequence::FP16x16Sequence",],
+}
+
+
 dtype_to_partial_eq = {
     Dtype.U32: ["orion::operators::tensor::U32TensorPartialEq",],
     Dtype.I32: ["orion::operators::tensor::I32TensorPartialEq",],
@@ -246,6 +280,7 @@ def find_all_types(tensors: list[Tensor | Sequence]) -> list[Dtype]:
     Dtype.FP8x23: ["orion::operators::tensor::FP8x23TensorPartialEq",],
     Dtype.FP16x16: ["orion::operators::tensor::FP16x16TensorPartialEq",],
     Dtype.BOOL: ["orion::operators::tensor::BoolTensorPartialEq",],
+    Dtype.COMPLEX64: ["orion::operators::tensor::Complex64TensorPartialEq",],
 }
 
 
@@ -256,4 +291,5 @@ def find_all_types(tensors: list[Tensor | Sequence]) -> list[Dtype]:
     Dtype.FP8x23: ["orion::numbers::{FixedTrait, FP8x23}",],
     Dtype.FP16x16: ["orion::numbers::{FixedTrait, FP16x16}",],
     Dtype.BOOL: [],
+    Dtype.COMPLEX64: ["orion::numbers::{NumberTrait, complex64}",],
 }
\ No newline at end of file
diff --git a/nodegen/node/compress.py b/nodegen/node/compress.py
new file mode 100644
index 000000000..cca518f54
--- /dev/null
+++ b/nodegen/node/compress.py
@@ -0,0 +1,326 @@
+import numpy as np
+from nodegen.node import RunAll
+from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
+
+class Compress(RunAll):
+
+    @staticmethod
+    def compress_fp16x16():
+            
+        def compress_3D():
+            def default():
+                x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
+                x2 = np.array([0, 1, 1]).astype(np.uint32)
+                y = x1.compress(x2, axis=0)
+
+                x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten()) 
+                y = Tensor(Dtype.FP16x16, y.shape, to_fp(
+                y.flatten(), FixedImpl.FP16x16)) 
+
+                name = "compress_fp16x16_3d_default"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(0))", 
+                    name= name)
+                
+            def axis1():
+                x1 = np.arange(0,180).reshape(3,4,3,5).astype(np.int64)
+                x2 = np.array([1, 1, 1, 0]).astype(np.int64)
+                y = x1.compress(x2, axis=1)
+
+                x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten()) 
+                y = Tensor(Dtype.FP16x16, y.shape, to_fp(
+                y.flatten(), FixedImpl.FP16x16)) 
+
+                name = "compress_fp16x16_3d_axis1"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(1))", 
+                    name= name)
+                
+            def axis2():
+                x1 = np.arange(0,48).reshape(4,3,4).astype(np.int64)
+                x2 = np.array([1, 0, 1, 1]).astype(np.int64)
+                y = x1.compress(x2, axis=2)
+
+                x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten()) 
+                y = Tensor(Dtype.FP16x16, y.shape, to_fp(
+                y.flatten(), FixedImpl.FP16x16)) 
+
+                name = "compress_fp16x16_3d_axis2"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(2))", 
+                    name= name)
+                
+            def axis3():
+                x1 = np.arange(0,96).reshape(4,3,4, 2).astype(np.int64)
+                x2 = np.array([1, 0]).astype(np.int64)
+                y = x1.compress(x2, axis=3)
+
+                x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten()) 
+                y = Tensor(Dtype.FP16x16, y.shape, to_fp(
+                y.flatten(), FixedImpl.FP16x16)) 
+
+                name = "compress_fp16x16_3d_axis3"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(3))", 
+                    name= name)
+                
+            def noaxis():
+                x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
+                x2 = np.array([1, 0, 1, 0, 1, 1, 1, 1, 1]).astype(np.int64)
+                y = x1.compress(x2)
+
+                x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten()) 
+                y = Tensor(Dtype.FP16x16, y.shape, to_fp(
+                y.flatten(), FixedImpl.FP16x16)) 
+
+                name = "compress_fp16x16_3d_noaxis"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::None(()))", 
+                    name= name)
+                
+            default()
+            axis1()
+            axis2()
+            axis3()
+            noaxis()
+        compress_3D()
+
+    @staticmethod
+    def compress_fp8x23():
+            
+        def compress_3D():
+            def default():
+                x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
+                x2 = np.array([0, 1, 1]).astype(np.uint32)
+                y = x1.compress(x2, axis=0)
+
+                x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten()) 
+                y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
+
+                name = "compress_fp8x23_3d_default"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(0))", 
+                    name= name)
+                
+            def axis1():
+                x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
+                x2 = np.array([0, 1, 1]).astype(np.uint32)
+                y = x1.compress(x2, axis=1)
+
+                x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())  
+                y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23)) 
+
+                name = "compress_fp8x23_3d_axis1"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(1))", 
+                    name= name)
+                
+            def axis2():
+                x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
+                x2 = np.array([0, 1, 1]).astype(np.uint32)
+                y = x1.compress(x2, axis=2)
+
+                x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten()) 
+                y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
+
+                name = "compress_fp8x23_3d_axis2"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(2))", 
+                    name= name)
+                
+            default()
+            axis1()
+            axis2()
+        compress_3D()
+
+    @staticmethod
+    def compress_i8():
+            
+        def compress_3D():
+            def default():
+                x1 = np.arange(0,27).reshape(3,3,3).astype(np.int8)
+                x2 = np.array([0, 1, 1]).astype(np.uint8)
+                y = x1.compress(x2, axis=0)
+
+                x1 =  Tensor(Dtype.I8, x1.shape, x1.flatten())
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten()) 
+                y =  Tensor(Dtype.I8, y.shape, y.flatten()) 
+
+                name = "compress_i8_3d_default"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(0))", 
+                    name= name)
+                
+            def axis1():
+                x1 = np.arange(0,27).reshape(3,3,3).astype(np.int8)
+                x2 = np.array([0, 1, 1]).astype(np.uint8)
+                y = x1.compress(x2, axis=1)
+
+                x1 =  Tensor(Dtype.I8, x1.shape, x1.flatten())
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten()) 
+                y =  Tensor(Dtype.I8, y.shape, y.flatten())
+
+                name = "compress_i8_3d_axis1"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(1))", 
+                    name= name)
+                
+            def axis2():
+                x1 = np.arange(0,27).reshape(3,3,3).astype(np.int8)
+                x2 = np.array([0, 1, 1]).astype(np.uint8)
+                y = x1.compress(x2, axis=2)
+
+                x1 =  Tensor(Dtype.I8, x1.shape, x1.flatten())
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten()) 
+                y =  Tensor(Dtype.I8, y.shape, y.flatten())
+
+                name = "compress_i8_3d_axis2"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(2))", 
+                    name= name)
+                
+            default()
+            axis1()
+            axis2()
+        compress_3D()
+
+    
+    @staticmethod
+    def compress_i32():
+            
+        def compress_3D():
+            def default():
+                x1 = np.arange(0,27).reshape(3,3,3).astype(np.int32)
+                x2 = np.array([0, 1, 1]).astype(np.int32)
+                y = x1.compress(x2, axis=0)
+
+                x1 =  Tensor(Dtype.I32, x1.shape, x1.flatten())
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten()) 
+                y =  Tensor(Dtype.I32, y.shape, y.flatten()) 
+
+                name = "compress_i32_3d_default"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(0))", 
+                    name= name)
+                
+            def axis1():
+                x1 = np.arange(0,27).reshape(3,3,3).astype(np.int32)
+                x2 = np.array([0, 1, 1]).astype(np.int32)
+                y = x1.compress(x2, axis=1)
+
+                x1 =  Tensor(Dtype.I32, x1.shape, x1.flatten())
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten()) 
+                y =  Tensor(Dtype.I32, y.shape, y.flatten())
+
+                name = "compress_i32_3d_axis1"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(1))", 
+                    name= name)
+                
+            def axis2():
+                x1 = np.arange(0,27).reshape(3,3,3).astype(np.int32)
+                x2 = np.array([0, 1, 1]).astype(np.int32)
+                y = x1.compress(x2, axis=2)
+
+                x1 =  Tensor(Dtype.I32, x1.shape, x1.flatten())
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten()) 
+                y =  Tensor(Dtype.I32, y.shape, y.flatten())
+
+                name = "compress_i32_3d_axis2"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(2))", 
+                    name= name)
+                
+            default()
+            axis1()
+            axis2()
+        compress_3D()
+
+    @staticmethod
+    def compress_u32():
+            
+        def compress_3D():
+            def default():
+                x1 = np.arange(0,48).reshape(4,4,3).astype(np.uint32)
+                x2 = np.array([1, 1]).astype(np.uint32)
+                y = x1.compress(x2, axis=0)
+
+                x1 =  Tensor(Dtype.U32, x1.shape, x1.flatten())
+                x2 =  Tensor(Dtype.U32, x2.shape, x2.flatten())
+                y =  Tensor(Dtype.U32, y.shape, y.flatten()) 
+
+                name = "compress_u32_3d_default"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(0))", 
+                    name= name)
+                
+            def axis1():
+                x1 = np.arange(0,36).reshape(3,4,3).astype(np.uint32)
+                x2 = np.array([0, 1, 1]).astype(np.uint32)
+                y = x1.compress(x2, axis=1)
+
+                x1 =  Tensor(Dtype.U32, x1.shape, x1.flatten())
+                x2 = Tensor(Dtype.U32, x2.shape, x2.flatten()) 
+                y =  Tensor(Dtype.U32, y.shape, y.flatten())
+
+                name = "compress_u32_3d_axis1"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(1))", 
+                    name= name)
+                
+            def axis2():
+                x1 = np.arange(0,48).reshape(3,4,4).astype(np.uint32)
+                x2 = np.array([0, 1, 1]).astype(np.uint32)
+                y = x1.compress(x2, axis=2)
+
+                x1 =  Tensor(Dtype.U32, x1.shape, x1.flatten())
+                x2 =  Tensor(Dtype.U32, x2.shape, x2.flatten())
+                y =  Tensor(Dtype.U32, y.shape, y.flatten())
+
+                name = "compress_u32_3d_axis2"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(2))", 
+                    name= name)
+                
+            def axis2_2():
+                x1 = np.arange(0,60).reshape(3,4,5).astype(np.uint32)
+                x2 = np.array([0, 1, 1]).astype(np.uint32)
+                y = x1.compress(x2, axis=2)
+
+                x1 =  Tensor(Dtype.U32, x1.shape, x1.flatten())
+                x2 =  Tensor(Dtype.U32, x2.shape, x2.flatten())
+                y =  Tensor(Dtype.U32, y.shape, y.flatten())
+
+                name = "compress_u32_3d_axis2_2"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(2))", 
+                    name= name)
+                
+            def axis3():
+                x1 = np.arange(0,270).reshape(3,3,5,6).astype(np.uint32)
+                x2 = np.array([0, 1, 1,1,0,1]).astype(np.uint32)
+                y = x1.compress(x2, axis=3)
+
+                x1 =  Tensor(Dtype.U32, x1.shape, x1.flatten())
+                x2 =  Tensor(Dtype.U32, x2.shape, x2.flatten())
+                y =  Tensor(Dtype.U32, y.shape, y.flatten())
+
+                name = "compress_u32_3d_axis3"
+                make_test(
+                    inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(3))", 
+                    name= name)
+                
+            default()
+            axis1()
+            axis2()
+            axis2_2()
+            axis3()
+        compress_3D()
diff --git a/nodegen/node/concat_from_sequence.py b/nodegen/node/concat_from_sequence.py
index 4918785ea..eb6d6c9e1 100644
--- a/nodegen/node/concat_from_sequence.py
+++ b/nodegen/node/concat_from_sequence.py
@@ -1,6 +1,6 @@
 import numpy as np
 from nodegen.node import RunAll
-from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
+from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
 
 
 class Concat_from_sequence(RunAll):
@@ -25,7 +25,7 @@ def new_axis_zero():
             concatenated_tensor = Tensor(Dtype.U32, concatenated_tensor.shape, concatenated_tensor.flatten())
 
             name = "concat_from_sequence_u32_new_axis_zero"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0))", name, Trait.SEQUENCE)
 
         def new_axis_one():
             sequence = []
@@ -45,7 +45,7 @@ def new_axis_one():
             concatenated_tensor = Tensor(Dtype.U32, concatenated_tensor.shape, concatenated_tensor.flatten())
 
             name = "concat_from_sequence_u32_new_axis_one"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1))", name, Trait.SEQUENCE)
 
         def new_axis_default():
             sequence = []
@@ -65,7 +65,7 @@ def new_axis_default():
             concatenated_tensor = Tensor(Dtype.U32, concatenated_tensor.shape, concatenated_tensor.flatten())
 
             name = "concat_from_sequence_u32_new_axis_default"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::None(()))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::None(()))", name, Trait.SEQUENCE)
 
         new_axis_zero()
         new_axis_one()
@@ -92,7 +92,7 @@ def new_axis_zero():
             concatenated_tensor = Tensor(Dtype.I32, concatenated_tensor.shape, concatenated_tensor.flatten())
 
             name = "concat_from_sequence_i32_new_axis_zero"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0))", name, Trait.SEQUENCE)
 
         def new_axis_one():
             sequence = []
@@ -112,7 +112,7 @@ def new_axis_one():
             concatenated_tensor = Tensor(Dtype.I32, concatenated_tensor.shape, concatenated_tensor.flatten())
 
             name = "concat_from_sequence_i32_new_axis_one"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1))", name, Trait.SEQUENCE)
 
         def new_axis_default():
             sequence = []
@@ -132,7 +132,7 @@ def new_axis_default():
             concatenated_tensor = Tensor(Dtype.I32, concatenated_tensor.shape, concatenated_tensor.flatten())
 
             name = "concat_from_sequence_i32_new_axis_default"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::None(()))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::None(()))", name, Trait.SEQUENCE)
 
         new_axis_zero()
         new_axis_one()
@@ -159,7 +159,7 @@ def new_axis_zero():
             concatenated_tensor = Tensor(Dtype.I8, concatenated_tensor.shape, concatenated_tensor.flatten())
 
             name = "concat_from_sequence_i8_new_axis_zero"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0))", name, Trait.SEQUENCE)
 
         def new_axis_one():
             sequence = []
@@ -179,7 +179,7 @@ def new_axis_one():
             concatenated_tensor = Tensor(Dtype.I8, concatenated_tensor.shape, concatenated_tensor.flatten())
 
             name = "concat_from_sequence_i8_new_axis_one"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1))", name, Trait.SEQUENCE)
 
         def new_axis_default():
             sequence = []
@@ -199,7 +199,7 @@ def new_axis_default():
             concatenated_tensor = Tensor(Dtype.I8, concatenated_tensor.shape, concatenated_tensor.flatten())
 
             name = "concat_from_sequence_i8_new_axis_default"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::None(()))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::None(()))", name, Trait.SEQUENCE)
 
         new_axis_zero()
         new_axis_one()
@@ -226,7 +226,7 @@ def new_axis_zero():
             concatenated_tensor = Tensor(Dtype.FP8x23, concatenated_tensor.shape, to_fp(concatenated_tensor.flatten(), FixedImpl.FP8x23))
 
             name = "concat_from_sequence_fp8x23_new_axis_zero"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0))", name, Trait.SEQUENCE)
 
         def new_axis_one():
             sequence = []
@@ -246,7 +246,7 @@ def new_axis_one():
             concatenated_tensor = Tensor(Dtype.FP8x23, concatenated_tensor.shape, to_fp(concatenated_tensor.flatten(), FixedImpl.FP8x23))
 
             name = "concat_from_sequence_fp8x23_new_axis_one"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1))", name, Trait.SEQUENCE)
 
         def new_axis_default():
             sequence = []
@@ -266,7 +266,7 @@ def new_axis_default():
             concatenated_tensor = Tensor(Dtype.FP8x23, concatenated_tensor.shape, to_fp(concatenated_tensor.flatten(), FixedImpl.FP8x23))
 
             name = "concat_from_sequence_fp8x23_new_axis_default"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::None(()))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::None(()))", name, Trait.SEQUENCE)
 
         new_axis_zero()
         new_axis_one()
@@ -293,7 +293,7 @@ def new_axis_zero():
             concatenated_tensor = Tensor(Dtype.FP16x16, concatenated_tensor.shape, to_fp(concatenated_tensor.flatten(), FixedImpl.FP16x16))
 
             name = "concat_from_sequence_fp16x16_new_axis_zero"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0))", name, Trait.SEQUENCE)
 
         def new_axis_one():
             sequence = []
@@ -313,7 +313,7 @@ def new_axis_one():
             concatenated_tensor = Tensor(Dtype.FP16x16, concatenated_tensor.shape, to_fp(concatenated_tensor.flatten(), FixedImpl.FP16x16))
 
             name = "concat_from_sequence_fp16x16_new_axis_one"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1))", name, Trait.SEQUENCE)
 
         def new_axis_default():
             sequence = []
@@ -333,7 +333,7 @@ def new_axis_default():
             concatenated_tensor = Tensor(Dtype.FP16x16, concatenated_tensor.shape, to_fp(concatenated_tensor.flatten(), FixedImpl.FP16x16))
 
             name = "concat_from_sequence_fp16x16_new_axis_default"
-            make_test([sequence], concatenated_tensor, "TensorTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::None(()))", name)
+            make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, IntegerTrait::<i32>::new(1, false), Option::None(()))", name, Trait.SEQUENCE)
 
         new_axis_zero()
         new_axis_one()
diff --git a/nodegen/node/layer_normalization.py b/nodegen/node/layer_normalization.py
new file mode 100644
index 000000000..54f6e63fb
--- /dev/null
+++ b/nodegen/node/layer_normalization.py
@@ -0,0 +1,152 @@
+import numpy as np
+from nodegen.node import RunAll
+from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
+import numpy as np
+
+import onnx
+from onnx.backend.test.case.base import Base
+from onnx.backend.test.case.node import expect
+
+
+def _layer_normalization(X, W, B, axis=-1, epsilon=1e-5): 
+    X_shape = X.shape
+    X_rank = len(X_shape)
+    if axis < 0:
+        axis = axis + X_rank
+    unsqueezed_rank = X_rank - axis
+    reduction_shape = X_shape[0:axis] + (1,) * unsqueezed_rank
+
+    row_number = 1
+    col_number = 1
+    for i in range(X_rank):
+        if i < axis:
+            row_number *= X_shape[i]
+        else:
+            col_number *= X_shape[i]
+    x_mat = np.reshape(X, (row_number, col_number))
+    x_mean = np.sum(x_mat, axis=1, keepdims=True) / col_number
+    x_diff = x_mat - x_mean
+    x_squared_diff = x_diff * x_diff
+    variance = np.sum(x_squared_diff, axis=1, keepdims=True) / col_number
+    variance_eps = variance + epsilon
+    std_dev = np.sqrt(variance_eps)
+    inv_std_dev = np.reciprocal(std_dev)
+    y_mat = x_diff * inv_std_dev
+    Y = np.reshape(y_mat, X_shape) * W + B
+    X_mean = np.reshape(x_mean, reduction_shape)
+    X_inv_std_dev = np.reshape(inv_std_dev, reduction_shape)
+
+    return Y, X_mean, X_inv_std_dev
+
+
+def calculate_normalized_shape(X_shape, axis):  
+    X_rank = len(X_shape)
+    if axis < 0:
+        axis = axis + X_rank
+    return X_shape[axis:]
+
+
+class Layer_normalization(RunAll):
+    @staticmethod
+    def export4d() -> None:
+        X = np.random.randn(2, 3, 4, 5).astype(np.float32)
+
+        def case(axis: int) -> None:
+            normalized_shape = calculate_normalized_shape(X.shape, axis)
+            W = np.random.randn(*normalized_shape).astype(np.float32)
+            B = np.random.randn(*normalized_shape).astype(np.float32)
+            Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis)
+
+            if axis < 0:
+                name = f"layer_normalization_4d_axis_negative_{-axis}"
+                func_sig = f"input_0.layer_normalization(@input_1,Option::Some(@input_2),Option::Some(IntegerTrait::<i32>::new({-axis}, true)),Option::None,Option::None)"
+            else:
+                name = f"layer_normalization_4d_axis{axis}"
+                func_sig = f"input_0.layer_normalization(@input_1,Option::Some(@input_2),Option::Some(IntegerTrait::<i32>::new({axis}, false)),Option::None,Option::None)"
+
+
+            x = Tensor(Dtype.FP8x23, X.shape, to_fp(X.flatten(), FixedImpl.FP8x23))
+            w = Tensor(Dtype.FP8x23, W.shape, to_fp(W.flatten(), FixedImpl.FP8x23))
+            b = Tensor(Dtype.FP8x23, B.shape, to_fp(B.flatten(), FixedImpl.FP8x23))
+            y = Tensor(Dtype.FP8x23, Y.shape, to_fp(Y.flatten(), FixedImpl.FP8x23))
+            
+            make_test([x,w,b], y, func_sig, name)
+
+
+        for i in range(len(X.shape)):
+            case(i)
+            case(i - len(X.shape))
+
+    @staticmethod
+    def export_default_axis() -> None:
+        X = np.random.randn(2, 3, 4, 5).astype(np.float32)
+
+        normalized_shape = calculate_normalized_shape(X.shape, -1)
+        W = np.random.randn(*normalized_shape).astype(np.float32)
+        B = np.random.randn(*normalized_shape).astype(np.float32)
+        Y, mean, inv_std_dev = _layer_normalization(X, W, B)
+
+        x = Tensor(Dtype.FP16x16, X.shape, to_fp(X.flatten(), FixedImpl.FP16x16))
+        w = Tensor(Dtype.FP16x16, W.shape, to_fp(W.flatten(), FixedImpl.FP16x16))
+        b = Tensor(Dtype.FP16x16, B.shape, to_fp(B.flatten(), FixedImpl.FP16x16))
+        y = Tensor(Dtype.FP16x16, Y.shape, to_fp(Y.flatten(), FixedImpl.FP16x16))
+        
+        name = "layer_normalization_default_axis"
+        make_test([x,w,b], y, "input_0.layer_normalization(@input_1,Option::Some(@input_2),Option::None,Option::None,Option::None)", name)
+
+    @staticmethod
+    def export3d_epsilon() -> None:
+        epsilon = 1e-1
+        X = np.random.randn(2, 3, 5).astype(np.float32)
+
+        def case(axis: int) -> None:
+            normalized_shape = calculate_normalized_shape(X.shape, axis)
+            W = np.random.randn(*normalized_shape).astype(np.float32)
+            B = np.random.randn(*normalized_shape).astype(np.float32)
+            Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis, epsilon)
+
+            if axis < 0:
+                name = f"layer_normalization_3d_axis_negative_{-axis}_epsilon"
+                func_sig = f"input_0.layer_normalization(@input_1,Option::Some(@input_2),Option::Some(IntegerTrait::<i32>::new({-axis}, true)),Option::Some(FixedTrait::new(6554, false)),Option::None)"
+            else:
+                name = f"layer_normalization_3d_axis{axis}_epsilon"
+                func_sig = f"input_0.layer_normalization(@input_1,Option::Some(@input_2),Option::Some(IntegerTrait::<i32>::new({axis}, false)),Option::Some(FixedTrait::new(6554, false)),Option::None)"
+
+            x = Tensor(Dtype.FP16x16, X.shape, to_fp(X.flatten(), FixedImpl.FP16x16))
+            w = Tensor(Dtype.FP16x16, W.shape, to_fp(W.flatten(), FixedImpl.FP16x16))
+            b = Tensor(Dtype.FP16x16, B.shape, to_fp(B.flatten(), FixedImpl.FP16x16))
+            y = Tensor(Dtype.FP16x16, Y.shape, to_fp(Y.flatten(), FixedImpl.FP16x16))
+            
+            make_test([x,w,b], y, func_sig, name)
+
+
+        for i in range(len(X.shape)):
+            case(i)
+            case(i - len(X.shape))
+
+    @staticmethod
+    def test_2d_example() -> None:
+        X = np.random.randn(3, 4).astype(np.float32)
+
+        def case(axis: int) -> None:
+            normalized_shape = calculate_normalized_shape(X.shape, axis)
+            W = np.random.randn(*normalized_shape).astype(np.float32)
+            B = np.random.randn(*normalized_shape).astype(np.float32)
+            Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis=axis)
+
+            node = onnx.helper.make_node(
+                "LayerNormalization",
+                inputs=["X", "W", "B"],
+                outputs=["Y", "Mean", "InvStdDev"],
+                axis=axis,
+            )
+
+            x = Tensor(Dtype.FP16x16, X.shape, to_fp(X.flatten(), FixedImpl.FP16x16))
+            w = Tensor(Dtype.FP16x16, W.shape, to_fp(W.flatten(), FixedImpl.FP16x16))
+            b = Tensor(Dtype.FP16x16, B.shape, to_fp(B.flatten(), FixedImpl.FP16x16))
+            y = Tensor(Dtype.FP16x16, Y.shape, to_fp(Y.flatten(), FixedImpl.FP16x16))
+
+            name = "layer_normalization_test"
+            make_test([x,w,b], y, "input_0.layer_normalization(@input_1,Option::Some(@input_2),Option::None,Option::None,Option::None)", name)
+
+        case(-1)
\ No newline at end of file
diff --git a/nodegen/node/reduce_l2.py b/nodegen/node/reduce_l2.py
index 081125f54..67c8cfa10 100644
--- a/nodegen/node/reduce_l2.py
+++ b/nodegen/node/reduce_l2.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 
+
 class Reduce_l2(RunAll):
     @staticmethod
     def reduce_l2_fp8x23():
@@ -107,4 +108,29 @@ def reduce_l2_axis_0():
         
         reduce_l2_export_do_not_keepdims()
         reduce_l2_export_keepdims()
+        reduce_l2_axis_0()
+
+    @staticmethod
+    def reduce_l2_complex64():
+        
+
+
+        def reduce_l2_axis_0():
+            shape = [2, 3]
+            axes = np.array([0], dtype=np.int64)
+            keepdims = True
+            x = np.reshape(np.array([1.+2.j,  2.-1.j,  3.-3.j,  3.-2.j,  3.+5.j, 4.- 1.j]), shape)
+            y = np.sqrt(np.sum(a=np.square(abs(x)), axis=tuple(axes), keepdims=True))
+            print(to_fp(x.flatten(), FixedImpl.FP64x64))
+
+            x = Tensor(Dtype.COMPLEX64, x.shape, to_fp(
+            x.flatten(), FixedImpl.FP64x64))
+            
+            y = Tensor(Dtype.COMPLEX64, y.shape, to_fp(
+            y.flatten(), FixedImpl.FP64x64))
+
+            name = "reduce_l2_complex64_axis_0"
+            make_test(
+                [x], y, "input_0.reduce_l2(0, true)", name)
+
         reduce_l2_axis_0()
\ No newline at end of file
diff --git a/nodegen/node/resize.py b/nodegen/node/resize.py
new file mode 100644
index 000000000..65cafa9ba
--- /dev/null
+++ b/nodegen/node/resize.py
@@ -0,0 +1,2204 @@
+# Python test implementation from ONNX library : https://github.com/onnx/onnx/blob/main/onnx/reference/ops/op_resize.py
+
+import numpy as np
+from typing import Any, Callable
+
+from nodegen.node import RunAll
+from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
+
+
+def _cartesian(arrays: list[np.ndarray], out: np.ndarray | None = None) -> np.ndarray:
+    #From https://stackoverflow.com/a/1235363
+    arrays = [np.asarray(x) for x in arrays]
+    dtype = arrays[0].dtype
+
+    n = np.prod([x.size for x in arrays])
+    if out is None:
+        out = np.zeros([n, len(arrays)], dtype=dtype)
+
+    m = n // arrays[0].size
+    out[:, 0] = np.repeat(arrays[0], m)
+    if arrays[1:]:
+        _cartesian(arrays[1:], out=out[0:m, 1:])
+        for j in range(1, arrays[0].size):
+            out[j * m : (j + 1) * m, 1:] = out[0:m, 1:]
+    return out
+
+
+def _get_neighbor_idxes(x: float, n: int, limit: int) -> np.ndarray:
+    idxes = sorted(range(limit), key=lambda idx: (abs(x - idx), idx))[:n]
+    idxes = sorted(idxes)
+    return np.array(idxes)
+
+
+def _get_neighbor(x: float, n: int, data: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+
+    pad_width = np.ceil(n / 2).astype(int)
+    padded = np.pad(data, pad_width, mode="edge")
+    x += pad_width
+
+    idxes = _get_neighbor_idxes(x, n, len(padded))
+
+
+    ret = padded[idxes]
+    return idxes - pad_width, ret
+
+def linear_coeffs(ratio: float, scale: float | None = None) -> np.ndarray:
+    del scale  
+    return np.array([1 - ratio, ratio])
+
+
+def linear_coeffs_antialias(ratio: float, scale: float) -> np.ndarray:
+    scale = min(scale, 1.0)
+
+    start = int(np.floor(-1 / scale) + 1)
+    footprint = 2 - 2 * start
+    args = (np.arange(start, start + footprint) - ratio) * scale
+    coeffs = np.clip(1 - np.abs(args), 0, 1)
+
+    return np.array(coeffs) / sum(coeffs) 
+
+def cubic_coeffs_antialias(ratio: float, scale: float, A: float = -0.75) -> np.ndarray:
+    scale = min(scale, 1.0)
+
+    def compute_coeff(x: float) -> float:
+        x = abs(x)
+        x_2 = x * x
+        x_3 = x * x_2
+        if x <= 1:
+            return (A + 2) * x_3 - (A + 3) * x_2 + 1
+        if x < 2:
+            return A * x_3 - 5 * A * x_2 + 8 * A * x - 4 * A
+        return 0.0
+
+    i_start = int(np.floor(-2 / scale) + 1)
+    i_end = 2 - i_start
+    args = [scale * (i - ratio) for i in range(i_start, i_end)]
+    coeffs = [compute_coeff(x) for x in args]
+    return np.array(coeffs) / sum(coeffs)
+
+def nearest_coeffs(
+    ratio: float | int | np.ndarray, mode: str = "round_prefer_floor"
+) -> np.ndarray:
+    if isinstance(ratio, int) or ratio.is_integer():
+        return np.array([0, 1])
+    if mode == "round_prefer_floor":
+        return np.array([ratio <= 0.5, ratio > 0.5])
+    if mode == "round_prefer_ceil":
+        return np.array([ratio < 0.5, ratio >= 0.5])
+    if mode == "floor":
+        return np.array([1, 0])
+    if mode == "ceil":
+        return np.array([0, 1])
+    raise ValueError(f"Unexpected value {mode!r}.")
+
+
+
+def _interpolate_1d_with_x(
+    data: np.ndarray,
+    scale_factor: float,
+    output_width_int: int,
+    x: float,
+    get_coeffs: Callable[[float, float], np.ndarray],
+    roi: np.ndarray | None = None,
+    extrapolation_value: float = 0.0,
+    coordinate_transformation_mode: str = "half_pixel",
+    exclude_outside: bool = False,
+) -> np.ndarray:
+    
+    input_width = len(data)
+    output_width = scale_factor * input_width
+
+    if coordinate_transformation_mode == "align_corners":
+        if output_width == 1:
+            x_ori = 0.0
+        else:
+            x_ori = x * (input_width - 1) / (output_width - 1)
+    elif coordinate_transformation_mode == "asymmetric":
+        x_ori = x / scale_factor
+    elif coordinate_transformation_mode == "tf_crop_and_resize":
+        if roi is None:
+            raise ValueError("roi cannot be None.")
+        if output_width == 1:
+            x_ori = (roi[1] - roi[0]) * (input_width - 1) / 2
+        else:
+            x_ori = x * (roi[1] - roi[0]) * (input_width - 1) / (output_width - 1)
+        x_ori += roi[0] * (input_width - 1)
+
+        if x_ori < 0 or x_ori > input_width - 1:
+            return np.array(extrapolation_value)
+    elif coordinate_transformation_mode == "pytorch_half_pixel":
+        if output_width == 1:
+            x_ori = -0.5
+        else:
+            x_ori = (x + 0.5) / scale_factor - 0.5
+    elif coordinate_transformation_mode == "half_pixel":
+        x_ori = (x + 0.5) / scale_factor - 0.5
+    elif coordinate_transformation_mode == "half_pixel_symmetric":
+        adjustment = output_width_int / output_width
+        center = input_width / 2
+        offset = center * (1 - adjustment)
+        x_ori = offset + (x + 0.5) / scale_factor - 0.5
+    else:
+        raise ValueError(
+            f"Invalid coordinate_transformation_mode: {coordinate_transformation_mode!r}."
+        )
+
+    x_ori_int = np.floor(x_ori).astype(int).item()
+
+    if x_ori.is_integer():
+        ratio = 1
+    else:
+        ratio = x_ori - x_ori_int
+
+    coeffs = get_coeffs(ratio, scale_factor)
+    n = len(coeffs)
+
+    idxes, points = _get_neighbor(x_ori, n, data)
+
+    if exclude_outside:
+        for i, idx in enumerate(idxes):
+            if idx < 0 or idx >= input_width:
+                coeffs[i] = 0
+        coeffs /= sum(coeffs)
+
+    return np.dot(coeffs, points).item()  
+
+
+def _interpolate_nd_with_x(
+    data: np.ndarray,
+    n: int,
+    scale_factors: list[float],
+    output_size: list[int],
+    x: list[float],
+    get_coeffs: Callable[[float, float], np.ndarray],
+    roi: np.ndarray | None = None,
+    exclude_outside: bool = False,
+    **kwargs: Any,
+) -> np.ndarray:
+    
+    if n == 1:
+        return _interpolate_1d_with_x(
+            data,
+            scale_factors[0],
+            output_size[0],
+            x[0],
+            get_coeffs,
+            roi=roi,
+            exclude_outside=exclude_outside,
+            **kwargs,
+        )
+    res1d = []
+
+    for i in range(data.shape[0]):
+        r = _interpolate_nd_with_x(
+            data[i],
+            n - 1,
+            scale_factors[1:],
+            output_size[1:],
+            x[1:],
+            get_coeffs,
+            roi=None if roi is None else np.concatenate([roi[1:n], roi[n + 1 :]]),
+            exclude_outside=exclude_outside,
+            **kwargs,
+        )
+        res1d.append(r)
+    
+
+    return _interpolate_1d_with_x(
+        res1d, 
+        scale_factors[0],
+        output_size[0],
+        x[0],
+        get_coeffs,
+        roi=None if roi is None else [roi[0], roi[n]], 
+        exclude_outside=exclude_outside,
+        **kwargs,
+    )
+
+
+def _get_all_coords(data: np.ndarray) -> np.ndarray:
+    return _cartesian(
+        [list(range(data.shape[i])) for i in range(len(data.shape))]  
+    )
+
+
+def interpolate_nd(
+    data: np.ndarray,
+    get_coeffs: Callable[[float, float], np.ndarray],
+    output_size: list[int] | None = None,
+    scale_factors: list[float] | None = None,
+    axes: list[int] | None = None,
+    roi: np.ndarray | None = None,
+    keep_aspect_ratio_policy: str | None = "stretch",
+    exclude_outside: bool = False,
+    **kwargs: Any,
+) -> np.ndarray:
+    if output_size is None and scale_factors is None:
+        raise ValueError("output_size is None and scale_factors is None.")
+
+    r = len(data.shape)
+    if axes is not None:
+        if scale_factors is not None:
+            new_scale_factors = [1.0] * r
+            for i, d in enumerate(axes):
+                new_scale_factors[d] = scale_factors[i]
+            scale_factors = new_scale_factors
+
+        if output_size is not None:
+            new_output_size = [data.shape[i] for i in range(r)]
+            for i, d in enumerate(axes):
+                new_output_size[d] = output_size[i]
+            output_size = new_output_size
+
+
+        if roi is not None:
+            new_roi = ([0.0] * r) + ([1.0] * r)
+            naxes = len(axes)
+            for i, d in enumerate(axes):
+                new_roi[d] = roi[i]
+                new_roi[r + d] = roi[naxes + i]
+            roi = new_roi 
+    else:
+        axes = list(range(r))
+
+    if output_size is not None:
+        scale_factors = [output_size[i] / data.shape[i] for i in range(r)]
+        if keep_aspect_ratio_policy != "stretch":
+            if keep_aspect_ratio_policy == "not_larger":
+                scale = np.array(scale_factors)[axes].min()
+            elif keep_aspect_ratio_policy == "not_smaller":
+                scale = np.array(scale_factors)[axes].max()
+            else:
+                raise ValueError(
+                    f"Invalid keep_aspect_ratio_policy={keep_aspect_ratio_policy!r}"
+                )
+
+            scale_factors = [scale if i in axes else 1.0 for i in range(r)]
+
+            def round_half_up(x: float) -> int:
+                return int(x + 0.5)
+
+            output_size = [
+                round_half_up(scale * data.shape[i]) if i in axes else data.shape[i]
+                for i in range(r)
+            ]
+
+    else:
+        output_size = (scale_factors * np.array(data.shape)).astype(int) 
+    if scale_factors is None:
+        raise ValueError("scale_factors is None.")
+    if output_size is None:
+        raise ValueError("output_size is None.")
+
+    ret = np.zeros(output_size)
+    for x in _get_all_coords(ret):
+        ret[tuple(x)] = _interpolate_nd_with_x(
+            data,
+            len(data.shape),
+            scale_factors,
+            output_size,
+            x,
+            get_coeffs,
+            roi=roi,
+            exclude_outside=exclude_outside,
+            **kwargs,
+        )
+    return ret
+
+
+def cubic_coeffs(
+    ratio: float, scale: float | None = None, A: float = -0.75
+) -> np.ndarray:
+    del scale  # Unused
+    coeffs = [
+        ((A * (ratio + 1) - 5 * A) * (ratio + 1) + 8 * A) * (ratio + 1) - 4 * A,
+        ((A + 2) * ratio - (A + 3)) * ratio * ratio + 1,
+        ((A + 2) * (1 - ratio) - (A + 3)) * (1 - ratio) * (1 - ratio) + 1,
+        ((A * ((1 - ratio) + 1) - 5 * A) * ((1 - ratio) + 1) + 8 * A)
+        * ((1 - ratio) + 1)
+        - 4 * A,
+    ]
+    return np.array(coeffs)
+
+
+    
+
+class Resize(RunAll):
+
+    @staticmethod
+    def resize_upsample_scales_nearest() -> None:
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
+
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), scale_factors=scales
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_scales_nearest"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::HALF_PIXEL_SYMMETRIC),"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+
+    @staticmethod
+    def resize_downsample_scales_nearest() -> None:
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), scale_factors=scales
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_downsample_scales_nearest"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+
+    @staticmethod
+    def resize_upsample_sizes_nearest() -> None:
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 7, 8], dtype=np.int64)
+
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), output_size=sizes
+        ).astype(np.float32)
+
+        x = [data, sizes]
+        y = output
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten()) 
+           
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_sizes_nearest"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+
+    @staticmethod
+    def resize_downsample_sizes_nearest() -> None:
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 1, 3], dtype=np.int64)
+
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), output_size=sizes
+        ).astype(np.float32)
+
+        x = [data, sizes]
+        y = output
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten()) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_downsample_sizes_nearest"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+    @staticmethod
+    def resize_upsample_scales_linear() -> None:
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        output = interpolate_nd(
+            data, lambda x, _: linear_coeffs(x, None), scale_factors=scales
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_scales_linear"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::LINEAR),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+        
+    @staticmethod
+    def resize_upsample_scales_linear_align_corners() -> None:
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x, None),
+            scale_factors=scales,
+            coordinate_transformation_mode="align_corners",
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_scales_linear_align_corners"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS)," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::LINEAR),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+
+    @staticmethod
+    def resize_downsample_scales_linear() -> None:
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+        output = interpolate_nd(
+            data, lambda x, _: linear_coeffs(x, None), scale_factors=scales
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_scales_linear"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::LINEAR),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+    @staticmethod
+    def resize_downsample_scales_linear_align_corners() -> None:
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x, None),
+            scale_factors=scales,
+            coordinate_transformation_mode="align_corners",
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_downsample_scales_linear_align_corners"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS)," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::LINEAR),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+    @staticmethod
+    def resize_upsample_scales_cubic() -> None:
+
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        output = interpolate_nd(
+            data, lambda x, _: cubic_coeffs(x, None), scale_factors=scales
+        ).astype(np.float32)
+        
+        x = [data, scales]
+        y = output
+
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_scales_cubic"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::CUBIC),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+    @staticmethod
+    def resize_upsample_scales_cubic_align_corners() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        output = interpolate_nd(
+            data,
+            lambda x, _: cubic_coeffs(x),
+            scale_factors=scales,
+            coordinate_transformation_mode="align_corners",
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_scales_cubic_align_corners"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS)," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::CUBIC),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+    @staticmethod
+    def resize_downsample_scales_cubic() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+        output = interpolate_nd(
+            data, lambda x, _: cubic_coeffs(x), scale_factors=scales
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_downsample_scales_cubic"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::CUBIC),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+
+    @staticmethod
+    def resize_downsample_scales_cubic_align_corners() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+
+        output = interpolate_nd(
+            data,
+            lambda x, _: cubic_coeffs(x),
+            scale_factors=scales,
+            coordinate_transformation_mode="align_corners",
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_downsample_scales_cubic_align_corners"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS)," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::CUBIC),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+        
+    @staticmethod
+    def resize_upsample_sizes_cubic() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 9, 10], dtype=np.int64)
+        output = interpolate_nd(
+            data, lambda x, _: cubic_coeffs(x), output_size=sizes
+        ).astype(np.float32)
+
+        x = [data, sizes]
+        y = output
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten()) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_sizes_cubic"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::CUBIC),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+        
+    @staticmethod
+    def resize_downsample_sizes_cubic() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+        output = interpolate_nd(
+            data, lambda x, _: cubic_coeffs(x), output_size=sizes
+        ).astype(np.float32)
+
+        x = [data, sizes]
+        y = output
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten()) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_downsample_sizes_cubic"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::CUBIC),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+
+
+
+
+    @staticmethod
+    def resize_upsample_scales_cubic_A_n0p5_exclude_outside() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+        output = interpolate_nd(
+            data,
+            lambda x, _: cubic_coeffs(x, A=-0.5),
+            scale_factors=scales,
+            exclude_outside=True,
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_scales_cubic_A_n0p5_exclude_outside"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(FixedTrait::<FP16x16>::new(32768, true)),"
+        func_sig += "Option::Some(true),"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::CUBIC),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+
+    @staticmethod
+    def resize_downsample_scales_cubic_A_n0p5_exclude_outside() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+        output = interpolate_nd(
+            data,
+            lambda x, _: cubic_coeffs(x, A=-0.5),
+            scale_factors=scales,
+            exclude_outside=True,
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_downsample_scales_cubic_A_n0p5_exclude_outside"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(FixedTrait::<FP16x16>::new(32768, true)),"
+        func_sig += "Option::Some(true),"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::CUBIC),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+
+
+    @staticmethod
+    def resize_upsample_scales_cubic_asymmetric() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
+
+        output = interpolate_nd(
+            data,
+            lambda x, _: cubic_coeffs(x, A=-0.75),
+            scale_factors=scales,
+            coordinate_transformation_mode="asymmetric",
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_scales_cubic_asymmetric"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::ASYMMETRIC)," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::CUBIC),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+
+
+    @staticmethod
+    def resize_tf_crop_and_resize() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+        roi = np.array([0, 0, 0.4, 0.6, 1, 1, 0.6, 0.8], dtype=np.float32)
+        sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            output_size=sizes,
+            roi=roi,
+            coordinate_transformation_mode="tf_crop_and_resize",
+        ).astype(np.float32)
+        x = [data, sizes, roi]
+        y = output
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten()) 
+        x[2] = Tensor(Dtype.FP16x16, x[2].shape, to_fp(x[2].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+        name = "resize_tf_crop_and_resize"
+        func_sig = "data.resize("
+        func_sig += "roi,"
+        func_sig += "Option::None," 
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::TF_CROP_AND_RESIZE)," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::LINEAR),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1], x[2]], y, func_sig, name)
+
+    @staticmethod
+    def resize_tf_crop_and_resize_extrapolation_value() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        roi = np.array([0, 0, 0.4, 0.6, 1, 1, 1.2, 1.7], dtype=np.float32)
+        sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+        
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            output_size=sizes,
+            roi=roi,
+            coordinate_transformation_mode="tf_crop_and_resize",
+            extrapolation_value=10.0,
+        ).astype(np.float32)
+
+        x = [data, sizes, roi]
+        y = output
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten()) 
+        x[2] = Tensor(Dtype.FP16x16, x[2].shape, to_fp(x[2].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+        name = "resize_tf_crop_and_resize_extrapolation_value"
+        func_sig = "data.resize("
+        func_sig += "roi,"
+        func_sig += "Option::None," 
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::TF_CROP_AND_RESIZE)," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(FixedTrait::<FP16x16>::new(655360, false)),"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::LINEAR),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1], x[2]], y, func_sig, name)
+
+    @staticmethod
+    def resize_downsample_sizes_linear_pytorch_half_pixel() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 3, 1], dtype=np.int64)
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            output_size=sizes,
+            coordinate_transformation_mode="pytorch_half_pixel",
+        ).astype(np.float32)
+
+        x = [data, sizes]
+        y = output
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())  
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+        name = "resize_downsample_sizes_linear_pytorch_half_pixel"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::PYTORCH_HALF_PIXEL)," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::LINEAR),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+
+
+    @staticmethod
+    def resize_upsample_sizes_nearest_floor_align_corners() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+        output = interpolate_nd(
+            data,
+            lambda x, _: nearest_coeffs(x, mode="floor"),
+            output_size=sizes,
+            coordinate_transformation_mode="align_corners",
+        ).astype(np.float32)
+
+        x = [data, sizes]
+        y = output
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())  
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+        name = "resize_upsample_sizes_nearest_floor_align_corners"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS)," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::Some(NEAREST_MODE::FLOOR),)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+    @staticmethod
+    def resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+
+        output = interpolate_nd(
+            data,
+            lambda x, _: nearest_coeffs(x, mode="round_prefer_ceil"),
+            output_size=sizes,
+            coordinate_transformation_mode="asymmetric",
+        ).astype(np.float32)
+
+        x = [data, sizes]
+        y = output
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())  
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+        name = "resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::ASYMMETRIC)," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::Some(NEAREST_MODE::ROUND_PREFER_CEIL),)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+
+    @staticmethod
+    def resize_upsample_sizes_nearest_ceil_half_pixel() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 8, 8], dtype=np.int64)
+
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x, mode="ceil"), output_size=sizes
+        ).astype(np.float32)
+
+        x = [data, sizes]
+        y = output
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())  
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+        name = "resize_upsample_sizes_nearest_ceil_half_pixel"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::HALF_PIXEL)," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::Some(NEAREST_MODE::CEIL),)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+    @staticmethod
+    def resize_downsample_scales_linear_antialias() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+        output = interpolate_nd(
+            data, linear_coeffs_antialias, scale_factors=scales
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_downsample_scales_linear_antialias"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(1),"
+        func_sig += "Option::None," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::LINEAR),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+    @staticmethod
+    def resize_downsample_sizes_linear_antialias() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+        output = interpolate_nd(
+            data, linear_coeffs_antialias, output_size=sizes
+        ).astype(np.float32)
+
+        x = [data, sizes]
+        y = output
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())  
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+        name = "resize_downsample_sizes_linear_pytorch_half_pixel"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "sizes,"
+        func_sig += "Option::Some(1),"
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::LINEAR),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+
+    @staticmethod
+    def resize_downsample_scales_cubic_antialias() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+
+        output = interpolate_nd(
+            data, cubic_coeffs_antialias, scale_factors=scales
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_downsample_scales_cubic_antialias"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(1),"
+        func_sig += "Option::None," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::CUBIC),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+    @staticmethod
+    def resize_downsample_sizes_cubic_antialias() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 1, 3, 3], dtype=np.int64)
+
+        output = interpolate_nd(data, cubic_coeffs_antialias, output_size=sizes).astype(
+            np.float32
+        )
+        x = [data, sizes]
+        y = output
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())  
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+        name = "resize_downsample_sizes_cubic_antialias"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "sizes,"
+        func_sig += "Option::Some(1),"
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::CUBIC),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+    @staticmethod
+    def resize_upsample_scales_nearest_axes_2_3() -> None:
+        axes = np.array([2, 3], dtype=np.int64)
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        scales = np.array([2.0, 3.0], dtype=np.float32)
+
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes
+        ).astype(np.float32)
+
+        x = [data, scales, axes]
+        y = output
+
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.FP16x16, x[1].shape, to_fp(x[1].flatten(), FixedImpl.FP16x16)) 
+        x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())  
+
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_scales_nearest_axes_2_3"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "axes," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1], x[2]], y, func_sig, name)
+
+    @staticmethod
+    def resize_upsample_scales_nearest_axes_3_2() -> None:
+        
+        axes = np.array([3, 2], dtype=np.int64)
+        data = np.array([[[[1, 2],[3, 4],]]],dtype=np.float32,)
+
+        scales = np.array([3.0, 2.0], dtype=np.float32)
+
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes
+        ).astype(np.float32)
+        x = [data, scales, axes]
+        y = output
+
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.FP16x16, x[1].shape, to_fp(x[1].flatten(), FixedImpl.FP16x16)) 
+        x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())  
+
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_scales_nearest_axes_3_2"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "axes," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1], x[2]], y, func_sig, name)
+
+    @staticmethod
+    def resize_upsample_sizes_nearest_axes_2_3() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([7, 8], dtype=np.int64)
+        axes = np.array([2, 3], dtype=np.int64)
+
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes
+        ).astype(np.float32)
+
+        x = [data, sizes, axes]
+        y = output
+
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten()) 
+        x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())  
+
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_sizes_nearest_axes_2_3"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "axes," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1], x[2]], y, func_sig, name)
+
+    @staticmethod
+    def resize_upsample_sizes_nearest_axes_3_2() -> None:
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([8, 7], dtype=np.int64)
+        axes = np.array([3, 2], dtype=np.int64)
+
+        output = interpolate_nd(
+            data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes
+        ).astype(np.float32)
+
+        x = [data, sizes, axes]
+        y = output
+
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten()) 
+        x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())  
+
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_sizes_nearest_axes_3_2"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "axes," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1], x[2]], y, func_sig, name)
+
+        
+    @staticmethod
+    def resize_tf_crop_and_resize_axes_2_3() -> None:
+        axes = np.array([2, 3], dtype=np.int64)
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        roi = np.array([0.4, 0.6, 0.6, 0.8], dtype=np.float32)
+        sizes = np.array([3, 3], dtype=np.int64)
+
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            output_size=sizes,
+            roi=roi,
+            axes=axes,
+            coordinate_transformation_mode="tf_crop_and_resize",
+        ).astype(np.float32)
+
+        x = [data, sizes, roi, axes]
+        y = output
+
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten()) 
+        x[2] = Tensor(Dtype.FP16x16, x[2].shape, to_fp(x[2].flatten(), FixedImpl.FP16x16)) 
+        x[3] = Tensor(Dtype.U32, x[3].shape, x[3].flatten())  
+
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_tf_crop_and_resize_axes_2_3"
+        func_sig = "data.resize("
+        func_sig += "roi,"
+        func_sig += "Option::None,"
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "axes," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::TF_CROP_AND_RESIZE)," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::LINEAR),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1], x[2], x[3]], y, func_sig, name)
+
+    @staticmethod
+    def resize_tf_crop_and_resize_axes_3_2() -> None:
+        axes = np.array([3, 2], dtype=np.int64)
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                        [9, 10, 11, 12],
+                        [13, 14, 15, 16],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        roi = np.array([0.6, 0.4, 0.8, 0.6], dtype=np.float32)
+        sizes = np.array([3, 3], dtype=np.int64)
+
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            output_size=sizes,
+            roi=roi,
+            axes=axes,
+            coordinate_transformation_mode="tf_crop_and_resize",
+        ).astype(np.float32)
+
+        x = [data, sizes, roi, axes]
+        y = output
+
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten()) 
+        x[2] = Tensor(Dtype.FP16x16, x[2].shape, to_fp(x[2].flatten(), FixedImpl.FP16x16)) 
+        x[3] = Tensor(Dtype.U32, x[3].shape, x[3].flatten())  
+
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_tf_crop_and_resize_axes_3_2"
+        func_sig = "data.resize("
+        func_sig += "roi,"
+        func_sig += "Option::None,"
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "axes," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::TF_CROP_AND_RESIZE)," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::LINEAR),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1], x[2], x[3]], y, func_sig, name)
+
+
+
+    @staticmethod
+    def resize_upsample_sizes_nearest_not_larger() -> None:
+        keep_aspect_ratio_policy = "not_larger"
+        axes = np.array([2, 3], dtype=np.int64)
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([7, 8], dtype=np.int64) 
+        output = interpolate_nd(
+            data,
+            lambda x, _: nearest_coeffs(x),
+            output_size=sizes,
+            axes=axes,
+            keep_aspect_ratio_policy=keep_aspect_ratio_policy,
+        ).astype(np.float32)
+
+        x = [data, sizes, axes]
+        y = output
+
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten()) 
+        x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())  
+
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_sizes_nearest_not_larger"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "axes," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(KEEP_ASPECT_RATIO_POLICY::NOT_LARGER),"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1], x[2]], y, func_sig, name)
+
+
+
+    @staticmethod
+    def resize_upsample_sizes_nearest_not_smaller() -> None:
+        keep_aspect_ratio_policy = "not_smaller"
+        axes = np.array([2, 3], dtype=np.int64)
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2],
+                        [3, 4],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([7, 8], dtype=np.int64)  # Results in 8x8
+
+        output = interpolate_nd(
+            data,
+            lambda x, _: nearest_coeffs(x),
+            output_size=sizes,
+            axes=axes,
+            keep_aspect_ratio_policy=keep_aspect_ratio_policy,
+        ).astype(np.float32)
+
+        x = [data, sizes, axes]
+        y = output
+
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten()) 
+        x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())  
+
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_sizes_nearest_not_smaller"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "axes," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(KEEP_ASPECT_RATIO_POLICY::NOT_SMALLER),"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1], x[2]], y, func_sig, name)
+
+
+
+
+    @staticmethod
+    def resize_downsample_sizes_nearest_not_larger() -> None:
+        keep_aspect_ratio_policy = "not_larger" 
+        axes = np.array([2, 3], dtype=np.int64)
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 3], dtype=np.int64)
+
+        output = interpolate_nd(
+            data,
+            lambda x, _: nearest_coeffs(x),
+            output_size=sizes,
+            axes=axes,
+            keep_aspect_ratio_policy=keep_aspect_ratio_policy,
+        ).astype(np.float32)
+
+        x = [data, sizes, axes]
+        y = output
+
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten()) 
+        x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())  
+
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_downsample_sizes_nearest_not_larger"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "axes," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(KEEP_ASPECT_RATIO_POLICY::NOT_LARGER),"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1], x[2]], y, func_sig, name)
+
+
+
+    @staticmethod
+    def resize_downsample_sizes_nearest_not_smaller() -> None:
+        keep_aspect_ratio_policy = "not_smaller"
+        axes = np.array([2, 3], dtype=np.int64)
+        
+        data = np.array(
+            [
+                [
+                    [
+                        [1, 2, 3, 4],
+                        [5, 6, 7, 8],
+                    ]
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        sizes = np.array([1, 3], dtype=np.int64)  
+        output = interpolate_nd(
+            data,
+            lambda x, _: nearest_coeffs(x),
+            output_size=sizes,
+            axes=axes,
+            keep_aspect_ratio_policy=keep_aspect_ratio_policy,
+        ).astype(np.float32)
+
+        x = [data, sizes, axes]
+        y = output
+
+        x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16)) 
+        x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten()) 
+        x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())  
+
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_downsample_sizes_nearest_not_smaller"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "sizes,"
+        func_sig += "Option::None,"
+        func_sig += "axes," 
+        func_sig += "Option::None," 
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(KEEP_ASPECT_RATIO_POLICY::NOT_SMALLER),"
+        func_sig += "Option::Some(MODE::NEAREST),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1], x[2]], y, func_sig, name)
+
+
+
+
+    @staticmethod
+    def resize_downsample_scales_linear_half_pixel_symmetric() -> None:
+        data = np.array([[[[1, 2, 3, 4]]]], dtype=np.float32)
+        scales = np.array([1.0, 1.0, 1.0, 0.6], dtype=np.float32)
+
+
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            scale_factors=scales,
+            coordinate_transformation_mode="half_pixel_symmetric",
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_downsample_scales_linear_half_pixel_symmetric"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::HALF_PIXEL_SYMMETRIC),"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::LINEAR),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+
+
+    @staticmethod
+    def resize_upsample_scales_linear_half_pixel_symmetric() -> None:
+        data = np.array([[[[1, 2], [3, 4]]]], dtype=np.float32)
+        scales = np.array([1.0, 1.0, 2.3, 2.94], dtype=np.float32)
+
+        output = interpolate_nd(
+            data,
+            lambda x, _: linear_coeffs(x),
+            scale_factors=scales,
+            coordinate_transformation_mode="half_pixel_symmetric",
+        ).astype(np.float32)
+
+        x = [data, scales]
+        y = output
+        for i in range(len(x)):
+            x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16)) 
+        
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
+
+        name = "resize_upsample_scales_linear_half_pixel_symmetric"
+        func_sig = "data.resize("
+        func_sig += "Option::None,"
+        func_sig += "scales,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None," 
+        func_sig += "Option::Some(TRANSFORMATION_MODE::HALF_PIXEL_SYMMETRIC),"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::None,"
+        func_sig += "Option::Some(MODE::LINEAR),"
+        func_sig += "Option::None,)"
+        make_test([x[0], x[1]], y, func_sig, name)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/nodegen/node/sequence_at.py b/nodegen/node/sequence_at.py
index b65ac5dae..a108ad52a 100644
--- a/nodegen/node/sequence_at.py
+++ b/nodegen/node/sequence_at.py
@@ -1,6 +1,6 @@
 import numpy as np
 from nodegen.node import RunAll
-from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
+from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
 
 
 scalar = lambda x: Tensor(Dtype.I32, (), np.array([x]).astype(np.int32).flatten())
@@ -23,7 +23,7 @@ def positive_position():
             position = scalar(2)
 
             name = "sequence_at_u32_positive"
-            make_test([sequence, position], sequence[2], "TensorTrait::sequence_at(input_0, input_1)", name)
+            make_test([sequence, position], sequence[2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
 
         def negative_position():
             sequence = []
@@ -38,7 +38,7 @@ def negative_position():
             position = scalar(-2)
 
             name = "sequence_at_u32_negative"
-            make_test([sequence, position], sequence[-2], "TensorTrait::sequence_at(input_0, input_1)", name)
+            make_test([sequence, position], sequence[-2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
 
         positive_position()
         negative_position()
@@ -59,7 +59,7 @@ def positive_position():
             position = scalar(2)
 
             name = "sequence_at_i32_positive"
-            make_test([sequence, position], sequence[2], "TensorTrait::sequence_at(input_0, input_1)", name)
+            make_test([sequence, position], sequence[2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
 
         def negative_position():
             sequence = []
@@ -74,7 +74,7 @@ def negative_position():
             position = scalar(-2)
 
             name = "sequence_at_i32_negative"
-            make_test([sequence, position], sequence[-2], "TensorTrait::sequence_at(input_0, input_1)", name)
+            make_test([sequence, position], sequence[-2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
 
         positive_position()
         negative_position()
@@ -95,7 +95,7 @@ def positive_position():
             position = scalar(2)
 
             name = "sequence_at_i8_positive"
-            make_test([sequence, position], sequence[2], "TensorTrait::sequence_at(input_0, input_1)", name)
+            make_test([sequence, position], sequence[2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
 
         def negative_position():
             sequence = []
@@ -110,7 +110,7 @@ def negative_position():
             position = scalar(-2)
 
             name = "sequence_at_i8_negative"
-            make_test([sequence, position], sequence[-2], "TensorTrait::sequence_at(input_0, input_1)", name)
+            make_test([sequence, position], sequence[-2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
 
         positive_position()
         negative_position()
@@ -131,7 +131,7 @@ def positive_position():
             position = scalar(2)
 
             name = "sequence_at_fp8x23_positive"
-            make_test([sequence, position], sequence[2], "TensorTrait::sequence_at(input_0, input_1)", name)
+            make_test([sequence, position], sequence[2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
 
         def negative_position():
             sequence = []
@@ -146,7 +146,7 @@ def negative_position():
             position = scalar(-2)
 
             name = "sequence_at_fp8x23_negative"
-            make_test([sequence, position], sequence[-2], "TensorTrait::sequence_at(input_0, input_1)", name)
+            make_test([sequence, position], sequence[-2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
 
         positive_position()
         negative_position()
@@ -167,7 +167,7 @@ def positive_position():
             position = scalar(2)
 
             name = "sequence_at_fp16x16_positive"
-            make_test([sequence, position], sequence[2], "TensorTrait::sequence_at(input_0, input_1)", name)
+            make_test([sequence, position], sequence[2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
 
         def negative_position():
             sequence = []
@@ -182,7 +182,7 @@ def negative_position():
             position = scalar(-2)
 
             name = "sequence_at_fp16x16_negative"
-            make_test([sequence, position], sequence[-2], "TensorTrait::sequence_at(input_0, input_1)", name)
+            make_test([sequence, position], sequence[-2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
 
         positive_position()
         negative_position()
diff --git a/nodegen/node/sequence_construct.py b/nodegen/node/sequence_construct.py
index 7c76d1532..fba919f56 100644
--- a/nodegen/node/sequence_construct.py
+++ b/nodegen/node/sequence_construct.py
@@ -1,6 +1,6 @@
 import numpy as np
 from nodegen.node import RunAll
-from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
+from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
 
 
 class Sequence_construct(RunAll):
@@ -18,7 +18,7 @@ def sequence_construct_u32():
             sequence.append(tensor)
 
         name = "sequence_construct_u32"
-        make_test([sequence], sequence, "TensorTrait::sequence_construct(input_0)", name)
+        make_test([sequence], sequence, "SequenceTrait::sequence_construct(input_0)", name, Trait.SEQUENCE)
 
 
     @staticmethod
@@ -34,7 +34,7 @@ def sequence_construct_i32():
             sequence.append(tensor)
 
         name = "sequence_construct_i32"
-        make_test([sequence], sequence, "TensorTrait::sequence_construct(input_0)", name)
+        make_test([sequence], sequence, "SequenceTrait::sequence_construct(input_0)", name, Trait.SEQUENCE)
 
 
     @staticmethod
@@ -50,7 +50,7 @@ def sequence_construct_i8():
             sequence.append(tensor)
 
         name = "sequence_construct_i8"
-        make_test([sequence], sequence, "TensorTrait::sequence_construct(input_0)", name)
+        make_test([sequence], sequence, "SequenceTrait::sequence_construct(input_0)", name, Trait.SEQUENCE)
 
 
     @staticmethod
@@ -66,7 +66,7 @@ def sequence_construct_fp8x23():
             sequence.append(tensor)
 
         name = "sequence_construct_fp8x23"
-        make_test([sequence], sequence, "TensorTrait::sequence_construct(input_0)", name)
+        make_test([sequence], sequence, "SequenceTrait::sequence_construct(input_0)", name, Trait.SEQUENCE)
 
 
     @staticmethod
@@ -82,4 +82,4 @@ def sequence_construct_fp16x16():
             sequence.append(tensor)
 
         name = "sequence_construct_fp16x16"
-        make_test([sequence], sequence, "TensorTrait::sequence_construct(input_0)", name)
+        make_test([sequence], sequence, "SequenceTrait::sequence_construct(input_0)", name, Trait.SEQUENCE)
diff --git a/nodegen/node/sequence_empty.py b/nodegen/node/sequence_empty.py
index 91dc78dbc..779859fa5 100644
--- a/nodegen/node/sequence_empty.py
+++ b/nodegen/node/sequence_empty.py
@@ -1,6 +1,6 @@
 import numpy as np
 from nodegen.node import RunAll
-from ..helpers import make_test, Dtype, Tensor
+from ..helpers import make_test, Dtype, Tensor, Trait
 
 
 class Sequence_empty(RunAll):
@@ -14,8 +14,9 @@ def default():
             make_test(
                 inputs=[],
                 output=[t],
-                func_sig="TensorTrait::sequence_empty()",
+                func_sig="SequenceTrait::sequence_empty()",
                 name="sequence_empty_u32",
+                trait=Trait.SEQUENCE
             )
 
         default()
@@ -29,8 +30,9 @@ def default():
             make_test(
                 inputs=[],
                 output=[t],
-                func_sig="TensorTrait::sequence_empty()",
+                func_sig="SequenceTrait::sequence_empty()",
                 name="sequence_empty_i32",
+                trait=Trait.SEQUENCE
             )
 
         default()
@@ -44,8 +46,9 @@ def default():
             make_test(
                 inputs=[],
                 output=[t],
-                func_sig="TensorTrait::sequence_empty()",
+                func_sig="SequenceTrait::sequence_empty()",
                 name="sequence_empty_i8",
+                trait=Trait.SEQUENCE
             )
 
         default()
@@ -59,8 +62,9 @@ def default():
             make_test(
                 inputs=[],
                 output=[t],
-                func_sig="TensorTrait::sequence_empty()",
+                func_sig="SequenceTrait::sequence_empty()",
                 name="sequence_empty_fp8x23",
+                trait=Trait.SEQUENCE
             )
 
         default()
@@ -74,8 +78,9 @@ def default():
             make_test(
                 inputs=[],
                 output=[t],
-                func_sig="TensorTrait::sequence_empty()",
+                func_sig="SequenceTrait::sequence_empty()",
                 name="sequence_empty_fp16x16",
+                trait=Trait.SEQUENCE
             )
 
         default()
diff --git a/nodegen/node/sequence_erase.py b/nodegen/node/sequence_erase.py
index 8f8f07c46..a57b2e7d2 100644
--- a/nodegen/node/sequence_erase.py
+++ b/nodegen/node/sequence_erase.py
@@ -1,6 +1,6 @@
 import numpy as np
 from nodegen.node import RunAll
-from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
+from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
 
 
 scalar = lambda x: Tensor(Dtype.I32, (), np.array([x]).astype(np.int32).flatten())
@@ -26,7 +26,7 @@ def positive_position():
             output_sequence.pop(2)
 
             name = "sequence_erase_u32_positive"
-            make_test([sequence, position], output_sequence, "TensorTrait::sequence_erase(input_0, Option::Some(input_1))", name)
+            make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
 
         def negative_position():
             sequence = []
@@ -44,7 +44,7 @@ def negative_position():
             output_sequence.pop(-2)
 
             name = "sequence_erase_u32_negative"
-            make_test([sequence, position], output_sequence, "TensorTrait::sequence_erase(input_0, Option::Some(input_1))", name)
+            make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
 
         def empty_position():
             sequence = []
@@ -60,7 +60,7 @@ def empty_position():
             output_sequence.pop(-1)
 
             name = "sequence_erase_u32_empty"
-            make_test([sequence], output_sequence, "TensorTrait::sequence_erase(input_0, Option::None(()))", name)
+            make_test([sequence], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::None(()))", name, Trait.SEQUENCE)
 
         positive_position()
         negative_position()
@@ -85,7 +85,7 @@ def positive_position():
             output_sequence.pop(2)
 
             name = "sequence_erase_i32_positive"
-            make_test([sequence, position], output_sequence, "TensorTrait::sequence_erase(input_0, Option::Some(input_1))", name)
+            make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
 
         def negative_position():
             sequence = []
@@ -103,7 +103,7 @@ def negative_position():
             output_sequence.pop(-2)
 
             name = "sequence_erase_i32_negative"
-            make_test([sequence, position], output_sequence, "TensorTrait::sequence_erase(input_0, Option::Some(input_1))", name)
+            make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
 
         def empty_position():
             sequence = []
@@ -119,7 +119,7 @@ def empty_position():
             output_sequence.pop(-1)
 
             name = "sequence_erase_i32_empty"
-            make_test([sequence], output_sequence, "TensorTrait::sequence_erase(input_0, Option::None(()))", name)
+            make_test([sequence], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::None(()))", name, Trait.SEQUENCE)
 
         positive_position()
         negative_position()
@@ -144,7 +144,7 @@ def positive_position():
             output_sequence.pop(2)
 
             name = "sequence_erase_i8_positive"
-            make_test([sequence, position], output_sequence, "TensorTrait::sequence_erase(input_0, Option::Some(input_1))", name)
+            make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
 
         def negative_position():
             sequence = []
@@ -162,7 +162,7 @@ def negative_position():
             output_sequence.pop(-2)
 
             name = "sequence_erase_i8_negative"
-            make_test([sequence, position], output_sequence, "TensorTrait::sequence_erase(input_0, Option::Some(input_1))", name)
+            make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
 
         def empty_position():
             sequence = []
@@ -178,7 +178,7 @@ def empty_position():
             output_sequence.pop(-1)
 
             name = "sequence_erase_i8_empty"
-            make_test([sequence], output_sequence, "TensorTrait::sequence_erase(input_0, Option::None(()))", name)
+            make_test([sequence], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::None(()))", name, Trait.SEQUENCE)
 
         positive_position()
         negative_position()
@@ -203,7 +203,7 @@ def positive_position():
             output_sequence.pop(2)
 
             name = "sequence_erase_fp8x23_positive"
-            make_test([sequence, position], output_sequence, "TensorTrait::sequence_erase(input_0, Option::Some(input_1))", name)
+            make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
 
         def negative_position():
             sequence = []
@@ -221,7 +221,7 @@ def negative_position():
             output_sequence.pop(-2)
 
             name = "sequence_erase_fp8x23_negative"
-            make_test([sequence, position], output_sequence, "TensorTrait::sequence_erase(input_0, Option::Some(input_1))", name)
+            make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
 
         def empty_position():
             sequence = []
@@ -237,7 +237,7 @@ def empty_position():
             output_sequence.pop(-1)
 
             name = "sequence_erase_fp8x23_empty"
-            make_test([sequence], output_sequence, "TensorTrait::sequence_erase(input_0, Option::None(()))", name)
+            make_test([sequence], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::None(()))", name, Trait.SEQUENCE)
 
         positive_position()
         negative_position()
@@ -262,7 +262,7 @@ def positive_position():
             output_sequence.pop(2)
 
             name = "sequence_erase_fp16x16_positive"
-            make_test([sequence, position], output_sequence, "TensorTrait::sequence_erase(input_0, Option::Some(input_1))", name)
+            make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
 
         def negative_position():
             sequence = []
@@ -280,7 +280,7 @@ def negative_position():
             output_sequence.pop(-2)
 
             name = "sequence_erase_fp16x16_negative"
-            make_test([sequence, position], output_sequence, "TensorTrait::sequence_erase(input_0, Option::Some(input_1))", name)
+            make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
 
         def empty_position():
             sequence = []
@@ -296,7 +296,7 @@ def empty_position():
             output_sequence.pop(-1)
 
             name = "sequence_erase_fp16x16_empty"
-            make_test([sequence], output_sequence, "TensorTrait::sequence_erase(input_0, Option::None(()))", name)
+            make_test([sequence], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::None(()))", name, Trait.SEQUENCE)
 
         positive_position()
         negative_position()
diff --git a/nodegen/node/sequence_insert.py b/nodegen/node/sequence_insert.py
index 9cc2ceb82..e4ae7be55 100644
--- a/nodegen/node/sequence_insert.py
+++ b/nodegen/node/sequence_insert.py
@@ -1,6 +1,6 @@
 import numpy as np
 from nodegen.node import RunAll
-from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
+from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
 
 
 scalar = lambda x: Tensor(Dtype.I32, (), np.array([x]).astype(np.int32).flatten())
@@ -30,7 +30,7 @@ def default():
             expected_sequence.insert(position, tensor)
 
             name = "sequence_insert_u32"
-            make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name)
+            make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name, Trait.SEQUENCE)
 
         default()
 
@@ -56,7 +56,7 @@ def default():
             expected_sequence.insert(position, tensor)
 
             name = "sequence_insert_i32"
-            make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name)
+            make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name, Trait.SEQUENCE)
 
         default()
 
@@ -82,7 +82,7 @@ def default():
             expected_sequence.insert(position, tensor)
 
             name = "sequence_insert_i8"
-            make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name)
+            make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name, Trait.SEQUENCE)
 
         default()
 
@@ -110,7 +110,7 @@ def default():
             expected_sequence.insert(position, tensor)
 
             name = "sequence_insert_fp8x23"
-            make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name)
+            make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name, Trait.SEQUENCE)
 
         default()
 
@@ -138,6 +138,6 @@ def default():
             expected_sequence.insert(position, tensor)
 
             name = "sequence_insert_fp16x16"
-            make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name)
+            make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name, Trait.SEQUENCE)
 
         default()
diff --git a/nodegen/node/sequence_length.py b/nodegen/node/sequence_length.py
index 87f0dcd01..7b8993309 100644
--- a/nodegen/node/sequence_length.py
+++ b/nodegen/node/sequence_length.py
@@ -1,6 +1,6 @@
 import numpy as np
 from nodegen.node import RunAll
-from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
+from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
 
 
 scalar = lambda x: Tensor(Dtype.U32, (), np.array([x]).astype(np.uint32).flatten())
@@ -21,7 +21,7 @@ def default():
                 sequence.append(tensor)
 
             name = "sequence_length_u32"
-            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name)
+            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
 
         def broadcast():
             sequence = []
@@ -35,7 +35,7 @@ def broadcast():
                 sequence.append(tensor)
 
             name = "sequence_length_u32_broadcast"
-            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name)
+            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
 
         default()
         broadcast()
@@ -54,7 +54,7 @@ def default():
                 sequence.append(tensor)
 
             name = "sequence_length_i32"
-            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name)
+            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
 
         def broadcast():
             sequence = []
@@ -68,7 +68,7 @@ def broadcast():
                 sequence.append(tensor)
 
             name = "sequence_length_i32_broadcast"
-            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name)
+            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
 
         default()
         broadcast()
@@ -87,7 +87,7 @@ def default():
                 sequence.append(tensor)
 
             name = "sequence_length_i8"
-            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name)
+            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
 
         def broadcast():
             sequence = []
@@ -101,7 +101,7 @@ def broadcast():
                 sequence.append(tensor)
 
             name = "sequence_length_i8_broadcast"
-            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name)
+            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
 
         default()
         broadcast()
@@ -120,7 +120,7 @@ def default():
                 sequence.append(tensor)
 
             name = "sequence_length_fp8x23"
-            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name)
+            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
 
         def broadcast():
             sequence = []
@@ -134,7 +134,7 @@ def broadcast():
                 sequence.append(tensor)
 
             name = "sequence_length_fp8x23_broadcast"
-            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name)
+            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
 
         default()
         broadcast()
@@ -153,7 +153,7 @@ def default():
                 sequence.append(tensor)
 
             name = "sequence_length_fp16x16"
-            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name)
+            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
 
         def broadcast():
             sequence = []
@@ -167,7 +167,7 @@ def broadcast():
                 sequence.append(tensor)
 
             name = "sequence_length_fp16x16_broadcast"
-            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name)
+            make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
 
         default()
         broadcast()
diff --git a/nodegen/node/softmax_zero.py b/nodegen/node/softmax_zero.py
new file mode 100644
index 000000000..40e5528cb
--- /dev/null
+++ b/nodegen/node/softmax_zero.py
@@ -0,0 +1,48 @@
+import numpy as np
+from nodegen.node import RunAll
+from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
+
+
+def softmax_zero(x: np.ndarray, axis: int = -1) -> np.ndarray:
+    x_max = np.max(x, axis=axis, keepdims=True)
+    tmp = np.exp(x - x_max)
+    tmp = np.where(x == 0.0, 0.0, tmp)
+
+    s = np.sum(tmp, axis=axis, keepdims=True)
+    s = np.where(s == 0.0, 1, s)
+
+    
+    return tmp / s
+
+
+class Softmax_zero(RunAll):
+
+
+    @staticmethod
+    def fp8x23():
+        x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
+        y = softmax_zero(x)
+
+        x = Tensor(Dtype.FP8x23, x.shape, to_fp(
+            x.flatten(), FixedImpl.FP8x23))
+        y = Tensor(Dtype.FP8x23, y.shape, to_fp(
+            y.flatten(), FixedImpl.FP8x23))
+
+        name = "softmax_zero_fp8x23"
+        make_test([x], y, "NNTrait::softmax_zero(@input_0, 1)",
+                    name, Trait.NN)
+
+    @staticmethod
+    def fp16x16():
+        x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
+        y = softmax_zero(x)
+
+        x = Tensor(Dtype.FP16x16, x.shape, to_fp(
+            x.flatten(), FixedImpl.FP16x16))
+        y = Tensor(Dtype.FP16x16, y.shape, to_fp(
+            y.flatten(), FixedImpl.FP16x16))
+
+        name = "softmax_zero_fp16x16"
+        make_test([x], y, "NNTrait::softmax_zero(@input_0, 1)",
+                    name, Trait.NN)
+
diff --git a/nodegen/node/split.py b/nodegen/node/split.py
new file mode 100644
index 000000000..8e765141e
--- /dev/null
+++ b/nodegen/node/split.py
@@ -0,0 +1,261 @@
+import numpy as np
+from nodegen.node import RunAll
+from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
+
+
+class Split(RunAll):
+    @staticmethod
+    def split_u32():
+        def split_1D():
+            x = np.random.randint(0, 255, 6).astype(np.uint32)
+            y = [
+                np.array(x[0:2]).astype(np.uint32), 
+                np.array(x[2:4]).astype(np.uint32),
+                np.array(x[4:6]).astype(np.uint32),
+            ]
+
+            _x = Tensor(Dtype.U32, x.shape, x.flatten())
+            _y = [
+                Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
+                Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
+                Tensor(Dtype.U32, y[2].shape, y[2].flatten()),
+            ]
+
+            name = "split_u32_1d_equal_parts"
+            make_test(
+                [_x], _y, "input_0.split(0, Option::Some(3), Option::None(()))", name)
+            y = [
+                np.array(x[0:2]).astype(np.uint32), 
+                np.array(x[2:6]).astype(np.uint32),
+            ]
+            _y = [
+                Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
+                Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
+            ]
+            name = "split_u32_1d_variable_parts"
+            make_test(
+                [_x], _y, "input_0.split(0, Option::None(()), Option::Some(TensorTrait::<u32>::new(shape: array![2].span(), data: array![2, 4].span(),)))", name)
+        def split_2D():
+            x = np.random.randint(0, 255, (2, 6)).astype(np.uint32)
+            y = [
+                np.array(x[0:2, 0:3]).astype(np.uint32),
+                np.array(x[0:2, 3:6]).astype(np.uint32),
+            ]
+            _x = Tensor(Dtype.U32, x.shape, x.flatten())
+            _y = [
+                Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
+                Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
+            ]
+            name = "split_u32_2d_equal_parts"
+            make_test(
+                [_x], _y, "input_0.split(1, Option::Some(2), Option::None(()))", name)
+
+            y = [
+                np.array(x[0:2, 0:2]).astype(np.uint32), 
+                np.array(x[0:2, 2:6]).astype(np.uint32)
+            ]
+            _y = [
+                Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
+                Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
+            ]
+            name = "split_u32_2d_variable_parts"
+            make_test(
+                [_x], _y, "input_0.split(1, Option::None(()), Option::Some(TensorTrait::<u32>::new(shape: array![2].span(), data: array![2, 4].span(),)))", name)
+        
+        def split_zero_size():
+            # 1-dimensional tensor with dimension_size=0
+            x = np.array([]).astype(np.uint32)
+            y = [
+                np.array([]).astype(np.uint32),
+                np.array([]).astype(np.uint32),
+                np.array([]).astype(np.uint32),
+            ]
+            _x = Tensor(Dtype.U32, x.shape, x.flatten())
+            _y = [
+                Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
+                Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
+                Tensor(Dtype.U32, y[2].shape, y[2].flatten()),
+            ]
+            # Split emtpy tensor to tensors of size zero
+            name = "split_u32_zero_size"
+            make_test(
+                [_x], _y, "input_0.split(0, Option::None(()), Option::Some(TensorTrait::<u32>::new(shape: array![3].span(), data: array![0, 0, 0].span(),)))", name)
+            
+        
+        def split_1d_uneven():
+            x = np.random.randint(0, 255, 7).astype(np.uint32)
+            y = [
+                np.array(x[0:2]).astype(np.uint32), 
+                np.array(x[2:4]).astype(np.uint32),
+                np.array(x[4:6]).astype(np.uint32),
+                np.array(x[6:7]).astype(np.uint32),
+            ]
+            
+            _x = Tensor(Dtype.U32, x.shape, x.flatten())
+            _y = [
+                Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
+                Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
+                Tensor(Dtype.U32, y[2].shape, y[2].flatten()),
+                Tensor(Dtype.U32, y[3].shape, y[3].flatten()),
+            ]
+
+            name = "split_u32_1d_uneven"
+            make_test(
+                [_x], _y, "input_0.split(0, Option::Some(4), Option::None(()))", name)
+            
+
+        def split_2d_uneven():
+            x = np.random.randint(0, 255, (2, 8)).astype(np.uint32)
+            y = [
+                np.array(x[0:2, 0:3]).astype(np.uint32), 
+                np.array(x[0:2, 3:6]).astype(np.uint32), 
+                np.array(x[0:2, 6:8]).astype(np.uint32)
+            ]
+            _x = Tensor(Dtype.U32, x.shape, x.flatten())
+            _y = [
+                Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
+                Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
+                Tensor(Dtype.U32, y[2].shape, y[2].flatten()),
+            ]
+
+            name = "split_u32_2d_uneven"
+            make_test(
+                [_x], _y, "input_0.split(1, Option::Some(3), Option::None(()))", name)
+
+        
+        split_1D()
+        split_2D()
+        split_zero_size()
+        split_1d_uneven()
+        split_2d_uneven()
+
+    @staticmethod
+    def split_fp16x16():
+        def split_1D():
+            x = to_fp(np.random.randint(-127, 127, 6
+                                        ).astype(np.int64), FixedImpl.FP16x16)
+            y = [
+                np.array(x[0:2]).astype(np.int64), 
+                np.array(x[2:4]).astype(np.int64),
+                np.array(x[4:6]).astype(np.int64),
+            ]
+
+            _x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
+            _y = [
+                Tensor(Dtype.FP16x16, y[0].shape, y[0].flatten()),
+                Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
+                Tensor(Dtype.FP16x16, y[2].shape, y[2].flatten()),
+            ]
+
+            name = "split_fp16x16_1d_equal_parts"
+            make_test(
+                [_x], _y, "input_0.split(0, Option::Some(3), Option::None(()))", name)
+            y = [
+                np.array(x[0:2]).astype(np.int64), 
+                np.array(x[2:6]).astype(np.int64),
+            ]
+            _y = [
+                Tensor(Dtype.FP16x16, y[0].shape, y[0].flatten()),
+                Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
+            ]
+            name = "split_fp16x16_1d_variable_parts"
+            make_test(
+                [_x], _y, "input_0.split(0, Option::None(()), Option::Some(TensorTrait::<u32>::new(shape: array![2].span(), data: array![2, 4].span(),)))", name)
+        def split_2D():
+            x = to_fp(np.random.randint(-127, 127, (2, 6)
+                                        ).astype(np.int64), FixedImpl.FP16x16)
+            y = [
+                np.array(x[0:2, 0:3]).astype(np.int64),
+                np.array(x[0:2, 3:6]).astype(np.int64),
+            ]
+            _x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
+            _y = [
+                Tensor(Dtype.FP16x16, y[0].shape, y[0].flatten()),
+                Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
+            ]
+            name = "split_fp16x16_2d_equal_parts"
+            make_test(
+                [_x], _y, "input_0.split(1, Option::Some(2), Option::None(()))", name)
+
+            y = [
+                np.array(x[0:2, 0:2]).astype(np.int64), 
+                np.array(x[0:2, 2:6]).astype(np.int64)
+            ]
+            _y = [
+                Tensor(Dtype.FP16x16, y[0].shape, y[0].flatten()),
+                Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
+            ]
+            name = "split_fp16x16_2d_variable_parts"
+            make_test(
+                [_x], _y, "input_0.split(1, Option::None(()), Option::Some(TensorTrait::<u32>::new(shape: array![2].span(), data: array![2, 4].span(),)))", name)
+        
+        def split_zero_size():
+            # 1-dimensional tensor with dimension_size=0
+            x = to_fp(np.array([]).astype(np.int64
+                                        ).astype(np.int64), FixedImpl.FP16x16)
+            y = [
+                np.array([]).astype(np.int64),
+                np.array([]).astype(np.int64),
+                np.array([]).astype(np.int64),
+            ]
+            _x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
+            _y = [
+                Tensor(Dtype.FP16x16, y[0].shape, y[0].flatten()),
+                Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
+                Tensor(Dtype.FP16x16, y[2].shape, y[2].flatten()),
+            ]
+            # Split emtpy tensor to tensors of size zero
+            name = "split_fp16x16_zero_size"
+            make_test(
+                [_x], _y, "input_0.split(0, Option::None(()), Option::Some(TensorTrait::<u32>::new(shape: array![3].span(), data: array![0, 0, 0].span(),)))", name)
+            
+        
+        def split_1d_uneven():
+            x = to_fp(np.random.randint(-127, 127, 7
+                                        ).astype(np.int64), FixedImpl.FP16x16)
+            y = [
+                np.array(x[0:2]).astype(np.int64), 
+                np.array(x[2:4]).astype(np.int64),
+                np.array(x[4:6]).astype(np.int64),
+                np.array(x[6:7]).astype(np.int64),
+            ]
+
+            _x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
+            _y = [
+                Tensor(Dtype.FP16x16, y[0].shape, y[0].flatten()),
+                Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
+                Tensor(Dtype.FP16x16, y[2].shape, y[2].flatten()),
+                Tensor(Dtype.FP16x16, y[3].shape, y[3].flatten()),
+            ]
+
+            name = "split_fp16x16_1d_uneven"
+            make_test(
+                [_x], _y, "input_0.split(0, Option::Some(4), Option::None(()))", name)
+            
+
+        def split_2d_uneven():
+            x = to_fp(np.random.randint(-127, 127, (2, 8)
+                                        ).astype(np.int64), FixedImpl.FP16x16)
+            y = [
+                np.array(x[0:2, 0:3]).astype(np.int64), 
+                np.array(x[0:2, 3:6]).astype(np.int64), 
+                np.array(x[0:2, 6:8]).astype(np.int64)
+            ]
+            _x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
+            _y = [
+                Tensor(Dtype.FP16x16, y[0].shape, y[0].flatten()),
+                Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
+                Tensor(Dtype.FP16x16, y[2].shape, y[2].flatten()),
+            ]
+
+            name = "split_fp16x16_2d_uneven"
+            make_test(
+                [_x], _y, "input_0.split(1, Option::Some(3), Option::None(()))", name)
+
+        
+        split_1D()
+        split_2D()
+        split_zero_size()
+        split_1d_uneven()
+        split_2d_uneven()
+    
\ No newline at end of file
diff --git a/src/numbers/complex_number/complex64.cairo b/src/numbers/complex_number/complex64.cairo
index fe7b70581..c3b649c6d 100644
--- a/src/numbers/complex_number/complex64.cairo
+++ b/src/numbers/complex_number/complex64.cairo
@@ -73,7 +73,12 @@ impl Complex64Impl of ComplexTrait<complex64, FP64x64> {
         let y = self.img;
         let two = FP64x64Impl::new(TWO, false);
         let real = (((x.pow(two) + y.pow(two)).sqrt() + x) / two).sqrt();
-        let img = (((x.pow(two) + y.pow(two)).sqrt() - x) / two).sqrt();
+        let img = if y == FP64x64Impl::ZERO() {
+            FP64x64Impl::ZERO()
+        } else {
+            (((x.pow(two) + y.pow(two)).sqrt() - x) / two).sqrt()
+        };
+
         let img = FP64x64Impl::new(img.mag, y.sign);
         complex64 { real, img }
     }
diff --git a/src/operators.cairo b/src/operators.cairo
index 1ca6cdee2..f125386a2 100644
--- a/src/operators.cairo
+++ b/src/operators.cairo
@@ -3,3 +3,4 @@ mod nn;
 mod ml;
 mod matrix;
 mod vec;
+mod sequence;
diff --git a/src/operators/ml.cairo b/src/operators/ml.cairo
index 4bfd10060..724664216 100644
--- a/src/operators/ml.cairo
+++ b/src/operators/ml.cairo
@@ -1,5 +1,6 @@
 mod tree_ensemble;
 mod linear;
+mod svm;
 
 use orion::operators::ml::tree_ensemble::core::{
     TreeEnsemble, TreeEnsembleAttributes, TreeEnsembleImpl, NODE_MODES
@@ -11,4 +12,11 @@ use orion::operators::ml::tree_ensemble::tree_ensemble_classifier::{
 use orion::operators::ml::tree_ensemble::tree_ensemble_regressor::{
     TreeEnsembleRegressor, TreeEnsembleRegressorImpl, TreeEnsembleRegressorTrait, AGGREGATE_FUNCTION
 };
-use orion::operators::ml::linear::linear_regressor::{LinearRegressorTrait, LinearRegressorImpl, LinearRegressor};
+
+use orion::operators::ml::linear::linear_regressor::{
+    LinearRegressorTrait, LinearRegressorImpl, LinearRegressor
+};
+
+use orion::operators::ml::linear::linear_classifier::{
+    LinearClassifierTrait, LinearClassifierImpl, LinearClassifier
+};
diff --git a/src/operators/ml/linear.cairo b/src/operators/ml/linear.cairo
index bc8062bd1..ef33bae6b 100644
--- a/src/operators/ml/linear.cairo
+++ b/src/operators/ml/linear.cairo
@@ -1 +1,2 @@
 mod linear_regressor;
+mod linear_classifier;
diff --git a/src/operators/ml/linear/linear_classifier.cairo b/src/operators/ml/linear/linear_classifier.cairo
new file mode 100644
index 000000000..fad7ea2d4
--- /dev/null
+++ b/src/operators/ml/linear/linear_classifier.cairo
@@ -0,0 +1,284 @@
+use core::array::ArrayTrait;
+use core::array::SpanTrait;
+use orion::numbers::FP16x16;
+
+use orion::operators::tensor::{Tensor, TensorTrait};
+use orion::numbers::NumberTrait;
+use orion::operators::tensor::{I8Tensor, I32Tensor, U32Tensor, FP16x16Tensor, FP16x16TensorAdd};
+use orion::numbers::{FP32x32, FP32x32Impl, FixedTrait};
+use orion::operators::nn::{NNTrait, FP16x16NN};
+
+
+#[derive(Destruct)]
+struct LinearClassifier<T> {
+    classlabels: Option<Span<usize>>,
+    coefficients: Span<T>,
+    intercepts: Option<Span<T>>,
+    multi_class: usize,
+    post_transform: POST_TRANSFORM,
+}
+
+
+#[derive(Copy, Drop)]
+enum POST_TRANSFORM {
+    NONE,
+    SOFTMAX,
+    LOGISTIC,
+    SOFTMAXZERO,
+    PROBIT,
+}
+
+/// Trait
+///
+/// predict - Performs the linear classification.
+trait LinearClassifierTrait<T> {
+    /// # LinearClassifierTrait::predict
+    ///
+    /// ```rust 
+    ///    fn predict(ref self: LinearClassifier<T>, X: Tensor<T>) -> Tensor<T>;
+    /// ```
+    ///
+    /// Linear Classifier. Performs the linear classification.
+    /// 
+    /// ## Args
+    ///
+    /// * `self`: LinearClassifier<T> - A LinearClassifier object.
+    /// * `X`:  Input 2D tensor.
+    ///
+    /// ## Returns
+    ///
+    /// * Tensor<T> containing the linear classification evaluation of the input X.
+    ///
+    /// ## Type Constraints
+    ///
+    /// `LinearClassifier` and `X` must be fixed points
+    ///
+    /// ## Examples
+    ///
+    /// ```rust
+    /// use orion::numbers::FP16x16;
+    /// use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor, U32Tensor};
+    /// 
+    /// use orion::operators::ml::linear::linear_classifier::{
+    ///     LinearClassifierTrait, POST_TRANSFORM, LinearClassifier
+    /// };
+    /// 
+    /// fn linear_classifier_helper(
+    ///     post_transform: POST_TRANSFORM
+    /// ) -> (LinearClassifier<FP16x16>, Tensor<FP16x16>) {
+    /// 
+    ///     let classlabels: Span<usize> = array![0, 1, 2].span();
+    ///     let classlabels = Option::Some(classlabels);
+    /// 
+    ///     let classlabels_strings: Option<Span<FP16x16>> = Option::None;
+    /// 
+    ///     let coefficients: Span<FP16x16> = array![
+    ///         FP16x16 { mag: 38011, sign: true },
+    ///         FP16x16 { mag: 19005, sign: true },
+    ///         FP16x16 { mag: 5898, sign: true },
+    ///         FP16x16 { mag: 38011, sign: false },
+    ///         FP16x16 { mag: 19005, sign: false },
+    ///         FP16x16 { mag: 5898, sign: false }, 
+    ///     ]
+    ///         .span();
+    /// 
+    ///     let intercepts: Span<FP16x16> = array![
+    ///         FP16x16 { mag: 176947, sign: false },
+    ///         FP16x16 { mag: 176947, sign: true },
+    ///         FP16x16 { mag: 32768, sign: false },
+    ///     ]
+    ///         .span();
+    ///     let intercepts = Option::Some(intercepts);
+    /// 
+    ///     let multi_class: usize = 0;
+    /// 
+    ///     let mut classifier: LinearClassifier<FP16x16> = LinearClassifier {
+    ///         classlabels,
+    ///         coefficients,
+    ///         intercepts,
+    ///         multi_class,
+    ///         post_transform
+    ///         };
+    /// 
+    ///     let mut X: Tensor<FP16x16> = TensorTrait::new(
+    ///         array![3, 2].span(),
+    ///         array![
+    ///             FP16x16 { mag: 0, sign: false },
+    ///             FP16x16 { mag: 65536, sign: false },
+    ///             FP16x16 { mag: 131072, sign: false },
+    ///             FP16x16 { mag: 196608, sign: false },
+    ///             FP16x16 { mag: 262144, sign: false },
+    ///             FP16x16 { mag: 327680, sign: false },
+    ///         ]
+    ///             .span()
+    ///     );
+    /// 
+    ///     (classifier, X)
+    /// }
+    /// 
+    /// fn linear_classifier_multi_softmax() -> (Span<usize>, Tensor<FP16x16>) {
+    ///     let (mut classifier, X) = linear_classifier_helper(POST_TRANSFORM::SOFTMAX);
+    /// 
+    ///     let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+    /// 
+    ///     (labels, scores)
+    /// }
+    /// 
+    /// >>> 
+    /// ([0, 2, 2],
+    ///  [
+    ///     [0.852656, 0.009192, 0.138152],
+    ///     [0.318722, 0.05216, 0.629118],
+    ///     [0.036323, 0.090237, 0.87344]
+    ///  ])
+    /// ```
+    fn predict(ref self: LinearClassifier<T>, X: Tensor<T>) -> (Span<usize>, Tensor<T>);
+}
+
+impl LinearClassifierImpl<
+    T,
+    MAG,
+    +Drop<T>,
+    +Copy<T>,
+    +NumberTrait<T, MAG>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +Add<T>,
+    +TensorTrait<usize>,
+    +TensorTrait<T>,
+    +AddEq<T>,
+    +Div<T>,
+    +Mul<T>,
+    +Add<Tensor<T>>,
+    +NNTrait<T>
+> of LinearClassifierTrait<T> {
+    fn predict(ref self: LinearClassifier<T>, X: Tensor<T>) -> (Span<usize>, Tensor<T>) {
+        let n: usize = self.coefficients.len() / *(X.shape).at(1);
+        let mut shape = ArrayTrait::<usize>::new();
+        shape.append(n);
+        shape.append(*(X.shape).at(1));
+
+        let mut coefficients = TensorTrait::new(shape.span(), self.coefficients);
+        let coefficients = coefficients.transpose(array![1, 0].span());
+
+        let mut scores = X.matmul(@coefficients);
+        match self.intercepts {
+            Option::Some(intercepts) => {
+                let mut shape = ArrayTrait::<usize>::new();
+                shape.append(1);
+                shape.append(intercepts.len());
+                let intercepts = TensorTrait::new(shape.span(), intercepts);
+                scores = TensorTrait::add(scores, intercepts);
+            },
+            Option::None(_) => {},
+        };
+
+        let (n_classes, classlabels) = match self.classlabels {
+            Option::Some(classlabels) => { (classlabels.len(), classlabels) },
+            Option::None(_) => { (0, ArrayTrait::<usize>::new().span()) },
+        };
+        if *coefficients.shape.at(1) == 1 && n_classes == 2 {
+            let mut new_scores = ArrayTrait::new();
+
+            loop {
+                match scores.data.pop_front() {
+                    Option::Some(item) => {
+                        new_scores.append(NumberTrait::neg(*item));
+                        new_scores.append(*item);
+                    },
+                    Option::None(_) => { break; },
+                }
+            };
+            scores = TensorTrait::new(array![*scores.shape.at(0), 2].span(), new_scores.span());
+        }
+        // Post Transform
+        scores = match self.post_transform {
+            POST_TRANSFORM::NONE => { scores },
+            POST_TRANSFORM::SOFTMAX => { NNTrait::softmax(@scores, 1) },
+            POST_TRANSFORM::LOGISTIC => { NNTrait::sigmoid(@scores) },
+            POST_TRANSFORM::SOFTMAXZERO => { NNTrait::softmax_zero(@scores, 1) },
+            POST_TRANSFORM::PROBIT => core::panic_with_felt252('Probit not supported yet'),
+        };
+
+        // Labels
+        let mut labels_list = ArrayTrait::new();
+        if *scores.shape.at(1) > 1 {
+            let mut labels = scores.argmax(1, Option::None, Option::None);
+            loop {
+                match labels.data.pop_front() {
+                    Option::Some(i) => { labels_list.append(*classlabels[*i]); },
+                    Option::None(_) => { break; }
+                };
+            };
+        } else {
+            let mut i = 0;
+            match self.post_transform {
+                POST_TRANSFORM::NONE => {
+                    loop {
+                        if i == scores.data.len() {
+                            break;
+                        }
+                        if *scores.data.at(i) >= NumberTrait::zero() {
+                            labels_list.append(*classlabels[0]);
+                        } else {
+                            labels_list.append(0);
+                        }
+                        i += 1;
+                    };
+                },
+                POST_TRANSFORM::SOFTMAX => {
+                    loop {
+                        if i == scores.data.len() {
+                            break;
+                        }
+                        if *scores.data.at(i) >= NumberTrait::half() {
+                            labels_list.append(*classlabels[0]);
+                        } else {
+                            labels_list.append(0);
+                        }
+                        i += 1;
+                    };
+                },
+                POST_TRANSFORM::LOGISTIC => {
+                    loop {
+                        if i == scores.data.len() {
+                            break;
+                        }
+                        if *scores.data.at(i) >= NumberTrait::half() {
+                            labels_list.append(*classlabels[0]);
+                        } else {
+                            labels_list.append(0);
+                        }
+                        i += 1;
+                    };
+                },
+                POST_TRANSFORM::SOFTMAXZERO => {
+                    loop {
+                        if i == scores.data.len() {
+                            break;
+                        }
+                        if *scores.data.at(i) >= NumberTrait::half() {
+                            labels_list.append(*classlabels[0]);
+                        } else {
+                            labels_list.append(0);
+                        }
+                        i += 1;
+                    };
+                },
+                POST_TRANSFORM::PROBIT => core::panic_with_felt252('Probit not supported yet'),
+            };
+        }
+
+        (labels_list.span(), scores)
+    }
+}
+
+
+fn max(a: usize, b: usize) -> usize {
+    if a > b {
+        return a;
+    } else {
+        return b;
+    }
+}
+
diff --git a/src/operators/ml/linear/linear_regressor.cairo b/src/operators/ml/linear/linear_regressor.cairo
index c9374a4cd..85aec9560 100644
--- a/src/operators/ml/linear/linear_regressor.cairo
+++ b/src/operators/ml/linear/linear_regressor.cairo
@@ -215,9 +215,7 @@ impl LinearRegressorImpl<
             POST_TRANSFORM::NONE => score, // No action required
             POST_TRANSFORM::SOFTMAX => NNTrait::softmax(@score, 1),
             POST_TRANSFORM::LOGISTIC => NNTrait::sigmoid(@score),
-            POST_TRANSFORM::SOFTMAXZERO => core::panic_with_felt252(
-                'Softmax_zero not supported yet'
-            ),
+            POST_TRANSFORM::SOFTMAXZERO => NNTrait::softmax_zero(@score, 1),
             POST_TRANSFORM::PROBIT => core::panic_with_felt252('Probit not supported yet'),
         };
 
diff --git a/src/operators/ml/svm.cairo b/src/operators/ml/svm.cairo
new file mode 100644
index 000000000..93ab4515e
--- /dev/null
+++ b/src/operators/ml/svm.cairo
@@ -0,0 +1,2 @@
+mod core;
+mod svm_regressor;
diff --git a/src/operators/ml/svm/core.cairo b/src/operators/ml/svm/core.cairo
new file mode 100644
index 000000000..156fea8ee
--- /dev/null
+++ b/src/operators/ml/svm/core.cairo
@@ -0,0 +1,100 @@
+use core::traits::TryInto;
+use core::array::ArrayTrait;
+use core::array::SpanTrait;
+use core::traits::Into;
+use orion::numbers::NumberTrait;
+use orion::operators::tensor::{
+    TensorTrait, Tensor, I8Tensor, I32Tensor, U32Tensor, FP16x16Tensor, BoolTensor
+};
+use orion::numbers::{FP16x16, FP16x16Impl, FP32x32, FP32x32Impl, FixedTrait};
+use core::debug::PrintTrait;
+use orion::utils::get_row;
+
+#[derive(Copy, Drop)]
+enum KERNEL_TYPE {
+    LINEAR,
+    POLY,
+    RBF,
+    SIGMOID,
+}
+
+
+fn kernel_dot<
+    T,
+    MAG,
+    +Drop<T>,
+    +Copy<T>,
+    +NumberTrait<T, MAG>,
+    +Add<T>,
+    +TensorTrait<T>,
+    +AddEq<T>,
+    +Mul<T>,
+    +Neg<T>,
+    +Sub<T>,
+>(
+    kernel_params: Span<T>, pA: Span<T>, pB: Span<T>, kernel: KERNEL_TYPE
+) -> T {
+    let s = match kernel {
+        KERNEL_TYPE::LINEAR => sv_dot(pA, pB),
+        KERNEL_TYPE::POLY => {
+            let mut s = sv_dot(pA, pB);
+            s = s * *kernel_params.at(0) + *kernel_params.at(1);
+            s.pow(*kernel_params.at(2))
+        },
+        KERNEL_TYPE::RBF => {
+            let mut s = squared_diff(pA, pB);
+            NumberTrait::exp(-*kernel_params.at(0) * s)
+        },
+        KERNEL_TYPE::SIGMOID => {
+            let mut s = sv_dot(pA, pB);
+            s = s * *kernel_params.at(0) + *kernel_params.at(1);
+            NumberTrait::tanh(s)
+        },
+    };
+    return s;
+}
+
+
+fn sv_dot<
+    T, MAG, +Drop<T>, +Copy<T>, +NumberTrait<T, MAG>, +Add<T>, +TensorTrait<T>, +AddEq<T>, +Mul<T>,
+>(
+    pA: Span<T>, pB: Span<T>
+) -> T {
+    let mut i = 0;
+    let mut sum = NumberTrait::zero();
+    loop {
+        if i == pA.len() {
+            break;
+        }
+        sum = sum + *pA.at(i) * *pB.at(i);
+        i += 1;
+    };
+
+    return sum;
+}
+
+fn squared_diff<
+    T,
+    MAG,
+    +Drop<T>,
+    +Copy<T>,
+    +NumberTrait<T, MAG>,
+    +Add<T>,
+    +TensorTrait<T>,
+    +AddEq<T>,
+    +Mul<T>,
+    +Sub<T>,
+>(
+    pA: Span<T>, pB: Span<T>
+) -> T {
+    let mut i = 0;
+    let mut sum = NumberTrait::zero();
+    loop {
+        if i == pA.len() {
+            break;
+        }
+        sum = sum + (*pA.at(i) - *pB.at(i)).pow(NumberTrait::one() + NumberTrait::one());
+        i += 1;
+    };
+    return sum;
+}
diff --git a/src/operators/ml/svm/svm_regressor.cairo b/src/operators/ml/svm/svm_regressor.cairo
new file mode 100644
index 000000000..be76931e9
--- /dev/null
+++ b/src/operators/ml/svm/svm_regressor.cairo
@@ -0,0 +1,252 @@
+use core::traits::TryInto;
+use core::array::ArrayTrait;
+use core::array::SpanTrait;
+use core::traits::Into;
+use orion::numbers::NumberTrait;
+use orion::operators::tensor::{
+    TensorTrait, Tensor, I8Tensor, I32Tensor, U32Tensor, FP16x16Tensor, BoolTensor
+};
+use orion::numbers::{FP16x16, FP16x16Impl, FP32x32, FP32x32Impl, FixedTrait};
+use core::debug::PrintTrait;
+use orion::operators::nn::{NNTrait, FP16x16NN};
+use orion::utils::get_row;
+
+use orion::operators::ml::svm::core::{kernel_dot, KERNEL_TYPE};
+
+#[derive(Copy, Drop, Destruct)]
+struct SVMRegressor<T> {
+    coefficients: Span<T>,
+    kernel_params: Span<T>,
+    kernel_type: KERNEL_TYPE,
+    n_supports: usize,
+    one_class: usize,
+    post_transform: POST_TRANSFORM,
+    rho: Span<T>,
+    support_vectors: Span<T>,
+}
+
+#[derive(Copy, Drop)]
+enum POST_TRANSFORM {
+    NONE,
+    SOFTMAX,
+    LOGISTIC,
+    SOFTMAXZERO,
+    PROBIT,
+}
+
+#[derive(Copy, Drop)]
+enum MODE {
+    SVM_LINEAR,
+    SVM_SVC,
+}
+
+/// Trait
+///
+/// predict - Returns the regressed values for each input in N.
+trait SVMRegressorTrait<T> {
+    /// # SVMRegressorTrait::predict
+    ///
+    /// ```rust 
+    ///    fn predict(ref self: SVMRegressor<T>, X: Tensor<T>) -> Tensor<T>;
+    /// ```
+    ///
+    /// Support Vector Machine regression prediction and one-class SVM anomaly detection.
+    /// 
+    /// ## Args
+    ///
+    /// * `self`: SVMRegressor<T> - A SVMRegressor object.
+    /// * `X`:  Input 2D tensor.
+    ///
+    /// ## Returns
+    ///
+    /// * Tensor<T> containing the Support Vector Machine regression prediction and one-class SVM anomaly detection of the input X.
+    ///
+    /// ## Type Constraints
+    ///
+    /// `SVMRegressor` and `X` must be fixed points
+    ///
+    /// ## Examples
+    /// 
+    /// ```rust
+    /// use orion::numbers::FP16x16;
+    /// use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor, U32Tensor};
+    /// use orion::operators::tensor::FP16x16TensorPartialEq;
+    /// 
+    /// use orion::operators::ml::svm::svm_regressor::{SVMRegressorTrait, POST_TRANSFORM, SVMRegressor};
+    /// use orion::operators::ml::svm::core::{KERNEL_TYPE};
+    /// 
+    /// fn example_svm_regressor_linear() -> Tensor<FP16x16> {
+    ///     let coefficients: Span<FP16x16> = array![
+    ///         FP16x16 { mag: 65536, sign: false },
+    ///         FP16x16 { mag: 65536, sign: true },
+    ///         FP16x16 { mag: 54959, sign: false },
+    ///         FP16x16 { mag: 54959, sign: true },
+    ///         FP16x16 { mag: 29299, sign: false },
+    ///         FP16x16 { mag: 65536, sign: true },
+    ///         FP16x16 { mag: 36236, sign: false }
+    ///     ]
+    ///         .span();
+    ///     let n_supports: usize = 7;
+    ///     let one_class: usize = 0;
+    ///     let rho: Span<FP16x16> = array![FP16x16 { mag: 35788, sign: false }].span();
+    ///     let support_vectors: Span<FP16x16> = array![
+    ///         FP16x16 { mag: 8421, sign: true },
+    ///         FP16x16 { mag: 5842, sign: false },
+    ///         FP16x16 { mag: 4510, sign: false },
+    ///         FP16x16 { mag: 5202, sign: true },
+    ///         FP16x16 { mag: 14783, sign: true },
+    ///         FP16x16 { mag: 17380, sign: true },
+    ///         FP16x16 { mag: 60595, sign: false },
+    ///         FP16x16 { mag: 1674, sign: true },
+    ///         FP16x16 { mag: 38669, sign: true },
+    ///         FP16x16 { mag: 63803, sign: false },
+    ///         FP16x16 { mag: 87720, sign: true },
+    ///         FP16x16 { mag: 22236, sign: false },
+    ///         FP16x16 { mag: 61816, sign: false },
+    ///         FP16x16 { mag: 34267, sign: true },
+    ///         FP16x16 { mag: 36418, sign: false },
+    ///         FP16x16 { mag: 27471, sign: false },
+    ///         FP16x16 { mag: 28421, sign: false },
+    ///         FP16x16 { mag: 69270, sign: true },
+    ///         FP16x16 { mag: 152819, sign: false },
+    ///         FP16x16 { mag: 4065, sign: false },
+    ///         FP16x16 { mag: 62274, sign: true }
+    ///     ]
+    ///         .span();
+    ///     let post_transform = POST_TRANSFORM::NONE;
+    ///     let kernel_params: Span<FP16x16> = array![
+    ///         FP16x16 { mag: 27812, sign: false },
+    ///         FP16x16 { mag: 0, sign: false },
+    ///         FP16x16 { mag: 196608, sign: false }
+    ///     ]
+    ///         .span();
+    ///     let kernel_type = KERNEL_TYPE::LINEAR;
+    /// 
+    ///     let mut regressor: SVMRegressor<FP16x16> = SVMRegressor {
+    ///         coefficients,
+    ///         kernel_params,
+    ///         kernel_type,
+    ///         n_supports,
+    ///         one_class,
+    ///         post_transform,
+    ///         rho,
+    ///         support_vectors,
+    ///     };
+    /// 
+    ///     let mut X: Tensor<FP16x16> = TensorTrait::new(
+    ///         array![3, 3].span(),
+    ///         array![
+    ///             FP16x16 { mag: 32768, sign: true },
+    ///             FP16x16 { mag: 26214, sign: true },
+    ///             FP16x16 { mag: 19660, sign: true },
+    ///             FP16x16 { mag: 13107, sign: true },
+    ///             FP16x16 { mag: 6553, sign: true },
+    ///             FP16x16 { mag: 0, sign: false },
+    ///             FP16x16 { mag: 6553, sign: false },
+    ///             FP16x16 { mag: 13107, sign: false },
+    ///             FP16x16 { mag: 19660, sign: false },
+    ///         ]
+    ///             .span()
+    ///     );
+    /// 
+    ///     return SVMRegressorTrait::predict(ref regressor, X);
+    /// }
+    /// 
+    /// >>> [[-0.468206], [0.227487], [0.92318]]
+    /// ```
+    ///
+    ///
+    fn predict(ref self: SVMRegressor<T>, X: Tensor<T>) -> Tensor<T>;
+}
+
+impl SVMRegressorImpl<
+    T,
+    MAG,
+    +Drop<T>,
+    +Copy<T>,
+    +NumberTrait<T, MAG>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +Add<T>,
+    +TensorTrait<T>,
+    +PrintTrait<T>,
+    +AddEq<T>,
+    +Div<T>,
+    +Mul<T>,
+    +Neg<T>,
+    +Sub<T>,
+    +NNTrait<T>,
+> of SVMRegressorTrait<T> {
+    fn predict(ref self: SVMRegressor<T>, X: Tensor<T>) -> Tensor<T> {
+        let (mode_, kernel_type_, sv) = if self.n_supports > 0 {
+            let mode_ = MODE::SVM_SVC;
+            let kernel_type_ = self.kernel_type;
+            let sv = TensorTrait::new(
+                array![self.n_supports, self.support_vectors.len() / self.n_supports].span(),
+                self.support_vectors
+            ); //self.atts.support_vectors.reshape((self.atts.n_supports, -1))
+            (mode_, kernel_type_, sv)
+        } else {
+            let mode_ = MODE::SVM_LINEAR;
+            let kernel_type_ = KERNEL_TYPE::LINEAR;
+            let sv = TensorTrait::new(
+                array![self.support_vectors.len()].span(), self.support_vectors
+            );
+            (mode_, kernel_type_, sv)
+        };
+
+        let mut z = ArrayTrait::new();
+        let mut n = 0;
+        loop {
+            if n == *X.shape.at(0) {
+                break;
+            }
+            let mut s = NumberTrait::zero();
+            match mode_ {
+                MODE::SVM_LINEAR => {
+                    let mut x_n = get_row(@X, n);
+                    s = kernel_dot(self.kernel_params, x_n, self.coefficients, kernel_type_);
+                    s += *self.rho.at(0);
+                },
+                MODE::SVM_SVC => {
+                    let mut x_n = get_row(@X, n);
+                    let mut j = 0;
+                    loop {
+                        if j == self.n_supports {
+                            break;
+                        }
+                        let mut sv_j = get_row(@sv, j);
+                        let d = kernel_dot(self.kernel_params, x_n, sv_j, kernel_type_);
+                        s += *self.coefficients.at(j) * d;
+                        j += 1;
+                    };
+                    s += *self.rho.at(0);
+                },
+            }
+            if self.one_class == 1 {
+                let elem = if s > NumberTrait::zero() {
+                    NumberTrait::one()
+                } else {
+                    -NumberTrait::one()
+                };
+                z.append(elem);
+            } else {
+                z.append(s);
+            };
+            n += 1;
+        };
+
+        // Post Transform
+        let mut score = TensorTrait::new(array![*X.shape.at(0)].span(), z.span());
+        score = match self.post_transform {
+            POST_TRANSFORM::NONE => score,
+            POST_TRANSFORM::SOFTMAX => NNTrait::softmax(@score, 1),
+            POST_TRANSFORM::LOGISTIC => NNTrait::sigmoid(@score),
+            POST_TRANSFORM::SOFTMAXZERO => NNTrait::softmax_zero(@score, 1),
+            POST_TRANSFORM::PROBIT => core::panic_with_felt252('Probit not supported yet'),
+        };
+
+        return score;
+    }
+}
+
diff --git a/src/operators/ml/tree_ensemble/tree_ensemble_classifier.cairo b/src/operators/ml/tree_ensemble/tree_ensemble_classifier.cairo
index db83cb0aa..051965260 100644
--- a/src/operators/ml/tree_ensemble/tree_ensemble_classifier.cairo
+++ b/src/operators/ml/tree_ensemble/tree_ensemble_classifier.cairo
@@ -270,7 +270,6 @@ impl TreeEnsembleClassifierImpl<
     fn predict(ref self: TreeEnsembleClassifier<T>, X: Tensor<T>) -> (Span<usize>, MutMatrix::<T>) {
         let leaves_index = self.ensemble.leave_index_tree(X);
         let n_classes = self.classlabels.len();
-        assert(n_classes > 1, 'binary class not supported yet');
         let mut res: MutMatrix<T> = MutMatrixImpl::new(*leaves_index.shape.at(0), n_classes);
 
         // Set base values
@@ -402,6 +401,130 @@ impl TreeEnsembleClassifierImpl<
             i += 1;
         };
 
+        // Binary class
+        let mut binary = false;
+        let mut i: usize = 0;
+        let mut class_ids = self.class_ids;
+        let mut class_id: usize = 0;
+        // Get first class_id in class_ids
+        match class_ids.pop_front() {
+            Option::Some(c_id) => { let mut class_id = *c_id; },
+            Option::None(_) => { let mut class_id: usize = 0; }
+        };
+        loop {
+            if i == self.class_ids.len() {
+                break;
+            }
+            match class_ids.pop_front() {
+                Option::Some(c_id) => {
+                    if *c_id == class_id {
+                        binary = true;
+                        continue;
+                    } else {
+                        binary = false;
+                        break;
+                    }
+                },
+                Option::None(_) => { break; }
+            };
+        };
+
+        // Clone res
+        if binary {
+            let mut new_res: MutMatrix<T> = MutMatrixImpl::new(res.rows, res.cols);
+            let mut i: usize = 0;
+            loop {
+                if i == res.rows {
+                    break;
+                }
+                // Exchange
+                let res_ele_1 = match res.get(i, 0) {
+                    Option::Some(res_0) => { new_res.set(i, 1, res_0); },
+                    Option::None(_) => { new_res.set(i, 1, NumberTrait::zero()); },
+                };
+                i += 1;
+            };
+            match self.post_transform {
+                POST_TRANSFORM::NONE => {
+                    let mut i: usize = 0;
+                    loop {
+                        if i == res.rows {
+                            break;
+                        }
+                        // Exchange
+                        let res_ele_0 = match new_res.get(i, 1) {
+                            Option::Some(res_1) => {
+                                let value = NumberTrait::sub(NumberTrait::one(), res_1);
+                                new_res.set(i, 0, value);
+                            },
+                            Option::None(_) => { new_res.set(i, 0, NumberTrait::zero()); },
+                        };
+                        i += 1;
+                    };
+                },
+                POST_TRANSFORM::SOFTMAX => {
+                    let mut i: usize = 0;
+                    loop {
+                        if i == res.rows {
+                            break;
+                        }
+                        // Exchange
+                        let res_ele_0 = match new_res.get(i, 1) {
+                            Option::Some(res_1) => { new_res.set(i, 0, res_1.neg()); },
+                            Option::None(_) => { new_res.set(i, 0, NumberTrait::zero()); },
+                        };
+                        i += 1;
+                    };
+                },
+                POST_TRANSFORM::LOGISTIC => {
+                    let mut i: usize = 0;
+                    loop {
+                        if i == res.rows {
+                            break;
+                        }
+                        // Exchange
+                        let res_ele_0 = match new_res.get(i, 1) {
+                            Option::Some(res_1) => { new_res.set(i, 0, res_1.neg()); },
+                            Option::None(_) => { new_res.set(i, 0, NumberTrait::zero()); },
+                        };
+                        i += 1;
+                    };
+                },
+                POST_TRANSFORM::SOFTMAXZERO => {
+                    let mut i: usize = 0;
+                    loop {
+                        if i == res.rows {
+                            break;
+                        }
+                        // Exchange
+                        let res_ele_0 = match new_res.get(i, 1) {
+                            Option::Some(res_1) => { new_res.set(i, 0, res_1.neg()); },
+                            Option::None(_) => { new_res.set(i, 0, NumberTrait::zero()); },
+                        };
+                        i += 1;
+                    };
+                },
+                POST_TRANSFORM::PROBIT => {
+                    let mut i: usize = 0;
+                    loop {
+                        if i == res.rows {
+                            break;
+                        }
+                        // Exchange
+                        let res_ele_0 = match new_res.get(i, 1) {
+                            Option::Some(res_1) => {
+                                let value = NumberTrait::sub(NumberTrait::one(), res_1);
+                                new_res.set(i, 0, value);
+                            },
+                            Option::None(_) => { new_res.set(i, 0, NumberTrait::zero()); },
+                        };
+                        i += 1;
+                    };
+                },
+            };
+            res = new_res;
+        }
+
         // Post Transform
         let mut new_scores = match self.post_transform {
             POST_TRANSFORM::NONE => res, // No action required
diff --git a/src/operators/ml/tree_ensemble/tree_ensemble_regressor.cairo b/src/operators/ml/tree_ensemble/tree_ensemble_regressor.cairo
index 7c2efcb97..9848efd9e 100644
--- a/src/operators/ml/tree_ensemble/tree_ensemble_regressor.cairo
+++ b/src/operators/ml/tree_ensemble/tree_ensemble_regressor.cairo
@@ -434,7 +434,6 @@ fn compute_res_AVERAGE<
 ) {
     let n_trees_felt: felt252 = (n_trees * 65536).into();
     let n_trees: T = NumberTrait::from_felt(n_trees_felt);
-    n_trees_felt.print();
     loop {
         match t_index.pop_front() {
             Option::Some(its) => {
diff --git a/src/operators/nn/core.cairo b/src/operators/nn/core.cairo
index ae794c3a1..7594ba908 100644
--- a/src/operators/nn/core.cairo
+++ b/src/operators/nn/core.cairo
@@ -6,6 +6,7 @@ use orion::operators::tensor::core::Tensor;
 /// leaky_relu - Applies the leaky rectified linear unit (Leaky ReLU) activation function element-wise.
 /// sigmoid - Applies the Sigmoid function to an n-dimensional input tensor.
 /// softmax - Computes softmax activations.
+/// softmax_zero - Computes softmax zero.
 /// logsoftmax - Applies the natural log to Softmax function to an n-dimensional input Tensor.
 /// softsign - Applies the Softsign function element-wise.
 /// softplus - Applies the Softplus function element-wise.
@@ -115,6 +116,72 @@ trait NNTrait<T> {
     /// ```
     ///
     fn softmax(tensor: @Tensor<T>, axis: usize) -> Tensor<T>;
+    /// # NNTrait::softmax_zero
+    ///
+    /// ```rust 
+    ///    fn softmax_zero(tensor: @Tensor<T>, axis: usize) -> Tensor<T>;
+    /// ```
+    ///
+    /// Applies the Softmax zero function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range \[0,1] and sum to 1 while keeping the zero elements to zero.
+    /// 
+    /// The softmax zero on the set $\mathbf{x} = (x_1, ..., x_n)$ is given by :
+    /// 
+    /// $$
+    /// \text{softmax zero}(x_i) = \begin{cases}
+    /// 0 & \qquad x_i = 0 \\
+    /// \frac{e^{x_i}}{ \sum_{x \in {S}} e^{x}} & \qquad \text{otherwise} 
+    /// \end{cases}
+    /// $$
+    /// where $S$ in a subset of $\mathbf{x}$ given by 
+    /// 
+    /// $$
+    ///  \  S = \{ (x_1, \ldots, x_k) \mid 1 \leq k \leq n, x_j \neq 0 \text{ for } 1 \leq j \leq k \}
+    /// $$
+    ///
+    /// ## Args
+    ///
+    /// * `tensor`(`@Tensor<T>`) - The input tensor.
+    /// * `axis`(`usize`) - The axis along which to compute the softmax zero.
+    ///
+    /// ## Returns
+    ///
+    /// A Tensor of fixed point numbers with the same shape than the input Tensor.
+    ///
+    /// ## Type Constraints
+    ///
+    /// Constrain input and output types to fixed point tensors.
+    ///
+    /// ## Examples
+    /// 
+    /// ```rust
+    /// use core::array::{ArrayTrait, SpanTrait};
+    /// 
+    /// use orion::operators::tensor::{TensorTrait, Tensor, FP8x23Tensor};
+    /// use orion::operators::nn::{NNTrait, FP8x23NN};
+    /// use orion::numbers::{FP8x23, FixedTrait};
+    /// 
+    /// use core::debug::PrintTrait;
+    /// 
+    /// fn softmax_zero_example() -> Tensor<FP8x23> {
+    ///     let tensor = TensorTrait::<FP8x23>::new(
+    ///         shape: array![2, 2].span(),
+    ///         data: array![
+    ///             FixedTrait::new(0, false),
+    ///             FixedTrait::new(8388608, false),
+    ///             FixedTrait::new(16777216, false),
+    ///             FixedTrait::new(25165824, false),
+    ///         ]
+    ///             .span(),
+    ///     );
+    /// 
+    ///     return NNTrait::softmax_zero(@tensor, 1);
+    /// }
+    /// >>> [[0,0x800000],[2256043,6132564]]
+    ///     // The fixed point representation of
+    ///     // [[0, 1],[0.2689, 0.7311]]
+    /// ```
+    ///
+    fn softmax_zero(tensor: @Tensor<T>, axis: usize) -> Tensor<T>;
     /// # NNTrait::logsoftmax
     ///
     /// ```rust 
diff --git a/src/operators/nn/functional.cairo b/src/operators/nn/functional.cairo
index 6e40e562f..a0fd96cc8 100644
--- a/src/operators/nn/functional.cairo
+++ b/src/operators/nn/functional.cairo
@@ -2,6 +2,7 @@ mod relu;
 mod leaky_relu;
 mod sigmoid;
 mod softmax;
+mod softmax_zero;
 mod softsign;
 mod softplus;
 mod linear;
diff --git a/src/operators/nn/functional/softmax_zero.cairo b/src/operators/nn/functional/softmax_zero.cairo
new file mode 100644
index 000000000..c0aa13dac
--- /dev/null
+++ b/src/operators/nn/functional/softmax_zero.cairo
@@ -0,0 +1,199 @@
+use core::traits::Into;
+use core::option::OptionTrait;
+
+use orion::numbers::fixed_point::core::FixedTrait;
+use orion::numbers::NumberTrait;
+
+use orion::operators::tensor::core::{Tensor, TensorTrait, ravel_index, unravel_index};
+use orion::operators::tensor::helpers::{reduce_output_shape, len_from_shape, combine_indices};
+use orion::operators::tensor::math::{reduce_sum::accumulate_sum, arithmetic::div_downcast};
+
+
+/// Cf: NNTrait::softmax_zero docstring
+fn softmax_zero<
+    T,
+    MAG,
+    impl TTensor: TensorTrait<T>,
+    impl TTensorDiv: Div<Tensor<T>>,
+    impl TPartialEq: PartialEq<T>,
+    impl TNumber: NumberTrait<T, MAG>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
+    impl TAddEq: AddEq<T>,
+>(
+    z: @Tensor<T>, axis: usize
+) -> Tensor<T> {
+    let exp_tensor = exp_zero(*z);
+    let sum_no_zero = reduce_sum_no_zero(@exp_tensor, axis, true);
+    exp_tensor / sum_no_zero
+}
+
+/// Cf: NNTrait::softmax_zero docstring
+fn softmaxWide_zero<
+    T,
+    TMAG,
+    W,
+    WMAG,
+    impl TTensor: TensorTrait<T>,
+    impl WTensor: TensorTrait<W>,
+    impl TDiv: Div<T>,
+    impl TIntoW: Into<T, W>,
+    impl WTryIntoT: TryInto<W, T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
+    impl WCopy: Copy<W>,
+    impl WDrop: Drop<W>,
+    impl TNumber: NumberTrait<T, TMAG>,
+    impl WNumber: NumberTrait<W, WMAG>,
+    impl TPartialEq: PartialEq<T>,
+    impl WPartialEq: PartialEq<W>,
+    impl TAddEq: AddEq<T>,
+    impl WAddEq: AddEq<W>,
+>(
+    z: @Tensor<T>, axis: usize
+) -> Tensor<T> {
+    let exp_tensor: Tensor<W> = exp_upcast_zero(*z);
+    let sum_no_zero = reduce_sum_no_zero(@exp_tensor, axis, true);
+    div_downcast(@exp_tensor, @sum_no_zero)
+}
+
+
+/// Helper function that compute the exponential of a tensor except if the value of an entry is zero, the value remains zero.
+///
+/// # Arguments
+/// * `z` - The input tensor.
+///
+/// # Returns
+/// * A Tensor<T> representing the exponential of the tensor except for the entries equal to zero in the input tensor, they remain zero.
+fn exp_zero<
+    T,
+    MAG,
+    impl TNumber: NumberTrait<T, MAG>,
+    impl FTensor: TensorTrait<T>,
+    impl TPartialEq: PartialEq<T>,
+    impl FCopy: Copy<T>,
+    impl FDrop: Drop<T>,
+>(
+    mut z: Tensor<T>
+) -> Tensor<T> {
+    let mut result = ArrayTrait::new();
+
+    loop {
+        match z.data.pop_front() {
+            Option::Some(item) => {
+                if *item == NumberTrait::zero() {
+                    result.append(NumberTrait::zero());
+                } else {
+                    result.append((*item).exp());
+                }
+            },
+            Option::None(_) => { break; }
+        };
+    };
+
+    return TensorTrait::new(z.shape, result.span());
+}
+
+/// Helper function that compute the exponential of a tensor except if the value of an entry is zero, the value remains zero.
+///
+/// # Arguments
+/// * `z` - The input tensor.
+///
+/// # Returns
+/// * A Tensor<T> representing the exponential of the tensor except for the entries equal to zero in the input tensor, they remain zero.
+fn exp_upcast_zero<
+    T,
+    TMAG,
+    W,
+    WMAG,
+    impl TNumber: NumberTrait<T, TMAG>,
+    impl TTensor: TensorTrait<T>,
+    impl TPartialEq: PartialEq<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
+    impl WNumber: NumberTrait<W, WMAG>,
+    impl WTensor: TensorTrait<W>,
+    impl WCopy: Copy<W>,
+    impl WDrop: Drop<W>,
+    impl TIntoW: Into<T, W>,
+>(
+    mut self: Tensor<T>
+) -> Tensor<W> {
+    let mut result = ArrayTrait::new();
+
+    loop {
+        match self.data.pop_front() {
+            Option::Some(item) => {
+                if *item == NumberTrait::zero() {
+                    result.append(NumberTrait::zero());
+                } else {
+                    result.append((TIntoW::into(*item)).exp());
+                }
+            },
+            Option::None(_) => { break; }
+        };
+    };
+
+    return TensorTrait::new(self.shape, result.span());
+}
+
+
+/// Helper function that compute the reduce sum making sure no none zero value are in the output tensor.
+///
+/// # Arguments
+/// * `z` - The input tensor.
+///
+/// # Returns
+/// * A Tensor<T> representing the ereduce sum with no entries equal to zero.
+
+fn reduce_sum_no_zero<
+    T,
+    MAG,
+    impl TTensor: TensorTrait<T>,
+    impl TNumber: NumberTrait<T, MAG>,
+    impl TAddEq: AddEq<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
+    impl TPartialEq: PartialEq<T>,
+>(
+    self: @Tensor<T>, axis: usize, keepdims: bool
+) -> Tensor<T> {
+    let mut output_data = ArrayTrait::new();
+
+    if (*self.shape).len() == 1 {
+        assert(axis == 0, 'axis out of dimensions');
+        let current_sum = accumulate_sum::<T>(*self.data, *self.shape, *self.shape, axis);
+        output_data.append(current_sum);
+
+        let mut output_shape = ArrayTrait::new();
+        output_shape.append(1);
+
+        return TensorTrait::new(output_shape.span(), output_data.span());
+    } else {
+        assert(axis <= (*self.shape).len(), 'axis out of dimensions');
+        let output_shape = reduce_output_shape(*self.shape, axis, false);
+        let output_data_len = len_from_shape(output_shape);
+        let mut index: usize = 0;
+        loop {
+            let output_indices = unravel_index(index, output_shape);
+            let current_sum = accumulate_sum::<T>(*self.data, *self.shape, output_indices, axis);
+
+            if current_sum == NumberTrait::zero() {
+                let current_sum: T = NumberTrait::one();
+            }
+            output_data.append(current_sum);
+
+            index += 1;
+            if index == output_data_len {
+                break ();
+            };
+        };
+
+        if keepdims {
+            let output_shape = reduce_output_shape(*self.shape, axis, true);
+            return TensorTrait::<T>::new(output_shape, output_data.span());
+        } else {
+            return TensorTrait::<T>::new(output_shape, output_data.span());
+        }
+    }
+}
diff --git a/src/operators/nn/implementations/nn_fp16x16.cairo b/src/operators/nn/implementations/nn_fp16x16.cairo
index c1bc6970b..785d3c9fa 100644
--- a/src/operators/nn/implementations/nn_fp16x16.cairo
+++ b/src/operators/nn/implementations/nn_fp16x16.cairo
@@ -27,6 +27,10 @@ impl FP16x16NN of NNTrait<FP16x16> {
         functional::softmax::softmaxWide::<FP16x16, u32, FP16x16W, u64>(tensor, axis)
     }
 
+    fn softmax_zero(tensor: @Tensor<FP16x16>, axis: usize) -> Tensor<FP16x16> {
+        functional::softmax_zero::softmaxWide_zero::<FP16x16, u32, FP16x16W, u64>(tensor, axis)
+    }
+
     fn logsoftmax(tensor: @Tensor<FP16x16>, axis: usize) -> Tensor<FP16x16> {
         functional::logsoftmax::logsoftmaxWide::<FP16x16, u32, FP16x16W, u64>(tensor, axis)
     }
diff --git a/src/operators/nn/implementations/nn_fp32x32.cairo b/src/operators/nn/implementations/nn_fp32x32.cairo
index 832c26dcf..0427ea5f7 100644
--- a/src/operators/nn/implementations/nn_fp32x32.cairo
+++ b/src/operators/nn/implementations/nn_fp32x32.cairo
@@ -21,6 +21,10 @@ impl FP32x32NN of NNTrait<FP32x32> {
         functional::softmax::softmax(tensor, axis)
     }
 
+    fn softmax_zero(tensor: @Tensor<FP32x32>, axis: usize) -> Tensor<FP32x32> {
+        functional::softmax_zero::softmax_zero(tensor, axis)
+    }
+
     fn logsoftmax(tensor: @Tensor<FP32x32>, axis: usize) -> Tensor<FP32x32> {
         functional::logsoftmax::logsoftmax(tensor, axis)
     }
diff --git a/src/operators/nn/implementations/nn_fp64x64.cairo b/src/operators/nn/implementations/nn_fp64x64.cairo
index 0a674fe47..fec810679 100644
--- a/src/operators/nn/implementations/nn_fp64x64.cairo
+++ b/src/operators/nn/implementations/nn_fp64x64.cairo
@@ -21,6 +21,10 @@ impl FP64x64NN of NNTrait<FP64x64> {
         functional::softmax::softmax(tensor, axis)
     }
 
+    fn softmax_zero(tensor: @Tensor<FP64x64>, axis: usize) -> Tensor<FP64x64> {
+        functional::softmax_zero::softmax_zero(tensor, axis)
+    }
+
     fn logsoftmax(tensor: @Tensor<FP64x64>, axis: usize) -> Tensor<FP64x64> {
         functional::logsoftmax::logsoftmax(tensor, axis)
     }
diff --git a/src/operators/nn/implementations/nn_fp8x23.cairo b/src/operators/nn/implementations/nn_fp8x23.cairo
index d246bf2cc..9f5416121 100644
--- a/src/operators/nn/implementations/nn_fp8x23.cairo
+++ b/src/operators/nn/implementations/nn_fp8x23.cairo
@@ -25,6 +25,10 @@ impl FP8x23NN of NNTrait<FP8x23> {
         functional::softmax::softmaxWide::<FP8x23, u32, FP8x23W, u64>(tensor, axis)
     }
 
+    fn softmax_zero(tensor: @Tensor<FP8x23>, axis: usize) -> Tensor<FP8x23> {
+        functional::softmax_zero::softmaxWide_zero::<FP8x23, u32, FP8x23W, u64>(tensor, axis)
+    }
+
     fn logsoftmax(tensor: @Tensor<FP8x23>, axis: usize) -> Tensor<FP8x23> {
         functional::logsoftmax::logsoftmaxWide::<FP8x23, u32, FP8x23W, u64>(tensor, axis)
     }
diff --git a/src/operators/nn/implementations/nn_i32.cairo b/src/operators/nn/implementations/nn_i32.cairo
index dee95ec9f..232ebee84 100644
--- a/src/operators/nn/implementations/nn_i32.cairo
+++ b/src/operators/nn/implementations/nn_i32.cairo
@@ -19,6 +19,10 @@ impl I32NN of NNTrait<i32> {
         panic(array!['not supported!'])
     }
 
+    fn softmax_zero(tensor: @Tensor<i32>, axis: usize) -> Tensor<i32> {
+        panic(array!['not supported!'])
+    }
+
     fn logsoftmax(tensor: @Tensor<i32>, axis: usize) -> Tensor<i32> {
         panic(array!['not supported!'])
     }
diff --git a/src/operators/nn/implementations/nn_i8.cairo b/src/operators/nn/implementations/nn_i8.cairo
index c9057fcce..185ba4a4e 100644
--- a/src/operators/nn/implementations/nn_i8.cairo
+++ b/src/operators/nn/implementations/nn_i8.cairo
@@ -19,6 +19,10 @@ impl I8NN of NNTrait<i8> {
         panic(array!['not supported!'])
     }
 
+    fn softmax_zero(tensor: @Tensor<i8>, axis: usize) -> Tensor<i8> {
+        panic(array!['not supported!'])
+    }
+
     fn logsoftmax(tensor: @Tensor<i8>, axis: usize) -> Tensor<i8> {
         panic(array!['not supported!'])
     }
diff --git a/src/operators/nn/implementations/nn_u32.cairo b/src/operators/nn/implementations/nn_u32.cairo
index 1a2883a16..370880e8d 100644
--- a/src/operators/nn/implementations/nn_u32.cairo
+++ b/src/operators/nn/implementations/nn_u32.cairo
@@ -18,6 +18,10 @@ impl U32NN of NNTrait<u32> {
         panic(array!['not supported!'])
     }
 
+    fn softmax_zero(tensor: @Tensor<u32>, axis: usize) -> Tensor<u32> {
+        panic(array!['not supported!'])
+    }
+
     fn logsoftmax(tensor: @Tensor<u32>, axis: usize) -> Tensor<u32> {
         panic(array!['not supported!'])
     }
diff --git a/src/operators/sequence.cairo b/src/operators/sequence.cairo
new file mode 100644
index 000000000..eaf749c63
--- /dev/null
+++ b/src/operators/sequence.cairo
@@ -0,0 +1,14 @@
+mod core;
+mod implementations;
+mod functional;
+
+use orion::operators::sequence::core::SequenceTrait;
+
+use orion::operators::sequence::implementations::sequence_fp8x23::FP8x23Sequence;
+use orion::operators::sequence::implementations::sequence_fp8x23wide::FP8x23WSequence;
+use orion::operators::sequence::implementations::sequence_fp16x16::FP16x16Sequence;
+use orion::operators::sequence::implementations::sequence_fp16x16wide::FP16x16WSequence;
+use orion::operators::sequence::implementations::sequence_i8::I8Sequence;
+use orion::operators::sequence::implementations::sequence_i32::I32Sequence;
+use orion::operators::sequence::implementations::sequence_u32::U32Sequence;
+use orion::operators::sequence::implementations::sequence_bool::BoolSequence;
diff --git a/src/operators/sequence/core.cairo b/src/operators/sequence/core.cairo
new file mode 100644
index 000000000..268e25b16
--- /dev/null
+++ b/src/operators/sequence/core.cairo
@@ -0,0 +1,348 @@
+use orion::operators::tensor::core::Tensor;
+use orion::numbers::signed_integer::i32::i32;
+
+/// Trait
+///
+/// sequence_construct - Constructs a tensor sequence containing the input tensors.
+/// sequence_empty - Returns an empty tensor sequence.
+/// sequence_length - Returns the length of the input sequence.
+/// sequence_insert - Insert a tensor into a sequence.
+/// sequence_at - Outputs the tensor at the specified position in the input sequence.
+/// sequence_erase – Outputs the tensor sequence with the erased tensor at the specified position.
+/// concat_from_sequence - Concatenate a sequence of tensors into a single tensor.
+trait SequenceTrait<T> {
+    /// ## sequence.sequence_construct
+    ///
+    /// ```rust 
+    ///    fn sequence_construct(tensors: Array<Tensor<T>>) -> Array<Tensor<T>>;
+    /// ```
+    ///
+    /// Constructs a tensor sequence containing the input tensors.
+    ///
+    /// ## Args
+    ///
+    /// * `tensors`(`Array<Tensor<T>>`) - The array of input tensors.
+    ///
+    /// ## Panics 
+    /// 
+    /// * Panics if input tensor array is empty.
+    ///
+    /// ## Returns
+    ///
+    /// A tensor sequence `Array<Tensor<T>>` containing the input tensors.
+    ///
+    /// ## Examples
+    ///
+    /// ```rust
+    /// use array::{ArrayTrait, SpanTrait};
+    /// 
+    /// use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};
+    /// use orion::operators::sequence::SequenceTrait;
+    ///
+    /// fn sequence_construct_example() -> Array<Tensor<usize>> {
+    ///     let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span());
+    ///     let tensor2 = TensorTrait::new(shape: array![2, 2].span(), data: array![4, 5, 6, 7].span());
+    ///     let result = SequenceTrait::sequence_construct(tensors: array![tensor1, tensor2]);
+    ///     return result;
+    /// }
+    /// >>> [[0, 1, 2, 3], [4, 5, 6, 7]]
+    /// ```
+    ///
+    fn sequence_construct(tensors: Array<Tensor<T>>) -> Array<Tensor<T>>;
+    /// ## sequence.sequence_empty
+    ///
+    /// ```rust
+    ///    fn sequence_empty() -> Array<Tensor<T>>;
+    /// ```
+    ///
+    /// Returns an empty tensor sequence.
+    ///
+    /// ## Args
+    ///
+    /// ## Returns
+    ///
+    /// An empty `Array<Tensor<T>>` instance.
+    ///
+    /// ## Examples
+    ///
+    /// Let's create a new empty sequence.
+    ///
+    /// ```rust
+    /// use array::{ArrayTrait, SpanTrait};
+    ///
+    /// use orion::operators::tensor::{
+    ///     TensorTrait, // we import the trait
+    ///     Tensor, // we import the type
+    ///     U32Tensor // we import the implementation. 
+    /// };
+    /// use orion::operators::sequence::SequenceTrait;
+    ///
+    /// fn sequence_empty_example() -> Array<Tensor<u32>> {
+    ///     let sequence = SequenceTrait::sequence_empty();
+    ///
+    ///     return sequence;
+    /// }
+    ///
+    /// >>> []
+    /// ```
+    ///
+    fn sequence_empty() -> Array<Tensor<T>>;
+    /// # tensor.sequence_length
+    ///
+    /// ```rust
+    ///    fn sequence_length(self: Array<Tensor<T>>) -> Tensor<u32>;
+    /// ```
+    ///
+    /// Returns the length of the input sequence.
+    ///
+    /// ## Args
+    ///
+    /// * `self`(`Array<Tensor<T>>`) - The input sequence.
+    ///
+    /// ## Returns
+    ///
+    /// The length of the sequence as scalar, i.e. a tensor of shape [].
+    ///
+    /// ## Examples
+    ///
+    /// Let's create new u32 Tensor with constant 42.
+    ///
+    /// ```rust
+    /// let mut sequence = ArrayTrait::new();
+    ///
+    /// let mut shape = ArrayTrait::<usize>::new();
+    /// shape.append(1);
+    /// shape.append(2);
+    ///
+    /// let mut data = ArrayTrait::new();
+    /// data.append(3);
+    /// data.append(1);
+    ///
+    /// sequence.append(TensorTrait::new(shape.span(), data.span()));
+    ///
+    /// sequence.sequence_length()
+    /// >>> [1]
+    /// ```
+    ///
+    fn sequence_length(self: Array<Tensor<T>>) -> Tensor<u32>;
+    /// # tensor.sequence_insert
+    ///
+    /// ```rust 
+    ///    fn sequence_insert(self: Array<Tensor<T>>, tensor: @Tensor<T>, position: Option<Tensor<i32>>) -> Array<Tensor<T>>;
+    /// ```
+    ///
+    /// Returns a tensor sequence that inserts 'tensor' into 'self' at 'position'.
+    ///
+    /// ## Args
+    ///
+    /// * `self`(`Array<Tensor<T>>`) - input sequence.
+    /// * `tensor` (`@Tensor<T>`) - the tensor to insert.
+    /// * `position` (`@Tensor<i32>`) - the index for insertion (default: -1).
+    ///
+    /// ## Returns
+    ///
+    /// Tensor sequence containing 'tensor' inserted into 'self' at 'position'.
+    ///
+    /// ## Examples
+    ///
+    /// Let's insert the tensor [2] into the sequence [[1], [3]] at position 1.
+    /// use orion::operators::tensor::{TensorTrait, Tensor, I32Tensor, U32Tensor};
+    ///
+    /// fn sequence_insert_example() -> Array<Tensor<u32>> {
+    ///     // Prepare sequence
+    ///     let mut sequence = ArrayTrait::new();
+    ///     let mut shape = ArrayTrait::<usize>::new();
+    ///     shape.append(1);
+    ///
+    ///     let mut data = ArrayTrait::new();
+    ///     data.append(1);
+    ///     sequence.append(TensorTrait::new(shape.span(), data.span()));
+    ///     let mut data = ArrayTrait::new();
+    ///     data.append(3);
+    ///
+    ///     sequence.append(TensorTrait::new(shape.span(), data.span()));
+    ///
+    ///     // Prepare input tensor
+    ///     let mut data = ArrayTrait::new();
+    ///     data.append(2);
+    ///     let tensor = TensorTrait::new(shape.span(), data.span());
+    ///
+    ///     // Prepare position
+    ///     let mut shape = ArrayTrait::<usize>::new();
+    ///     let mut data = ArrayTrait::<i32>::new();
+    ///     data.append(i32 { mag: 1, sign: false });
+    ///     let position = TensorTrait::<i32>::new(shape.span(), data.span())
+    ///
+    ///     let sequence = self.sequence_insert(tensor, Option::Some(position));
+    ///
+    ///     return sequence;
+    /// }
+    ///
+    /// >>> [[1], [2], [3]]
+    /// ```
+    ///
+    fn sequence_insert(
+        self: Array<Tensor<T>>, tensor: @Tensor<T>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<T>>;
+    /// ## tensor.sequence_at
+    ///
+    /// ```rust 
+    ///    fn sequence_at(sequence: Array<Tensor<T>>, position: Tensor<i32>) -> Tensor<T>;
+    /// ```
+    ///
+    /// Outputs the tensor at the specified position in the input sequence.
+    ///
+    /// ## Args
+    ///
+    /// * `tensors`(`Array<Tensor<T>>`) - The tensor sequence.
+    /// * `position`(`Tensor<i32>`) - The position tensor.
+    ///
+    /// ## Panics 
+    /// 
+    /// * Panics if position is not a scalar
+    /// * Panics if position is out of bounds [-n, n - 1]
+    ///
+    /// ## Returns
+    ///
+    /// The tensor `Tensor<T>` from the sequence at the specified position.
+    ///
+    /// ## Examples
+    ///
+    /// ```rust
+    /// use core::array::{ArrayTrait, SpanTrait};
+    /// 
+    /// use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor, I32Tensor};
+    /// use orion::numbers::{i32, IntegerTrait};
+    ///
+    /// fn sequence_at_example() -> Tensor<u32> {
+    ///     let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span());
+    ///     let tensor2 = TensorTrait::new(shape: array![2, 2].span(), data: array![4, 5, 6, 7].span());
+    ///     
+    ///     let mut sequence = ArrayTrait::new();
+    ///     sequence.append(tensor1);
+    ///     sequence.append(tensor2);
+    ///
+    ///     let position = TensorTrait::new(shape: array![].span(), data: array![IntegerTrait::new(1, false)].span());
+    ///
+    ///     let result = TensorTrait::sequence_at(sequence, position);
+    ///     return result;
+    /// }
+    /// >>> [4, 5, 6, 7]
+    /// ```
+    ///
+    fn sequence_at(sequence: Array<Tensor<T>>, position: Tensor<i32>) -> Tensor<T>;
+    /// ## tensor.sequence_erase
+    ///
+    /// ```rust 
+    ///    fn sequence_erase(sequence: Array<Tensor<T>>, position: Option<Tensor<i32>>) -> Array<Tensor<T>>;
+    /// ```
+    ///
+    /// Outputs the tensor sequence with the erased tensor at the specified position.
+    ///
+    /// ## Args
+    ///
+    /// * `tensors`(`Array<Tensor<T>>`) - The tensor sequence.
+    /// * `position`(`Option<Tensor<i32>>`) - The optional position tensor (by default erases the last tensor).
+    ///
+    /// ## Panics 
+    /// 
+    /// * Panics if position is not a scalar
+    /// * Panics if position is out of bounds [-n, n - 1]
+    ///
+    /// ## Returns
+    ///
+    /// The tensor sequence `Array<Tensor<T>>` with the erased tensor at the specified position.
+    ///
+    /// ## Examples
+    ///
+    /// ```rust
+    /// use core::array::{ArrayTrait, SpanTrait};
+    /// 
+    /// use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor, I32Tensor};
+    /// use orion::numbers::{i32, IntegerTrait};
+    ///
+    /// fn sequence_erase_example() -> Tensor<u32> {
+    ///     let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span());
+    ///     let tensor2 = TensorTrait::new(shape: array![2, 2].span(), data: array![4, 5, 6, 7].span());
+    ///     let tensor3 = TensorTrait::new(shape: array![2, 2].span(), data: array![8, 9, 10, 11].span());
+    ///     
+    ///     let mut sequence = ArrayTrait::new();
+    ///     sequence.append(tensor1);
+    ///     sequence.append(tensor2);
+    ///     sequence.append(tensor3);
+    ///
+    ///     let position = TensorTrait::new(shape: array![].span(), data: array![IntegerTrait::new(1, false)].span());
+    ///
+    ///     let result = TensorTrait::sequence_erase(sequence, position);
+    ///     return result;
+    /// }
+    /// >>> [[0, 1, 2, 3], [8, 9, 10, 11]]
+    /// ```
+    ///
+    fn sequence_erase(
+        sequence: Array<Tensor<T>>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<T>>;
+    /// # tensor.concat_from_sequence
+    ///
+    /// ```rust 
+    ///    fn concat_from_sequence(sequence: Array<Tensor<T>>, axis: i32, new_axis: Option<usize>) -> Tensor<T>;
+    /// ```
+    ///
+    /// Concatenate a sequence of tensors into a single tensor.
+    ///
+    /// ## Args
+    ///
+    /// * `sequence`(`Array<Tensor<T>>`) - The input sequence.
+    /// * `axis`(`i32`) -  Axis to concat on.
+    /// * `new_axis`(`Option<usize>`) -  Optionally added new axis.
+    ///
+    /// ## Panics
+    ///
+    /// * Panics if new_axis not 0 or 1 (if value provided).
+    /// * Panics if axis not in accepted ranges.
+    /// * Panics if sequence length is not greater than 1.
+    ///
+    /// ## Returns 
+    ///
+    /// A new `Tensor<T>` concatenated tensor from the input tensor sequence.
+    ///
+    /// ## Example
+    ///
+    /// ```rust
+    /// use core::array::{ArrayTrait, SpanTrait};
+    /// 
+    /// use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};
+    /// 
+    /// fn concat_example() -> Tensor<u32> {
+    ///     let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span(),);
+    ///     let tensor2 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span(),);
+    ///
+    ///     let mut sequence = ArrayTrait::new();
+    ///     sequence.append(tensor1);
+    ///     sequence.append(tensor2);
+    ///
+    ///     let result = TensorTrait::concat_from_sequence(sequence: sequence, axis: 0, new_axis: Option::Some(0));
+    ///     return result;
+    /// }
+    /// >>> [[0. 1.]
+    ///      [2. 3.],
+    ///      [0. 1.]
+    ///      [2. 3.]]
+    ///
+    ///     result.shape
+    /// >>> (4, 2)
+    ///
+    ///    let result = TensorTrait::concat_from_sequence(sequence: sequence, axis: 1, new_axis: Option::Some(0));
+    ///    return result;
+    /// }
+    /// >>> [[0. 1., 0., 1.]
+    ///      [2. 3., 2., 3.]]
+    ///
+    ///     result.shape
+    /// >>> (2, 4 ) 
+    /// ```
+    ///
+    fn concat_from_sequence(
+        sequence: Array<Tensor<T>>, axis: i32, new_axis: Option<usize>
+    ) -> Tensor<T>;
+}
diff --git a/src/operators/sequence/functional.cairo b/src/operators/sequence/functional.cairo
new file mode 100644
index 000000000..e0b80db7c
--- /dev/null
+++ b/src/operators/sequence/functional.cairo
@@ -0,0 +1,7 @@
+mod sequence_construct;
+mod sequence_empty;
+mod sequence_at;
+mod sequence_erase;
+mod sequence_insert;
+mod sequence_length;
+mod concat_from_sequence;
diff --git a/src/operators/tensor/math/concat_from_sequence.cairo b/src/operators/sequence/functional/concat_from_sequence.cairo
similarity index 100%
rename from src/operators/tensor/math/concat_from_sequence.cairo
rename to src/operators/sequence/functional/concat_from_sequence.cairo
diff --git a/src/operators/tensor/math/sequence_at.cairo b/src/operators/sequence/functional/sequence_at.cairo
similarity index 95%
rename from src/operators/tensor/math/sequence_at.cairo
rename to src/operators/sequence/functional/sequence_at.cairo
index 81f6776ac..6108b97d4 100644
--- a/src/operators/tensor/math/sequence_at.cairo
+++ b/src/operators/sequence/functional/sequence_at.cairo
@@ -5,7 +5,7 @@ use orion::operators::tensor::core::{Tensor, TensorTrait};
 use orion::numbers::NumberTrait;
 use orion::numbers::signed_integer::i32::i32;
 
-/// Cf: TensorTrait::sequence_at docstring
+/// Cf: SequenceTrait::sequence_at docstring
 fn sequence_at<T, impl TTensor: TensorTrait<T>, impl TCopy: Copy<T>, impl TDrop: Drop<T>>(
     sequence: Array<Tensor<T>>, position: Tensor<i32>
 ) -> Tensor<T> {
diff --git a/src/operators/tensor/math/sequence_construct.cairo b/src/operators/sequence/functional/sequence_construct.cairo
similarity index 84%
rename from src/operators/tensor/math/sequence_construct.cairo
rename to src/operators/sequence/functional/sequence_construct.cairo
index 7b24560a7..18902b078 100644
--- a/src/operators/tensor/math/sequence_construct.cairo
+++ b/src/operators/sequence/functional/sequence_construct.cairo
@@ -3,7 +3,7 @@ use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::{TensorTrait, Tensor};
 
 
-/// Cf: TensorTrait::sequence_construct docstring
+/// Cf: SequenceTrait::sequence_construct docstring
 fn sequence_construct<T, impl TDrop: Drop<T>>(tensors: Array<Tensor<T>>) -> Array<Tensor<T>> {
     assert(tensors.len() >= 1, 'Input tensors must be >= 1');
 
diff --git a/src/operators/tensor/math/sequence_empty.cairo b/src/operators/sequence/functional/sequence_empty.cairo
similarity index 90%
rename from src/operators/tensor/math/sequence_empty.cairo
rename to src/operators/sequence/functional/sequence_empty.cairo
index 66ad658da..93e2989cc 100644
--- a/src/operators/tensor/math/sequence_empty.cairo
+++ b/src/operators/sequence/functional/sequence_empty.cairo
@@ -3,7 +3,7 @@ use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::{TensorTrait, Tensor};
 
 
-/// Cf: TensorTrait::sequence_empty docstring
+/// Cf: SequenceTrait::sequence_empty docstring
 fn sequence_empty<T, impl TTensorTrait: TensorTrait<T>, impl TDrop: Drop<T>>() -> Array<Tensor<T>> {
     let mut sequence = ArrayTrait::new();
 
diff --git a/src/operators/tensor/math/sequence_erase.cairo b/src/operators/sequence/functional/sequence_erase.cairo
similarity index 97%
rename from src/operators/tensor/math/sequence_erase.cairo
rename to src/operators/sequence/functional/sequence_erase.cairo
index d2f8de500..69349b8ad 100644
--- a/src/operators/tensor/math/sequence_erase.cairo
+++ b/src/operators/sequence/functional/sequence_erase.cairo
@@ -6,7 +6,7 @@ use orion::operators::tensor::I32Tensor;
 use orion::numbers::NumberTrait;
 use orion::numbers::signed_integer::i32::i32;
 
-/// Cf: TensorTrait::sequence_erase docstring
+/// Cf: SequenceTrait::sequence_erase docstring
 fn sequence_erase<T, impl TTensor: TensorTrait<T>, impl TCopy: Copy<T>, impl TDrop: Drop<T>>(
     sequence: Array<Tensor<T>>, position: Option<Tensor<i32>>
 ) -> Array<Tensor<T>> {
diff --git a/src/operators/tensor/math/sequence_insert.cairo b/src/operators/sequence/functional/sequence_insert.cairo
similarity index 97%
rename from src/operators/tensor/math/sequence_insert.cairo
rename to src/operators/sequence/functional/sequence_insert.cairo
index a5f7ce673..a140938dc 100644
--- a/src/operators/tensor/math/sequence_insert.cairo
+++ b/src/operators/sequence/functional/sequence_insert.cairo
@@ -6,7 +6,7 @@ use orion::operators::tensor::I32Tensor;
 use orion::numbers::NumberTrait;
 use orion::numbers::signed_integer::i32::i32;
 
-/// Cf: TensorTrait::sequence_insert docstring
+/// Cf: SequenceTrait::sequence_insert docstring
 fn sequence_insert<T, impl TTensor: TensorTrait<T>, impl TCopy: Copy<T>, impl TDrop: Drop<T>>(
     self: Array<Tensor<T>>, tensor: @Tensor<T>, position: Option<Tensor<i32>>
 ) -> Array<Tensor<T>> {
diff --git a/src/operators/tensor/math/sequence_length.cairo b/src/operators/sequence/functional/sequence_length.cairo
similarity index 88%
rename from src/operators/tensor/math/sequence_length.cairo
rename to src/operators/sequence/functional/sequence_length.cairo
index d87aaf357..84f91e48f 100644
--- a/src/operators/tensor/math/sequence_length.cairo
+++ b/src/operators/sequence/functional/sequence_length.cairo
@@ -3,7 +3,7 @@ use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::{TensorTrait, Tensor};
 
 
-/// Cf: TensorTrait::sequence_length docstring
+/// Cf: SequenceTrait::sequence_length docstring
 fn sequence_length<T, impl TDrop: Drop<T>>(self: Array<Tensor<T>>) -> Tensor<u32> {
     let mut shape = ArrayTrait::<usize>::new();
     let mut result = ArrayTrait::new();
diff --git a/src/operators/sequence/implementations.cairo b/src/operators/sequence/implementations.cairo
new file mode 100644
index 000000000..68e3bdf6b
--- /dev/null
+++ b/src/operators/sequence/implementations.cairo
@@ -0,0 +1,10 @@
+mod sequence_bool;
+mod sequence_i8;
+mod sequence_i32;
+mod sequence_u32;
+mod sequence_fp8x23;
+mod sequence_fp8x23wide;
+mod sequence_fp16x16;
+mod sequence_fp16x16wide;
+mod sequence_fp32x32;
+mod sequence_fp64x64;
diff --git a/src/operators/sequence/implementations/sequence_bool.cairo b/src/operators/sequence/implementations/sequence_bool.cairo
new file mode 100644
index 000000000..b9d800123
--- /dev/null
+++ b/src/operators/sequence/implementations/sequence_bool.cairo
@@ -0,0 +1,44 @@
+use core::option::OptionTrait;
+
+use orion::operators::tensor::core::Tensor;
+use orion::operators::sequence::core::SequenceTrait;
+use orion::operators::sequence::functional;
+use orion::operators::tensor::implementations::tensor_bool::BoolTensor;
+use orion::numbers::signed_integer::i32::i32;
+use orion::operators::tensor::implementations::tensor_i32::I32Tensor;
+
+impl BoolSequence of SequenceTrait<bool> {
+    fn sequence_construct(tensors: Array<Tensor<bool>>) -> Array<Tensor<bool>> {
+        functional::sequence_construct::sequence_construct(tensors)
+    }
+
+    fn sequence_empty() -> Array<Tensor<bool>> {
+        functional::sequence_empty::sequence_empty::<bool>()
+    }
+
+    fn sequence_length(self: Array<Tensor<bool>>) -> Tensor<u32> {
+        functional::sequence_length::sequence_length(self)
+    }
+
+    fn sequence_at(sequence: Array<Tensor<bool>>, position: Tensor<i32>) -> Tensor<bool> {
+        functional::sequence_at::sequence_at(sequence, position)
+    }
+
+    fn sequence_erase(
+        sequence: Array<Tensor<bool>>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<bool>> {
+        functional::sequence_erase::sequence_erase(sequence, position)
+    }
+
+    fn sequence_insert(
+        self: Array<Tensor<bool>>, tensor: @Tensor<bool>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<bool>> {
+        functional::sequence_insert::sequence_insert(self, tensor, position)
+    }
+
+    fn concat_from_sequence(
+        sequence: Array<Tensor<bool>>, axis: i32, new_axis: Option<usize>
+    ) -> Tensor<bool> {
+        functional::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
+    }
+}
diff --git a/src/operators/sequence/implementations/sequence_fp16x16.cairo b/src/operators/sequence/implementations/sequence_fp16x16.cairo
new file mode 100644
index 000000000..5da4af097
--- /dev/null
+++ b/src/operators/sequence/implementations/sequence_fp16x16.cairo
@@ -0,0 +1,46 @@
+use core::option::OptionTrait;
+
+use orion::operators::tensor::core::Tensor;
+use orion::operators::sequence::core::SequenceTrait;
+use orion::operators::sequence::functional;
+use orion::numbers::fixed_point::implementations::fp16x16::core::FP16x16;
+use orion::operators::tensor::implementations::tensor_fp16x16::FP16x16Tensor;
+use orion::numbers::signed_integer::i32::i32;
+use orion::operators::tensor::implementations::tensor_i32::I32Tensor;
+
+
+impl FP16x16Sequence of SequenceTrait<FP16x16> {
+    fn sequence_construct(tensors: Array<Tensor<FP16x16>>) -> Array<Tensor<FP16x16>> {
+        functional::sequence_construct::sequence_construct(tensors)
+    }
+
+    fn sequence_empty() -> Array<Tensor<FP16x16>> {
+        functional::sequence_empty::sequence_empty::<FP16x16>()
+    }
+
+    fn sequence_length(self: Array<Tensor<FP16x16>>) -> Tensor<u32> {
+        functional::sequence_length::sequence_length(self)
+    }
+
+    fn sequence_at(sequence: Array<Tensor<FP16x16>>, position: Tensor<i32>) -> Tensor<FP16x16> {
+        functional::sequence_at::sequence_at(sequence, position)
+    }
+
+    fn sequence_erase(
+        sequence: Array<Tensor<FP16x16>>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<FP16x16>> {
+        functional::sequence_erase::sequence_erase(sequence, position)
+    }
+
+    fn sequence_insert(
+        self: Array<Tensor<FP16x16>>, tensor: @Tensor<FP16x16>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<FP16x16>> {
+        functional::sequence_insert::sequence_insert(self, tensor, position)
+    }
+
+    fn concat_from_sequence(
+        sequence: Array<Tensor<FP16x16>>, axis: i32, new_axis: Option<usize>
+    ) -> Tensor<FP16x16> {
+        functional::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
+    }
+}
diff --git a/src/operators/sequence/implementations/sequence_fp16x16wide.cairo b/src/operators/sequence/implementations/sequence_fp16x16wide.cairo
new file mode 100644
index 000000000..3b2273edb
--- /dev/null
+++ b/src/operators/sequence/implementations/sequence_fp16x16wide.cairo
@@ -0,0 +1,46 @@
+use core::option::OptionTrait;
+
+use orion::operators::tensor::core::Tensor;
+use orion::operators::sequence::core::SequenceTrait;
+use orion::operators::sequence::functional;
+use orion::numbers::fixed_point::implementations::fp16x16wide::core::FP16x16W;
+use orion::operators::tensor::implementations::tensor_fp16x16wide::FP16x16WTensor;
+use orion::numbers::signed_integer::i32::i32;
+use orion::operators::tensor::implementations::tensor_i32::I32Tensor;
+
+
+impl FP16x16WSequence of SequenceTrait<FP16x16W> {
+    fn sequence_construct(tensors: Array<Tensor<FP16x16W>>) -> Array<Tensor<FP16x16W>> {
+        functional::sequence_construct::sequence_construct(tensors)
+    }
+
+    fn sequence_empty() -> Array<Tensor<FP16x16W>> {
+        functional::sequence_empty::sequence_empty::<FP16x16W>()
+    }
+
+    fn sequence_length(self: Array<Tensor<FP16x16W>>) -> Tensor<u32> {
+        functional::sequence_length::sequence_length(self)
+    }
+
+    fn sequence_at(sequence: Array<Tensor<FP16x16W>>, position: Tensor<i32>) -> Tensor<FP16x16W> {
+        functional::sequence_at::sequence_at(sequence, position)
+    }
+
+    fn sequence_erase(
+        sequence: Array<Tensor<FP16x16W>>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<FP16x16W>> {
+        functional::sequence_erase::sequence_erase(sequence, position)
+    }
+
+    fn sequence_insert(
+        self: Array<Tensor<FP16x16W>>, tensor: @Tensor<FP16x16W>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<FP16x16W>> {
+        functional::sequence_insert::sequence_insert(self, tensor, position)
+    }
+
+    fn concat_from_sequence(
+        sequence: Array<Tensor<FP16x16W>>, axis: i32, new_axis: Option<usize>
+    ) -> Tensor<FP16x16W> {
+        functional::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
+    }
+}
diff --git a/src/operators/sequence/implementations/sequence_fp32x32.cairo b/src/operators/sequence/implementations/sequence_fp32x32.cairo
new file mode 100644
index 000000000..fd63f9639
--- /dev/null
+++ b/src/operators/sequence/implementations/sequence_fp32x32.cairo
@@ -0,0 +1,46 @@
+use core::option::OptionTrait;
+
+use orion::operators::tensor::core::Tensor;
+use orion::operators::sequence::core::SequenceTrait;
+use orion::operators::sequence::functional;
+use orion::numbers::fixed_point::implementations::fp32x32::core::FP32x32;
+use orion::operators::tensor::implementations::tensor_fp32x32::FP32x32Tensor;
+use orion::numbers::signed_integer::i32::i32;
+use orion::operators::tensor::implementations::tensor_i32::I32Tensor;
+
+
+impl FP32x32Sequence of SequenceTrait<FP32x32> {
+    fn sequence_construct(tensors: Array<Tensor<FP32x32>>) -> Array<Tensor<FP32x32>> {
+        functional::sequence_construct::sequence_construct(tensors)
+    }
+
+    fn sequence_empty() -> Array<Tensor<FP32x32>> {
+        functional::sequence_empty::sequence_empty::<FP32x32>()
+    }
+
+    fn sequence_length(self: Array<Tensor<FP32x32>>) -> Tensor<u32> {
+        functional::sequence_length::sequence_length(self)
+    }
+
+    fn sequence_at(sequence: Array<Tensor<FP32x32>>, position: Tensor<i32>) -> Tensor<FP32x32> {
+        functional::sequence_at::sequence_at(sequence, position)
+    }
+
+    fn sequence_erase(
+        sequence: Array<Tensor<FP32x32>>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<FP32x32>> {
+        functional::sequence_erase::sequence_erase(sequence, position)
+    }
+
+    fn sequence_insert(
+        self: Array<Tensor<FP32x32>>, tensor: @Tensor<FP32x32>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<FP32x32>> {
+        functional::sequence_insert::sequence_insert(self, tensor, position)
+    }
+
+    fn concat_from_sequence(
+        sequence: Array<Tensor<FP32x32>>, axis: i32, new_axis: Option<usize>
+    ) -> Tensor<FP32x32> {
+        functional::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
+    }
+}
diff --git a/src/operators/sequence/implementations/sequence_fp64x64.cairo b/src/operators/sequence/implementations/sequence_fp64x64.cairo
new file mode 100644
index 000000000..43424f9eb
--- /dev/null
+++ b/src/operators/sequence/implementations/sequence_fp64x64.cairo
@@ -0,0 +1,46 @@
+use core::option::OptionTrait;
+
+use orion::operators::tensor::core::Tensor;
+use orion::operators::sequence::core::SequenceTrait;
+use orion::operators::sequence::functional;
+use orion::numbers::fixed_point::implementations::fp64x64::core::FP64x64;
+use orion::operators::tensor::implementations::tensor_fp64x64::FP64x64Tensor;
+use orion::numbers::signed_integer::i32::i32;
+use orion::operators::tensor::implementations::tensor_i32::I32Tensor;
+
+
+impl FP64x64Sequence of SequenceTrait<FP64x64> {
+    fn sequence_construct(tensors: Array<Tensor<FP64x64>>) -> Array<Tensor<FP64x64>> {
+        functional::sequence_construct::sequence_construct(tensors)
+    }
+
+    fn sequence_empty() -> Array<Tensor<FP64x64>> {
+        functional::sequence_empty::sequence_empty::<FP64x64>()
+    }
+
+    fn sequence_length(self: Array<Tensor<FP64x64>>) -> Tensor<u32> {
+        functional::sequence_length::sequence_length(self)
+    }
+
+    fn sequence_at(sequence: Array<Tensor<FP64x64>>, position: Tensor<i32>) -> Tensor<FP64x64> {
+        functional::sequence_at::sequence_at(sequence, position)
+    }
+
+    fn sequence_erase(
+        sequence: Array<Tensor<FP64x64>>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<FP64x64>> {
+        functional::sequence_erase::sequence_erase(sequence, position)
+    }
+
+    fn sequence_insert(
+        self: Array<Tensor<FP64x64>>, tensor: @Tensor<FP64x64>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<FP64x64>> {
+        functional::sequence_insert::sequence_insert(self, tensor, position)
+    }
+
+    fn concat_from_sequence(
+        sequence: Array<Tensor<FP64x64>>, axis: i32, new_axis: Option<usize>
+    ) -> Tensor<FP64x64> {
+        functional::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
+    }
+}
diff --git a/src/operators/sequence/implementations/sequence_fp8x23.cairo b/src/operators/sequence/implementations/sequence_fp8x23.cairo
new file mode 100644
index 000000000..08ad26a10
--- /dev/null
+++ b/src/operators/sequence/implementations/sequence_fp8x23.cairo
@@ -0,0 +1,46 @@
+use core::option::OptionTrait;
+
+use orion::operators::tensor::core::Tensor;
+use orion::operators::sequence::core::SequenceTrait;
+use orion::operators::sequence::functional;
+use orion::numbers::fixed_point::implementations::fp8x23::core::FP8x23;
+use orion::operators::tensor::implementations::tensor_fp8x23::FP8x23Tensor;
+use orion::numbers::signed_integer::i32::i32;
+use orion::operators::tensor::implementations::tensor_i32::I32Tensor;
+
+
+impl FP8x23Sequence of SequenceTrait<FP8x23> {
+    fn sequence_construct(tensors: Array<Tensor<FP8x23>>) -> Array<Tensor<FP8x23>> {
+        functional::sequence_construct::sequence_construct(tensors)
+    }
+
+    fn sequence_empty() -> Array<Tensor<FP8x23>> {
+        functional::sequence_empty::sequence_empty::<FP8x23>()
+    }
+
+    fn sequence_length(self: Array<Tensor<FP8x23>>) -> Tensor<u32> {
+        functional::sequence_length::sequence_length(self)
+    }
+
+    fn sequence_at(sequence: Array<Tensor<FP8x23>>, position: Tensor<i32>) -> Tensor<FP8x23> {
+        functional::sequence_at::sequence_at(sequence, position)
+    }
+
+    fn sequence_erase(
+        sequence: Array<Tensor<FP8x23>>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<FP8x23>> {
+        functional::sequence_erase::sequence_erase(sequence, position)
+    }
+
+    fn sequence_insert(
+        self: Array<Tensor<FP8x23>>, tensor: @Tensor<FP8x23>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<FP8x23>> {
+        functional::sequence_insert::sequence_insert(self, tensor, position)
+    }
+
+    fn concat_from_sequence(
+        sequence: Array<Tensor<FP8x23>>, axis: i32, new_axis: Option<usize>
+    ) -> Tensor<FP8x23> {
+        functional::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
+    }
+}
diff --git a/src/operators/sequence/implementations/sequence_fp8x23wide.cairo b/src/operators/sequence/implementations/sequence_fp8x23wide.cairo
new file mode 100644
index 000000000..64bb5576f
--- /dev/null
+++ b/src/operators/sequence/implementations/sequence_fp8x23wide.cairo
@@ -0,0 +1,46 @@
+use core::option::OptionTrait;
+
+use orion::operators::tensor::core::Tensor;
+use orion::operators::sequence::core::SequenceTrait;
+use orion::operators::sequence::functional;
+use orion::numbers::fixed_point::implementations::fp8x23wide::core::FP8x23W;
+use orion::operators::tensor::implementations::tensor_fp8x23wide::FP8x23WTensor;
+use orion::numbers::signed_integer::i32::i32;
+use orion::operators::tensor::implementations::tensor_i32::I32Tensor;
+
+
+impl FP8x23WSequence of SequenceTrait<FP8x23W> {
+    fn sequence_construct(tensors: Array<Tensor<FP8x23W>>) -> Array<Tensor<FP8x23W>> {
+        functional::sequence_construct::sequence_construct(tensors)
+    }
+
+    fn sequence_empty() -> Array<Tensor<FP8x23W>> {
+        functional::sequence_empty::sequence_empty::<FP8x23W>()
+    }
+
+    fn sequence_length(self: Array<Tensor<FP8x23W>>) -> Tensor<u32> {
+        functional::sequence_length::sequence_length(self)
+    }
+
+    fn sequence_at(sequence: Array<Tensor<FP8x23W>>, position: Tensor<i32>) -> Tensor<FP8x23W> {
+        functional::sequence_at::sequence_at(sequence, position)
+    }
+
+    fn sequence_erase(
+        sequence: Array<Tensor<FP8x23W>>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<FP8x23W>> {
+        functional::sequence_erase::sequence_erase(sequence, position)
+    }
+
+    fn sequence_insert(
+        self: Array<Tensor<FP8x23W>>, tensor: @Tensor<FP8x23W>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<FP8x23W>> {
+        functional::sequence_insert::sequence_insert(self, tensor, position)
+    }
+
+    fn concat_from_sequence(
+        sequence: Array<Tensor<FP8x23W>>, axis: i32, new_axis: Option<usize>
+    ) -> Tensor<FP8x23W> {
+        functional::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
+    }
+}
diff --git a/src/operators/sequence/implementations/sequence_i32.cairo b/src/operators/sequence/implementations/sequence_i32.cairo
new file mode 100644
index 000000000..40892070a
--- /dev/null
+++ b/src/operators/sequence/implementations/sequence_i32.cairo
@@ -0,0 +1,44 @@
+use core::option::OptionTrait;
+
+use orion::operators::tensor::core::Tensor;
+use orion::operators::sequence::core::SequenceTrait;
+use orion::operators::sequence::functional;
+use orion::numbers::signed_integer::i32::i32;
+use orion::operators::tensor::implementations::tensor_i32::I32Tensor;
+
+
+impl I32Sequence of SequenceTrait<i32> {
+    fn sequence_construct(tensors: Array<Tensor<i32>>) -> Array<Tensor<i32>> {
+        functional::sequence_construct::sequence_construct(tensors)
+    }
+
+    fn sequence_empty() -> Array<Tensor<i32>> {
+        functional::sequence_empty::sequence_empty::<i32>()
+    }
+
+    fn sequence_length(self: Array<Tensor<i32>>) -> Tensor<u32> {
+        functional::sequence_length::sequence_length(self)
+    }
+
+    fn sequence_at(sequence: Array<Tensor<i32>>, position: Tensor<i32>) -> Tensor<i32> {
+        functional::sequence_at::sequence_at(sequence, position)
+    }
+
+    fn sequence_erase(
+        sequence: Array<Tensor<i32>>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<i32>> {
+        functional::sequence_erase::sequence_erase(sequence, position)
+    }
+
+    fn sequence_insert(
+        self: Array<Tensor<i32>>, tensor: @Tensor<i32>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<i32>> {
+        functional::sequence_insert::sequence_insert(self, tensor, position)
+    }
+
+    fn concat_from_sequence(
+        sequence: Array<Tensor<i32>>, axis: i32, new_axis: Option<usize>
+    ) -> Tensor<i32> {
+        functional::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
+    }
+}
diff --git a/src/operators/sequence/implementations/sequence_i8.cairo b/src/operators/sequence/implementations/sequence_i8.cairo
new file mode 100644
index 000000000..2510f3cf2
--- /dev/null
+++ b/src/operators/sequence/implementations/sequence_i8.cairo
@@ -0,0 +1,46 @@
+use core::option::OptionTrait;
+
+use orion::operators::tensor::core::Tensor;
+use orion::operators::sequence::core::SequenceTrait;
+use orion::operators::sequence::functional;
+use orion::numbers::signed_integer::i8::i8;
+use orion::operators::tensor::implementations::tensor_i8::I8Tensor;
+use orion::numbers::signed_integer::i32::i32;
+use orion::operators::tensor::implementations::tensor_i32::I32Tensor;
+
+
+impl I8Sequence of SequenceTrait<i8> {
+    fn sequence_construct(tensors: Array<Tensor<i8>>) -> Array<Tensor<i8>> {
+        functional::sequence_construct::sequence_construct(tensors)
+    }
+
+    fn sequence_empty() -> Array<Tensor<i8>> {
+        functional::sequence_empty::sequence_empty::<i8>()
+    }
+
+    fn sequence_length(self: Array<Tensor<i8>>) -> Tensor<u32> {
+        functional::sequence_length::sequence_length(self)
+    }
+
+    fn sequence_at(sequence: Array<Tensor<i8>>, position: Tensor<i32>) -> Tensor<i8> {
+        functional::sequence_at::sequence_at(sequence, position)
+    }
+
+    fn sequence_erase(
+        sequence: Array<Tensor<i8>>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<i8>> {
+        functional::sequence_erase::sequence_erase(sequence, position)
+    }
+
+    fn sequence_insert(
+        self: Array<Tensor<i8>>, tensor: @Tensor<i8>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<i8>> {
+        functional::sequence_insert::sequence_insert(self, tensor, position)
+    }
+
+    fn concat_from_sequence(
+        sequence: Array<Tensor<i8>>, axis: i32, new_axis: Option<usize>
+    ) -> Tensor<i8> {
+        functional::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
+    }
+}
diff --git a/src/operators/sequence/implementations/sequence_u32.cairo b/src/operators/sequence/implementations/sequence_u32.cairo
new file mode 100644
index 000000000..ff0e57212
--- /dev/null
+++ b/src/operators/sequence/implementations/sequence_u32.cairo
@@ -0,0 +1,45 @@
+use core::option::OptionTrait;
+
+use orion::operators::tensor::core::Tensor;
+use orion::operators::sequence::core::SequenceTrait;
+use orion::operators::sequence::functional;
+use orion::operators::tensor::implementations::tensor_u32::U32Tensor;
+use orion::numbers::signed_integer::i32::i32;
+use orion::operators::tensor::implementations::tensor_i32::I32Tensor;
+
+
+impl U32Sequence of SequenceTrait<u32> {
+    fn sequence_construct(tensors: Array<Tensor<u32>>) -> Array<Tensor<u32>> {
+        functional::sequence_construct::sequence_construct(tensors)
+    }
+
+    fn sequence_empty() -> Array<Tensor<u32>> {
+        functional::sequence_empty::sequence_empty::<u32>()
+    }
+
+    fn sequence_length(self: Array<Tensor<u32>>) -> Tensor<u32> {
+        functional::sequence_length::sequence_length(self)
+    }
+
+    fn sequence_at(sequence: Array<Tensor<u32>>, position: Tensor<i32>) -> Tensor<u32> {
+        functional::sequence_at::sequence_at(sequence, position)
+    }
+
+    fn sequence_erase(
+        sequence: Array<Tensor<u32>>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<u32>> {
+        functional::sequence_erase::sequence_erase(sequence, position)
+    }
+
+    fn sequence_insert(
+        self: Array<Tensor<u32>>, tensor: @Tensor<u32>, position: Option<Tensor<i32>>
+    ) -> Array<Tensor<u32>> {
+        functional::sequence_insert::sequence_insert(self, tensor, position)
+    }
+
+    fn concat_from_sequence(
+        sequence: Array<Tensor<u32>>, axis: i32, new_axis: Option<usize>
+    ) -> Tensor<u32> {
+        functional::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
+    }
+}
diff --git a/src/operators/tensor.cairo b/src/operators/tensor.cairo
index c217cb1f6..adf2076b5 100644
--- a/src/operators/tensor.cairo
+++ b/src/operators/tensor.cairo
@@ -39,3 +39,7 @@ use orion::operators::tensor::implementations::tensor_u32::{
 
 use orion::operators::tensor::implementations::tensor_bool::{BoolTensor, BoolTensorPartialEq};
 
+use orion::operators::tensor::implementations::tensor_complex64::{
+    Complex64Tensor, Complex64TensorAdd, Complex64TensorSub, Complex64TensorMul, Complex64TensorDiv,
+    Complex64TensorPartialEq,
+};
diff --git a/src/operators/tensor/core.cairo b/src/operators/tensor/core.cairo
index decc2e343..796224fb1 100644
--- a/src/operators/tensor/core.cairo
+++ b/src/operators/tensor/core.cairo
@@ -3,6 +3,7 @@ use core::serde::Serde;
 use core::option::OptionTrait;
 
 use alexandria_data_structures::array_ext::{SpanTraitExt};
+//::resize::{MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE};
 
 use orion::operators::tensor::helpers::{len_from_shape, check_shape};
 use orion::numbers::{i8, i32, NumberTrait};
@@ -94,6 +95,7 @@ impl TensorSerde<T, impl TSerde: Serde<T>, impl TDrop: Drop<T>> of Serde<Tensor<
 /// bitwise_and - Computes the bitwise AND of two tensors element-wise.
 /// bitwise_xor - Computes the bitwise XOR of two tensors element-wise.
 /// bitwise_or - Computes the bitwise OR of two tensors element-wise.
+/// resize - Resizes the input tensor.
 /// round - Computes the round value of all elements in the input tensor.
 /// reduce_l1 - Computes the L1 norm of the input tensor's elements along the provided axes.
 /// trilu - Returns the upper or lower triangular part of a tensor or a batch of 2D matrices.
@@ -102,28 +104,20 @@ impl TensorSerde<T, impl TSerde: Serde<T>, impl TDrop: Drop<T>> of Serde<Tensor<
 /// reduce_l2 - Computes the L2 norm of the input tensor's elements along the provided axes.
 /// gather_elements - GatherElements is an indexing operation that produces its output by indexing into the input data tensor at index positions determined by elements of the indices tensor.
 /// reduce_min - Computes the min of the input tensor's elements along the provided axes.
-/// sequence_construct – Constructs a tensor sequence containing the input tensors.
 /// shrink – Shrinks the input tensor element-wise to the output tensor with the same datatype and shape based on a defined formula.
-/// sequence_empty - Returns an empty tensor sequence.
-/// sequence_length - Returns the length of the input sequence.
-/// sequence_insert - Insert a tensor into a sequence.
-/// sequence_at - Outputs the tensor at the specified position in the input sequence.
-/// sequence_construct - Constructs a tensor sequence containing the input tensors.
-/// shrink - Shrinks the input tensor element-wise to the output tensor.
 /// reduce_mean - Computes the mean of the input tensor's elements along the provided axes.
 /// pow - Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function f(x) = x^exponent, is applied to the data tensor elementwise.
-/// sequence_erase – Outputs the tensor sequence with the erased tensor at the specified position.
-/// sequence_empty - Returns an empty tensor sequence.
 /// binarizer - Maps the values of a tensor element-wise to 0 or 1 based on the comparison against a threshold value.
 /// array_feature_extractor - Selects elements of the input tensor based on the indices passed applied to the last tensor axis.
 /// reduce_min - Computes the min of the input tensor's elements along the provided axes.
-/// concat_from_sequence - Concatenate a sequence of tensors into a single tensor.
 /// is_nan - Returns which elements of the input are NaN.
 /// is_inf - Maps infinity to true and other values to false.
 /// not - Computes the logical negation of all elements in the input tensor.
 /// gather_nd - Given data tensor of rank r >= 1, indices tensor of rank q >= 1, and batch_dims integer b, this operator gathers slices of data into an output tensor of rank q + r - indices_shape[-1] - 1 - b.
 /// reduce_log_sum - Computes the log sum of the input tensor's elements along the provided axes. 
 /// erf - Computes the error function of the given input tensor element-wise.
+/// layer_normalization - computes the layer normalization of the input tensor.
+/// split - Split a tensor into a list of tensors, along the specified ‘axis’. 
 trait TensorTrait<T> {
     /// # tensor.new
     ///
@@ -3548,6 +3542,255 @@ trait TensorTrait<T> {
     /// ```
     ///
     fn where(self: @Tensor<T>, x: @Tensor<T>, y: @Tensor<T>) -> Tensor<T>;
+    /// #tensor.resize
+    ///
+    /// ```rust
+    ///     fn resize(
+    ///         self: @Tensor<T>,
+    ///         roi: Option<Tensor<T>>,
+    ///         scales: Option<Span<T>>,
+    ///         sizes: Option<Span<usize>>,
+    ///         antialias: Option<usize>,
+    ///         axes: Option<Span<usize>>,
+    ///         coordinate_transformation_mode: Option<orion::operators::tensor::math::resize::TRANSFORMATION_MODE>,
+    ///         cubic_coeff_a: Option<T>,
+    ///         exclude_outside: Option<bool>,
+    ///         extrapolation_value: Option<T>,
+    ///         keep_aspect_ratio_policy: Option<orion::operators::tensor::math::resize::KEEP_ASPECT_RATIO_POLICY>,
+    ///         mode: Option<orion::operators::tensor::math::resize::MODE>,
+    ///         nearest_mode: Option<orion::operators::tensor::math::resize::NEAREST_MODE>,
+    ///     ) -> Tensor<T>;
+    /// ```
+    ///
+    /// Resizes the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood in the input tensor. 
+    /// 
+    /// ## Args
+    ///
+    /// * `self`(`@Tensor<T>`) - The input tensor.
+    /// * `roi` (`Option<Tensor<T>>`) (optional) - 1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X or the length of axes, if provided. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"
+    /// * `scales` (`Option<Tensor<T>>`) (optional) - The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X' or the length of 'axes', if provided. One and only one of 'scales' and 'sizes' MUST be specified.
+    /// * `sizes` (`Option<Tensor<usize>>`) (optional) - Target size of the output tensor. Its interpretation depends on the 'keep_aspect_ratio_policy' value. The number of elements of 'sizes' should be the same as the rank of input 'X', or the length of 'axes', if provided. One and only one of 'scales' and 'sizes' MUST be specified.  
+    /// * `antialias` (`Option<usize>`) (default is 0) - If set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale).
+    /// * `axes`(`Option<Span<usize>>`) - If provided, it specifies a subset of axes that 'roi', 'scales' and 'sizes' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). 
+    /// * `coordinate_transformation_mode` (`Option<TRANSFORMATION_MODE>`) (default is half_pixel) - This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. 
+    /// * `cubic_coeff_a` (`Option<T>`) (default is -0.75) - The coefficient 'a' used in cubic interpolation.
+    /// * `exclude_outside` (`Option<bool>`) (default is false) - If set to true, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. 
+    /// * `extrapolation_value` (`Option<T>`) (default is 0.0) - When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. 
+    /// * `keep_aspect_ratio_policy` (`Option<KEEP_ASPECT_RATIO_POLICY>`) (default is stretch) - This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used. 
+    /// * `mode` (`Option<MODE>`) (default is nearest) - Three interpolation modes: "nearest", "linear" and "cubic".
+    /// * `nearest_mode` (`Option<NEAREST_MODE>`) (default is round_prefer_floor) - Four modes: "round_prefer_floor" (as known as round half down), "round_prefer_ceil" (as known as round half up), "floor", "ceil". Only used by nearest interpolation. 
+    ///
+    /// ## Panics
+    ///
+    /// * Panics if both scales and sizes are `Option::None`.
+    /// * Panics if roi is `Option::None` for the coordinate_transformation_mode `tf_crop_and_resize`.
+    /// * Panics if antialias is not `Option::None` for mode `nearest`.
+    ///
+    /// ## Returns
+    ///
+    /// A new resized `Tensor<T>` of the dimension given by output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) is scale is specified, or output_size if size is specified (note that some value of the parameter `keep_aspect_ratio_policy` can change sizes and therefore the dimension of the output tensor) 
+    ///
+    /// ## Example
+    ///
+    /// ```rust
+    /// use core::array::{ArrayTrait, SpanTrait};
+    /// use orion::operators::tensor::{TensorTrait, Tensor, FP16x16Tensor, FP16x16TensorPartialEq};
+    /// use orion::operators::tensor::math::resize::{
+    ///     MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+    /// };
+    /// use orion::numbers::{FP16x16, FP16x16Impl, FixedTrait};
+    /// use core::debug::PrintTrait;
+    /// 
+    /// fn example_resize_downsample_scales_linear() -> Tensor<FP16x16>{
+    ///     let mut data = TensorTrait::<
+    ///         FP16x16
+    ///     >::new(
+    ///         shape: array![1, 1, 2, 4].span(),
+    ///         data: array![
+    ///             FixedTrait::<FP16x16>::new(65536, false),   //1
+    ///             FixedTrait::<FP16x16>::new(131072, false),  //2
+    ///             FixedTrait::<FP16x16>::new(196608, false),  //3
+    ///             FixedTrait::<FP16x16>::new(262144, false),  //4
+    ///             FixedTrait::<FP16x16>::new(327680, false),  //5
+    ///             FixedTrait::<FP16x16>::new(393216, false),  //6
+    ///             FixedTrait::<FP16x16>::new(458752, false),  //7
+    ///             FixedTrait::<FP16x16>::new(524288, false),  //8
+    ///         ]
+    ///             .span(),
+    ///     );
+    ///     let mut scales = array![
+    ///         FixedTrait::<FP16x16>::new(65536, false),  //1
+    ///         FixedTrait::<FP16x16>::new(65536, false),   
+    ///         FixedTrait::<FP16x16>::new(39322, false),  //0.6
+    ///         FixedTrait::<FP16x16>::new(39322, false)
+    ///     ]
+    ///         .span();
+    /// 
+    ///     let scales = Option::Some(scales);
+    /// 
+    ///     return data.resize(
+    ///         Option::None,
+    ///         scales,
+    ///         Option::None,
+    ///         Option::None,
+    ///         Option::None,
+    ///         Option::None,
+    ///         Option::None,
+    ///         Option::None,
+    ///         Option::None,
+    ///         Option::None,
+    ///         Option::Some(MODE::LINEAR),
+    ///         Option::None,
+    ///     );
+    /// 
+    /// }
+    /// >>> [[[[2.6666665 4.3333331]]]]
+    /// 
+    /// 
+    /// 
+    /// fn example_resize_tf_crop_and_resize_extrapolation_value() -> Tensor<FP16x16> {
+    ///     let mut data = TensorTrait::<
+    ///         FP16x16
+    ///     >::new(
+    ///         shape: array![1, 1, 4, 4].span(),
+    ///         data: array![
+    ///             FixedTrait::<FP16x16>::new(65536, false),
+    ///             FixedTrait::<FP16x16>::new(131072, false),
+    ///             FixedTrait::<FP16x16>::new(196608, false),
+    ///             FixedTrait::<FP16x16>::new(262144, false),
+    ///             FixedTrait::<FP16x16>::new(327680, false),
+    ///             FixedTrait::<FP16x16>::new(393216, false),
+    ///             FixedTrait::<FP16x16>::new(458752, false),
+    ///             FixedTrait::<FP16x16>::new(524288, false),
+    ///             FixedTrait::<FP16x16>::new(589824, false),
+    ///             FixedTrait::<FP16x16>::new(655360, false),
+    ///             FixedTrait::<FP16x16>::new(720896, false),
+    ///             FixedTrait::<FP16x16>::new(786432, false),
+    ///             FixedTrait::<FP16x16>::new(851968, false),
+    ///             FixedTrait::<FP16x16>::new(917504, false),
+    ///             FixedTrait::<FP16x16>::new(983040, false),
+    ///             FixedTrait::<FP16x16>::new(1048576, false),
+    ///         ]
+    ///             .span(),
+    ///     );
+    /// 
+    ///     let mut roi = TensorTrait::<
+    ///         FP16x16
+    ///     >::new(
+    ///         shape: array![8].span(),
+    ///         data: array![
+    ///             FixedTrait::<FP16x16>::new(0, false),
+    ///             FixedTrait::<FP16x16>::new(0, false),
+    ///             FixedTrait::<FP16x16>::new(26214, false),
+    ///             FixedTrait::<FP16x16>::new(39322, false),
+    ///             FixedTrait::<FP16x16>::new(65536, false),
+    ///             FixedTrait::<FP16x16>::new(65536, false),
+    ///             FixedTrait::<FP16x16>::new(78643, false),
+    ///             FixedTrait::<FP16x16>::new(111411, false),
+    ///         ]
+    ///             .span(),
+    ///     );
+    ///     let roi = Option::Some(roi);
+    /// 
+    ///     let mut sizes = array![1, 1, 3, 3].span();
+    ///     let sizes = Option::Some(sizes);
+    /// 
+    ///     let extrapolation_value = Option::Some(FixedTrait::<FP16x16>::new(655360, false));
+    /// 
+    ///     return data.resize(
+    ///         roi,
+    ///         Option::None,
+    ///         sizes,
+    ///         Option::None,
+    ///         Option::None,
+    ///         Option::Some(TRANSFORMATION_MODE::TF_CROP_AND_RESIZE),
+    ///         Option::None,
+    ///         Option::None,
+    ///         extrapolation_value,
+    ///         Option::None,
+    ///         Option::Some(MODE::LINEAR),
+    ///         Option::None,
+    ///     );
+    /// 
+    /// }
+    /// >>> [[[[ 7.6000004 10.        10.       ]
+    ///     [12.400001  10.        10.       ]
+    ///     [10.        10.        10.       ]]]]
+    /// 
+    /// 
+    /// 
+    /// fn example_resize_downsample_sizes_cubic_antialias() -> Tensor<FP16x16> {
+    ///     let mut data = TensorTrait::<
+    ///         FP16x16
+    ///     >::new(
+    ///         shape: array![1, 1, 4, 4].span(),
+    ///         data: array![
+    ///             FixedTrait::<FP16x16>::new(65536, false),
+    ///             FixedTrait::<FP16x16>::new(131072, false),
+    ///             FixedTrait::<FP16x16>::new(196608, false),
+    ///             FixedTrait::<FP16x16>::new(262144, false),
+    ///             FixedTrait::<FP16x16>::new(327680, false),
+    ///             FixedTrait::<FP16x16>::new(393216, false),
+    ///             FixedTrait::<FP16x16>::new(458752, false),
+    ///             FixedTrait::<FP16x16>::new(524288, false),
+    ///             FixedTrait::<FP16x16>::new(589824, false),
+    ///             FixedTrait::<FP16x16>::new(655360, false),
+    ///             FixedTrait::<FP16x16>::new(720896, false),
+    ///             FixedTrait::<FP16x16>::new(786432, false),
+    ///             FixedTrait::<FP16x16>::new(851968, false),
+    ///             FixedTrait::<FP16x16>::new(917504, false),
+    ///             FixedTrait::<FP16x16>::new(983040, false),
+    ///             FixedTrait::<FP16x16>::new(1048576, false),
+    ///         ]
+    ///             .span(),
+    ///     );
+    /// 
+    ///     let antialias = Option::Some(1);
+    /// 
+    ///     let mut sizes = array![1, 1, 3, 3].span();
+    ///     let sizes = Option::Some(sizes);
+    /// 
+    ///     return data.resize(
+    ///         Option::None,
+    ///         Option::None,
+    ///         sizes,
+    ///         antialias,
+    ///         Option::None,
+    ///         Option::None,
+    ///         Option::None,
+    ///         Option::None,
+    ///         Option::None,
+    ///         Option::None,
+    ///         Option::Some(MODE::CUBIC),
+    ///         Option::None,
+    ///     );
+    /// }
+    /// 
+    /// >>> [[[[ 1.7750092  3.1200073  4.4650054]
+    ///     [ 7.1550016  8.5        9.844998 ]
+    ///     [12.534994  13.8799925 15.224991 ]]]]
+    /// 
+    /// ```
+    ///
+    fn resize(
+        self: @Tensor<T>,
+        roi: Option<Tensor<T>>,
+        scales: Option<Span<T>>,
+        sizes: Option<Span<usize>>,
+        antialias: Option<usize>,
+        axes: Option<Span<usize>>,
+        coordinate_transformation_mode: Option<
+            orion::operators::tensor::math::resize::TRANSFORMATION_MODE
+        >,
+        cubic_coeff_a: Option<T>,
+        exclude_outside: Option<bool>,
+        extrapolation_value: Option<T>,
+        keep_aspect_ratio_policy: Option<
+            orion::operators::tensor::math::resize::KEEP_ASPECT_RATIO_POLICY
+        >,
+        mode: Option<orion::operators::tensor::math::resize::MODE>,
+        nearest_mode: Option<orion::operators::tensor::math::resize::NEAREST_MODE>,
+    ) -> Tensor<T>;
     /// #tensor.round
     ///
     /// ```rust
@@ -4147,43 +4390,6 @@ trait TensorTrait<T> {
     /// ```
     ///
     fn array_feature_extractor(self: @Tensor<T>, indices: Tensor<usize>) -> Tensor<T>;
-    /// # tensor.sequence_empty
-    ///
-    /// ```rust
-    ///    fn sequence_empty() -> Array<Tensor<T>>;
-    /// ```
-    ///
-    /// Returns an empty tensor sequence.
-    ///
-    /// ## Args
-    ///
-    /// ## Returns
-    ///
-    /// An empty `Array<Tensor<T>>` instance.
-    ///
-    /// ## Examples
-    ///
-    /// Let's create a new empty sequence.
-    ///
-    /// ```rust
-    /// use core::array::{ArrayTrait, SpanTrait};
-    ///
-    /// use orion::operators::tensor::{
-    ///     TensorTrait, // we import the trait
-    ///     Tensor, // we import the type
-    ///     U32Tensor // we import the implementation. 
-    /// };
-    ///
-    /// fn sequence_empty_example() -> Array<Tensor<u32>> {
-    ///     let sequence = TensorTrait::sequence_empty();
-    ///
-    ///     return sequence;
-    /// }
-    ///
-    /// >>> []
-    /// ```
-    ///
-    fn sequence_empty() -> Array<Tensor<T>>;
     /// # tensor.shrink
     /// 
     /// ```rust
@@ -4235,43 +4441,6 @@ trait TensorTrait<T> {
     /// ```
     ///
     fn shrink(self: Tensor<T>, bias: Option<T>, lambd: Option<T>) -> Tensor<T>;
-    /// ## tensor.sequence_construct
-    ///
-    /// ```rust 
-    ///    fn sequence_construct(tensors: Array<Tensor<T>>) -> Array<Tensor<T>>;
-    /// ```
-    ///
-    /// Constructs a tensor sequence containing the input tensors.
-    ///
-    /// ## Args
-    ///
-    /// * `tensors`(`Array<Tensor<T>>`) - The array of input tensors.
-    ///
-    /// ## Panics 
-    /// 
-    /// * Panics if input tensor array is empty.
-    ///
-    /// ## Returns
-    ///
-    /// A tensor sequence `Array<Tensor<T>>` containing the input tensors.
-    ///
-    /// ## Examples
-    ///
-    /// ```rust
-    /// use core::array::{ArrayTrait, SpanTrait};
-    /// 
-    /// use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};
-    ///
-    /// fn sequence_construct_example() -> Array<Tensor<usize>> {
-    ///     let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span());
-    ///     let tensor2 = TensorTrait::new(shape: array![2, 2].span(), data: array![4, 5, 6, 7].span());
-    ///     let result = TensorTrait::sequence_construct(tensors: array![tensor1, tensor2]);
-    ///     return result;
-    /// }
-    /// >>> [[0, 1, 2, 3], [4, 5, 6, 7]]
-    /// ```
-    ///
-    fn sequence_construct(tensors: Array<Tensor<T>>) -> Array<Tensor<T>>;
     /// ## tensor.reduce_mean
     ///
     /// ```rust 
@@ -4370,163 +4539,6 @@ trait TensorTrait<T> {
         keepdims: Option<bool>,
         noop_with_empty_axes: Option<bool>
     ) -> Tensor<T>;
-    /// # tensor.sequence_insert
-    ///
-    /// ```rust 
-    ///    fn sequence_insert(self: Array<Tensor<T>>, tensor: @Tensor<T>, position: Option<Tensor<i32>>) -> Array<Tensor<T>>;
-    /// ```
-    ///
-    /// Returns a tensor sequence that inserts 'tensor' into 'self' at 'position'.
-    ///
-    /// ## Args
-    ///
-    /// * `self`(`Array<Tensor<T>>`) - input sequence.
-    /// * `tensor` (`@Tensor<T>`) - the tensor to insert.
-    /// * `position` (`@Tensor<i32>`) - the index for insertion (default: -1).
-    ///
-    /// ## Returns
-    ///
-    /// Tensor sequence containing 'tensor' inserted into 'self' at 'position'.
-    ///
-    /// ## Examples
-    ///
-    /// Let's insert the tensor [2] into the sequence [[1], [3]] at position 1.
-    /// use orion::operators::tensor::{TensorTrait, Tensor, I32Tensor, U32Tensor};
-    ///
-    /// fn sequence_insert_example() -> Array<Tensor<u32>> {
-    ///     // Prepare sequence
-    ///     let mut sequence = ArrayTrait::new();
-    ///     let mut shape = ArrayTrait::<usize>::new();
-    ///     shape.append(1);
-    ///
-    ///     let mut data = ArrayTrait::new();
-    ///     data.append(1);
-    ///     sequence.append(TensorTrait::new(shape.span(), data.span()));
-    ///     let mut data = ArrayTrait::new();
-    ///     data.append(3);
-    ///
-    ///     sequence.append(TensorTrait::new(shape.span(), data.span()));
-    ///
-    ///     // Prepare input tensor
-    ///     let mut data = ArrayTrait::new();
-    ///     data.append(2);
-    ///     let tensor = TensorTrait::new(shape.span(), data.span());
-    ///
-    ///     // Prepare position
-    ///     let mut shape = ArrayTrait::<usize>::new();
-    ///     let mut data = ArrayTrait::<i32>::new();
-    ///     data.append(i32 { mag: 1, sign: false });
-    ///     let position = TensorTrait::<i32>::new(shape.span(), data.span())
-    ///
-    ///     let sequence = self.sequence_insert(tensor, Option::Some(position));
-    ///
-    ///     return sequence;
-    /// }
-    ///
-    /// >>> [[1], [2], [3]]
-    /// ```
-    ///
-    fn sequence_insert(
-        self: Array<Tensor<T>>, tensor: @Tensor<T>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<T>>;
-    /// ## tensor.sequence_at
-    ///
-    /// ```rust 
-    ///    fn sequence_at(sequence: Array<Tensor<T>>, position: Tensor<i32>) -> Tensor<T>;
-    /// ```
-    ///
-    /// Outputs the tensor at the specified position in the input sequence.
-    ///
-    /// ## Args
-    ///
-    /// * `tensors`(`Array<Tensor<T>>`) - The tensor sequence.
-    /// * `position`(`Tensor<i32>`) - The position tensor.
-    ///
-    /// ## Panics 
-    /// 
-    /// * Panics if position is not a scalar
-    /// * Panics if position is out of bounds [-n, n - 1]
-    ///
-    /// ## Returns
-    ///
-    /// The tensor `Tensor<T>` from the sequence at the specified position.
-    ///
-    /// ## Examples
-    ///
-    /// ```rust
-    /// use core::array::{ArrayTrait, SpanTrait};
-    /// 
-    /// use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor, I32Tensor};
-    /// use orion::numbers::{i32, IntegerTrait};
-    ///
-    /// fn sequence_at_example() -> Tensor<u32> {
-    ///     let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span());
-    ///     let tensor2 = TensorTrait::new(shape: array![2, 2].span(), data: array![4, 5, 6, 7].span());
-    ///     
-    ///     let mut sequence = ArrayTrait::new();
-    ///     sequence.append(tensor1);
-    ///     sequence.append(tensor2);
-    ///
-    ///     let position = TensorTrait::new(shape: array![].span(), data: array![IntegerTrait::new(1, false)].span());
-    ///
-    ///     let result = TensorTrait::sequence_at(sequence, position);
-    ///     return result;
-    /// }
-    /// >>> [4, 5, 6, 7]
-    /// ```
-    ///
-    fn sequence_at(sequence: Array<Tensor<T>>, position: Tensor<i32>) -> Tensor<T>;
-    /// ## tensor.sequence_erase
-    ///
-    /// ```rust 
-    ///    fn sequence_erase(sequence: Array<Tensor<T>>, position: Option<Tensor<i32>>) -> Array<Tensor<T>>;
-    /// ```
-    ///
-    /// Outputs the tensor sequence with the erased tensor at the specified position.
-    ///
-    /// ## Args
-    ///
-    /// * `tensors`(`Array<Tensor<T>>`) - The tensor sequence.
-    /// * `position`(`Option<Tensor<i32>>`) - The optional position tensor (by default erases the last tensor).
-    ///
-    /// ## Panics 
-    /// 
-    /// * Panics if position is not a scalar
-    /// * Panics if position is out of bounds [-n, n - 1]
-    ///
-    /// ## Returns
-    ///
-    /// The tensor sequence `Array<Tensor<T>>` with the erased tensor at the specified position.
-    ///
-    /// ## Examples
-    ///
-    /// ```rust
-    /// use core::array::{ArrayTrait, SpanTrait};
-    /// 
-    /// use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor, I32Tensor};
-    /// use orion::numbers::{i32, IntegerTrait};
-    ///
-    /// fn sequence_erase_example() -> Tensor<u32> {
-    ///     let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span());
-    ///     let tensor2 = TensorTrait::new(shape: array![2, 2].span(), data: array![4, 5, 6, 7].span());
-    ///     let tensor3 = TensorTrait::new(shape: array![2, 2].span(), data: array![8, 9, 10, 11].span());
-    ///     
-    ///     let mut sequence = ArrayTrait::new();
-    ///     sequence.append(tensor1);
-    ///     sequence.append(tensor2);
-    ///     sequence.append(tensor3);
-    ///
-    ///     let position = TensorTrait::new(shape: array![].span(), data: array![IntegerTrait::new(1, false)].span());
-    ///
-    ///     let result = TensorTrait::sequence_erase(sequence, position);
-    ///     return result;
-    /// }
-    /// >>> [[0, 1, 2, 3], [8, 9, 10, 11]]
-    /// ```
-    ///
-    fn sequence_erase(
-        sequence: Array<Tensor<T>>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<T>>;
     /// #tensor.pow
     ///
     /// ```rust
@@ -4596,44 +4608,6 @@ trait TensorTrait<T> {
     /// ```
     ///
     fn pow(self: @Tensor<T>, other: @Tensor<T>) -> Tensor<T>;
-    /// # tensor.sequence_length
-    ///
-    /// ```rust
-    ///    fn sequence_length(self: Array<Tensor<T>>) -> Tensor<u32>;
-    /// ```
-    ///
-    /// Returns the length of the input sequence.
-    ///
-    /// ## Args
-    ///
-    /// * `self`(`Array<Tensor<T>>`) - The input sequence.
-    ///
-    /// ## Returns
-    ///
-    /// The length of the sequence as scalar, i.e. a tensor of shape [].
-    ///
-    /// ## Examples
-    ///
-    /// Let's create new u32 Tensor with constant 42.
-    ///
-    /// ```rust
-    /// let mut sequence = ArrayTrait::new();
-    ///
-    /// let mut shape = ArrayTrait::<usize>::new();
-    /// shape.append(1);
-    /// shape.append(2);
-    ///
-    /// let mut data = ArrayTrait::new();
-    /// data.append(3);
-    /// data.append(1);
-    ///
-    /// sequence.append(TensorTrait::new(shape.span(), data.span()));
-    ///
-    /// sequence.sequence_length()
-    /// >>> [1]
-    /// ```
-    ///
-    fn sequence_length(self: Array<Tensor<T>>) -> Tensor<u32>;
     /// ## tensor.reduce_prod
     /// 
     /// ```rust 
@@ -4752,69 +4726,6 @@ trait TensorTrait<T> {
     /// ```
     ///
     fn is_nan(self: @Tensor<T>) -> Tensor<bool>;
-    /// # tensor.concat_from_sequence
-    ///
-    /// ```rust 
-    ///    fn concat_from_sequence(sequence: Array<Tensor<T>>, axis: i32, new_axis: Option<usize>) -> Tensor<T>;
-    /// ```
-    ///
-    /// Concatenate a sequence of tensors into a single tensor.
-    ///
-    /// ## Args
-    ///
-    /// * `sequence`(`Array<Tensor<T>>`) - The input sequence.
-    /// * `axis`(`i32`) -  Axis to concat on.
-    /// * `new_axis`(`Option<usize>`) -  Optionally added new axis.
-    ///
-    /// ## Panics
-    ///
-    /// * Panics if new_axis not 0 or 1 (if value provided).
-    /// * Panics if axis not in accepted ranges.
-    /// * Panics if sequence length is not greater than 1.
-    ///
-    /// ## Returns 
-    ///
-    /// A new `Tensor<T>` concatenated tensor from the input tensor sequence.
-    ///
-    /// ## Example
-    ///
-    /// ```rust
-    /// use core::array::{ArrayTrait, SpanTrait};
-    /// 
-    /// use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};
-    /// 
-    /// fn concat_example() -> Tensor<u32> {
-    ///     let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span(),);
-    ///     let tensor2 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span(),);
-    ///
-    ///     let mut sequence = ArrayTrait::new();
-    ///     sequence.append(tensor1);
-    ///     sequence.append(tensor2);
-    ///
-    ///     let result = TensorTrait::concat_from_sequence(sequence: sequence, axis: 0, new_axis: Option::Some(0));
-    ///     return result;
-    /// }
-    /// >>> [[0. 1.]
-    ///      [2. 3.],
-    ///      [0. 1.]
-    ///      [2. 3.]]
-    ///
-    ///     result.shape
-    /// >>> (4, 2)
-    ///
-    ///    let result = TensorTrait::concat_from_sequence(sequence: sequence, axis: 1, new_axis: Option::Some(0));
-    ///    return result;
-    /// }
-    /// >>> [[0. 1., 0., 1.]
-    ///      [2. 3., 2., 3.]]
-    ///
-    ///     result.shape
-    /// >>> (2, 4 ) 
-    /// ```
-    ///
-    fn concat_from_sequence(
-        sequence: Array<Tensor<T>>, axis: i32, new_axis: Option<usize>
-    ) -> Tensor<T>;
     /// #tensor.not
     /// 
     /// ```rust
@@ -5067,7 +4978,7 @@ trait TensorTrait<T> {
     /// 
     ///     return tensor.gather_nd(
     ///         indices: indices, 
-    ///         axis: Option::None((0)), 
+    ///         axis: Option::Some((0)), 
     ///     );
     /// }
     /// >>> [[0, 1],
@@ -5077,6 +4988,196 @@ trait TensorTrait<T> {
     /// ```
     ///
     fn gather_nd(self: @Tensor<T>, indices: Tensor<usize>, batch_dims: Option<usize>) -> Tensor<T>;
+    /// # tensor.compress
+    ///
+    /// ```rust 
+    ///    fn compress(self: @Tensor<T>, condition: Tensor<T>, axis: Option<usize>) -> Tensor<T>;
+    /// ```
+    ///
+    /// Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index. In case axis is not provided, input is flattened before elements are selected.
+    ///
+    /// ## Args
+    ///
+    /// * `self`(`@Tensor<T>`) - The input tensor.
+    /// * `condition`(`Tensor<T>`) - Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.
+    /// * `axis`(`Option<usize>`) - (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).
+    ///
+    /// ## Panics
+    ///
+    /// * Panics if condition rank is not equal to 1.
+    ///
+    /// ## Returns 
+    ///
+    /// A new `Tensor<T>` .
+    /// fn compress_example() -> Tensor<u32> {
+    ///     let tensor = TensorTrait::<u32>::new(
+    ///         shape: array![3, 2].span(), 
+    ///         data: array![[1, 2], [3, 4], [5, 6]].span(), 
+    ///     );
+    ///     let condition = TensorTrait::<u32>::new(
+    ///         shape: array![3].span(), 
+    ///         data: array![0, 1, 1].span(), 
+    ///     );
+    /// 
+    ///     return tensor.compress(
+    ///         condition: condition, 
+    ///         axis: Option::Some((0)), 
+    ///     );
+    /// }
+    /// >>> [[3, 4],
+    ///      [5, 6]]
+    /// ```
+    ///
+    fn compress(self: @Tensor<T>, condition: Tensor<usize>, axis: Option<usize>) -> Tensor<T>;
+    /// # tensor.layer_normalization
+    ///
+    /// ```rust 
+    ///    fn layer_normalization(
+    ///     self: @Tensor<T>,
+    ///     scale: @Tensor<T>,
+    ///     B: Option<@Tensor<T>>,
+    ///     axis: Option<i32>,
+    ///     epsilon: Option<T>,
+    ///     stash_type: Option<usize>,
+    /// ) -> (Tensor<T>, Tensor<T>, Tensor<T>);
+    /// ```
+    ///
+    /// Layer normalization of the input, in two stages.
+    /// The first stage is standardization, which makes the normalized elements have zero mean and unit variances.
+    /// The second stage then scales and shifts the outcome of the first stage 
+    /// ## Args
+    ///
+    /// * `self`(`@Tensor<T>`) - The input tensor.
+    /// * `scale`(`@Tensor<T>,`) - Scale tensor.
+    /// * `B`(`Option<@Tensor<T>>`) - Bias tensor. 
+    /// * `axis`(`Option<i32>`) (default is -1) - The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r). Negative value means counting dimensions from the back.
+    /// * `epsilon`(`Option<T>`) (default is 0) - The epsilon value to use to avoid division by zero.
+    /// * `stash_type`(`Option<usize>`) - Precise the computation precision - unused the precision is defined by the type of the tensor.
+    /// ## Panics
+    ///
+    /// * Panics if condition rank is not equal to 1.
+    ///
+    /// ## Returns 
+    ///
+    /// A new normalized tensor`Tensor<T>`.
+    /// A tensor containing the mean `Tensor<T>`.
+    /// A tensor containing the inverse standard deviation `Tensor<T>`.
+    ///
+    /// ## Example
+    ///
+    /// ```rust
+    /// use orion::operators::tensor::{TensorTrait, Tensor};
+    /// use orion::operators::tensor::FP16x16TensorPartialEq;
+    /// use core::array::{ArrayTrait, SpanTrait};
+    /// use orion::operators::tensor::FP16x16Tensor;
+    /// use orion::numbers::{FixedTrait, FP16x16};
+    /// 
+    /// fn layer_normalization_example() -> (Tensor<FP16x16>, Tensor<FP16x16>, Tensor<FP16x16>) {
+    ///     let mut shape = ArrayTrait::<usize>::new();
+    ///     shape.append(3);
+    ///     shape.append(4);
+    /// 
+    ///     let mut data = ArrayTrait::new();
+    ///     data.append(FP16x16 { mag: 41143, sign: true });
+    ///     data.append(FP16x16 { mag: 51803, sign: false });
+    ///     data.append(FP16x16 { mag: 113556, sign: false });
+    ///     data.append(FP16x16 { mag: 64774, sign: false });
+    ///     data.append(FP16x16 { mag: 866, sign: false });
+    ///     data.append(FP16x16 { mag: 698, sign: true });
+    ///     data.append(FP16x16 { mag: 106500, sign: false });
+    ///     data.append(FP16x16 { mag: 98929, sign: false });
+    ///     data.append(FP16x16 { mag: 7551, sign: false });
+    ///     data.append(FP16x16 { mag: 30689, sign: true });
+    ///     data.append(FP16x16 { mag: 38325, sign: false });
+    ///     data.append(FP16x16 { mag: 48164, sign: false });
+    ///     let X = TensorTrait::new(shape.span(), data.span());
+    /// 
+    ///     let shape = ArrayTrait::<usize>::new();
+    ///     shape.append(4);
+    ///     let mut data = ArrayTrait::new();
+    ///     data.append(FP16x16 { mag: 49855, sign: false });
+    ///     data.append(FP16x16 { mag: 150787, sign: false });
+    ///     data.append(FP16x16 { mag: 83498, sign: true });
+    ///     data.append(FP16x16 { mag: 30346, sign: false });
+    ///     let scale = TensorTrait::new(shape.span(), data.span());
+    /// 
+    ///      
+    ///     let mut shape = ArrayTrait::<usize>::new();
+    ///     shape.append(4);
+    ///     let mut data = ArrayTrait::new();
+    ///     data.append(FP16x16 { mag: 54864, sign: true });
+    ///     data.append(FP16x16 { mag: 50952, sign: false });
+    ///     data.append(FP16x16 { mag: 8870, sign: true });
+    ///     data.append(FP16x16 { mag: 23216, sign: true });
+    ///     let bias = TensorTrait::new(shape.span(), data.span());
+    /// 
+    ///     return X.layer_normalization(@scale,Option::Some(@bias),Option::None,Option::None,Option::None);
+    /// }
+    /// >>> [[-0.48926553  1.0185822  -0.02138367 -0.39223218]
+    ///      [-0.7945549   0.99696046  0.04332176 -0.412645  ]
+    ///      [-0.5664707   0.7491956  -0.7896356  -0.5320859 ]]
+    /// 
+    /// ``` 
+    ///
+    fn layer_normalization(
+        self: @Tensor<T>,
+        scale: @Tensor<T>,
+        B: Option<@Tensor<T>>,
+        axis: Option<i32>,
+        epsilon: Option<T>,
+        stash_type: Option<usize>,
+    ) -> (Tensor<T>, Tensor<T>, Tensor<T>);
+    /// # tensor.split
+    ///
+    /// ```rust 
+    ///    fn split(self: @Tensor<T>, axis: usize, num_outputs: Option<usize>, split: Option<Tensor<usize>>
+    ///    ) -> Array<Tensor<T>>;
+    /// ```
+    ///
+    /// Split a tensor into a list of tensors, along the specified ‘axis’
+    ///
+    ///
+    /// * `self`(`@Tensor<T>`) - The input tensor.
+    /// * `axis`(`usize`) - The axis along which to split on.
+    /// * `num_outputs `(Option<usize>) - Number of outputs to split parts of the tensor into. 
+    /// * `split  `(Option<Tensor<usize>>) - Optional length of each output.
+    ///
+    /// ## Panics
+    ///
+    /// * Panics if the 'axis' accepted range is not [-rank, rank-1] where r = rank(input).
+    /// * Panics if the 'split' values not >= 0. Sum of the values is not equal to the dim value at ‘axis’ specified.
+    /// * Panics if the input 'split' or the attribute 'num_outputs' both are specified or not.
+    ///
+    /// ## Returns
+    ///
+    /// One or more outputs forming list of tensors after splitting.
+    ///
+    /// ## Examples
+    /// 
+    /// ```rust
+    /// use core::array::{ArrayTrait, SpanTrait};
+    /// use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};
+    /// use core::option::OptionTrait;
+    /// fn split_tensor_example() -> Array<Tensor<u32>> {
+    ///     let tensor: Tensor<u32> = TensorTrait::<u32>::new(
+    ///         shape: array![2,4].span(), 
+    ///         data: array![
+    ///             0, 1, 2, 3, 4, 5, 6, 7
+    ///             ].span(),
+    ///     );
+    ///     let num_outputs = Option::Some(2);
+    ///     // split = Option::Some(array![1, 1].span());
+    ///     let split_num: Option<Tensor<usize>> = Option::None(());
+    ///     // We can call `split` function as follows.
+    ///     return tensor.split(0, num_outputs, split_num);
+    /// }
+    /// >>> [[0,1],[4,5]]
+    ///     [[2,3],[6,7]]
+    /// ```
+    ///
+    fn split(
+        self: @Tensor<T>, axis: usize, num_outputs: Option<usize>, spl: Option<Tensor<usize>>
+    ) -> Array<Tensor<T>>;
 }
 
 /// Cf: TensorTrait::new docstring
diff --git a/src/operators/tensor/implementations.cairo b/src/operators/tensor/implementations.cairo
index 61ee08dd3..f9a7b406f 100644
--- a/src/operators/tensor/implementations.cairo
+++ b/src/operators/tensor/implementations.cairo
@@ -8,3 +8,4 @@ mod tensor_fp64x64;
 mod tensor_fp32x32;
 mod tensor_fp16x16wide;
 mod tensor_fp8x23wide;
+mod tensor_complex64;
diff --git a/src/operators/tensor/implementations/tensor_bool.cairo b/src/operators/tensor/implementations/tensor_bool.cairo
index d2afe3fc5..eaeda00d5 100644
--- a/src/operators/tensor/implementations/tensor_bool.cairo
+++ b/src/operators/tensor/implementations/tensor_bool.cairo
@@ -380,26 +380,10 @@ impl BoolTensor of TensorTrait<bool> {
         math::gather_elements::gather_elements(self, indices, axis)
     }
 
-    fn sequence_length(self: Array<Tensor<bool>>) -> Tensor<u32> {
-        math::sequence_length::sequence_length(self)
-    }
-
-    fn sequence_at(sequence: Array<Tensor<bool>>, position: Tensor<i32>) -> Tensor<bool> {
-        math::sequence_at::sequence_at(sequence, position)
-    }
-
-    fn sequence_construct(tensors: Array<Tensor<bool>>) -> Array<Tensor<bool>> {
-        math::sequence_construct::sequence_construct(tensors)
-    }
-
     fn shrink(self: Tensor<bool>, bias: Option<bool>, lambd: Option<bool>) -> Tensor<bool> {
         panic(array!['not supported!'])
     }
 
-    fn sequence_empty() -> Array<Tensor<bool>> {
-        math::sequence_empty::sequence_empty::<bool>()
-    }
-
     fn reduce_mean(
         self: @Tensor<bool>,
         axes: Option<Span<usize>>,
@@ -430,18 +414,6 @@ impl BoolTensor of TensorTrait<bool> {
         panic(array!['not supported!'])
     }
 
-    fn sequence_erase(
-        sequence: Array<Tensor<bool>>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<bool>> {
-        math::sequence_erase::sequence_erase(sequence, position)
-    }
-
-    fn sequence_insert(
-        self: Array<Tensor<bool>>, tensor: @Tensor<bool>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<bool>> {
-        math::sequence_insert::sequence_insert(self, tensor, position)
-    }
-
     fn is_inf(
         self: @Tensor<bool>, detect_negative: Option<u8>, detect_positive: Option<u8>
     ) -> Tensor<bool> {
@@ -452,12 +424,6 @@ impl BoolTensor of TensorTrait<bool> {
         panic(array!['not supported!'])
     }
 
-    fn concat_from_sequence(
-        sequence: Array<Tensor<bool>>, axis: i32, new_axis: Option<usize>
-    ) -> Tensor<bool> {
-        panic(array!['not supported!'])
-    }
-
     fn erf(self: @Tensor<bool>) -> Tensor<bool> {
         panic(array!['not supported!'])
     }
@@ -472,9 +438,52 @@ impl BoolTensor of TensorTrait<bool> {
         panic(array!['not supported!'])
     }
 
-    fn gather_nd(self: @Tensor<bool>, indices: Tensor<usize>, batch_dims: Option<usize>) -> Tensor<bool> {
+    fn gather_nd(
+        self: @Tensor<bool>, indices: Tensor<usize>, batch_dims: Option<usize>
+    ) -> Tensor<bool> {
         math::gather_nd::gather_nd(self, indices, batch_dims)
     }
+
+    fn compress(
+        self: @Tensor<bool>, condition: Tensor<usize>, axis: Option<usize>
+    ) -> Tensor<bool> {
+        math::compress::compress(self, condition, axis)
+    }
+
+    fn layer_normalization(
+        self: @Tensor<bool>,
+        scale: @Tensor<bool>,
+        B: Option<@Tensor<bool>>,
+        axis: Option<i32>,
+        epsilon: Option<bool>,
+        stash_type: Option<usize>,
+    ) -> (Tensor<bool>, Tensor<bool>, Tensor<bool>) {
+        panic(array!['not supported!'])
+    }
+
+    fn resize(
+        self: @Tensor<bool>,
+        roi: Option<Tensor<bool>>,
+        scales: Option<Span<bool>>,
+        sizes: Option<Span<usize>>,
+        antialias: Option<usize>,
+        axes: Option<Span<usize>>,
+        coordinate_transformation_mode: Option<math::resize::TRANSFORMATION_MODE>,
+        cubic_coeff_a: Option<bool>,
+        exclude_outside: Option<bool>,
+        extrapolation_value: Option<bool>,
+        keep_aspect_ratio_policy: Option<math::resize::KEEP_ASPECT_RATIO_POLICY>,
+        mode: Option<math::resize::MODE>,
+        nearest_mode: Option<math::resize::NEAREST_MODE>,
+    ) -> Tensor<bool> {
+        panic(array!['not supported!'])
+    }
+
+    fn split(
+        self: @Tensor<bool>, axis: usize, num_outputs: Option<usize>, spl: Option<Tensor<usize>>
+    ) -> Array<Tensor<bool>> {
+        panic(array!['not supported!'])
+    }
 }
 
 /// Implements partial equal for two `Tensor<bool>` using the `PartialEq` trait.
diff --git a/src/operators/tensor/implementations/tensor_complex64.cairo b/src/operators/tensor/implementations/tensor_complex64.cairo
new file mode 100644
index 000000000..52d916b14
--- /dev/null
+++ b/src/operators/tensor/implementations/tensor_complex64.cairo
@@ -0,0 +1,624 @@
+use core::array::ArrayTrait;
+use core::array::SpanTrait;
+use core::option::OptionTrait;
+use core::traits::{TryInto, Into};
+
+use orion::numbers::fixed_point::core::FixedTrait;
+use orion::operators::tensor::core::{
+    new_tensor, constant_of_shape, stride, Tensor, TensorTrait, ravel_index, unravel_index, reshape,
+    at_tensor,
+};
+use orion::operators::tensor::{math, linalg, quantization, core as core_tensor, ml};
+use orion::numbers::{i8, i32, NumberTrait, FP64x64, FP64x64Impl};
+use orion::numbers::fixed_point::implementations::fp64x64::core::ONE;
+use orion::operators::tensor::implementations::{
+    tensor_i8::I8Tensor, tensor_u32::U32Tensor, tensor_bool::BoolTensor
+};
+use orion::numbers::complex_number::complex_trait::ComplexTrait;
+use orion::numbers::complex_number::complex64::{Complex64Impl, complex64};
+
+
+impl Complex64Tensor of TensorTrait<complex64> {
+    fn new(shape: Span<usize>, data: Span<complex64>) -> Tensor<complex64> {
+        new_tensor(shape, data)
+    }
+
+    fn constant_of_shape(shape: Span<usize>, value: complex64) -> Tensor<complex64> {
+        constant_of_shape(shape, value)
+    }
+
+    fn at(self: @Tensor<complex64>, indices: Span<usize>) -> complex64 {
+        *at_tensor(self, indices)
+    }
+
+    fn add(lhs: Tensor<complex64>, rhs: Tensor<complex64>) -> Tensor<complex64> {
+        math::arithmetic::add(@lhs, @rhs)
+    }
+
+    fn sub(lhs: Tensor<complex64>, rhs: Tensor<complex64>) -> Tensor<complex64> {
+        math::arithmetic::sub(@lhs, @rhs)
+    }
+
+    fn mul(lhs: Tensor<complex64>, rhs: Tensor<complex64>) -> Tensor<complex64> {
+        math::arithmetic::mul(@lhs, @rhs)
+    }
+
+    fn div(lhs: Tensor<complex64>, rhs: Tensor<complex64>) -> Tensor<complex64> {
+        math::arithmetic::div(@lhs, @rhs)
+    }
+
+    fn min_in_tensor(self: @Tensor<complex64>) -> complex64 {
+        panic(array!['not supported!'])
+    }
+
+    fn min(tensors: Span<Tensor<complex64>>) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn max_in_tensor(self: @Tensor<complex64>) -> complex64 {
+        panic(array!['not supported!'])
+    }
+
+    fn max(tensors: Span<Tensor<complex64>>) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn stride(self: @Tensor<complex64>) -> Span<usize> {
+        stride(*self.shape)
+    }
+
+    fn ravel_index(self: @Tensor<complex64>, indices: Span<usize>) -> usize {
+        ravel_index(*self.shape, indices)
+    }
+
+    fn unravel_index(self: @Tensor<complex64>, index: usize) -> Span<usize> {
+        unravel_index(index, *self.shape)
+    }
+
+    fn reshape(self: @Tensor<complex64>, target_shape: Span<usize>) -> Tensor<complex64> {
+        reshape(self, target_shape)
+    }
+
+    fn reduce_sum(self: @Tensor<complex64>, axis: usize, keepdims: bool) -> Tensor<complex64> {
+        math::reduce_sum::reduce_sum(self, axis, keepdims)
+    }
+
+    fn reduce_prod(self: @Tensor<complex64>, axis: usize, keepdims: bool) -> Tensor<complex64> {
+        math::reduce_prod::reduce_prod(self, axis, keepdims)
+    }
+
+    fn argmax(
+        self: @Tensor<complex64>,
+        axis: usize,
+        keepdims: Option<bool>,
+        select_last_index: Option<bool>
+    ) -> Tensor<usize> {
+        panic(array!['not supported!'])
+    }
+
+    fn argmin(
+        self: @Tensor<complex64>,
+        axis: usize,
+        keepdims: Option<bool>,
+        select_last_index: Option<bool>
+    ) -> Tensor<usize> {
+        panic(array!['not supported!'])
+    }
+
+    fn transpose(self: @Tensor<complex64>, axes: Span<usize>) -> Tensor<complex64> {
+        linalg::transpose::transpose(self, axes)
+    }
+
+    fn matmul(self: @Tensor<complex64>, other: @Tensor<complex64>) -> Tensor<complex64> {
+        linalg::matmul::matmul(self, other)
+    }
+
+    fn exp(self: @Tensor<complex64>) -> Tensor<complex64> {
+        math::exp::exp(*self)
+    }
+
+    fn log(self: @Tensor<complex64>) -> Tensor<complex64> {
+        math::log::log(*self)
+    }
+
+    fn equal(self: @Tensor<complex64>, other: @Tensor<complex64>) -> Tensor<usize> {
+        math::equal::equal(self, other)
+    }
+
+    fn greater(self: @Tensor<complex64>, other: @Tensor<complex64>) -> Tensor<usize> {
+        panic(array!['not supported!'])
+    }
+
+    fn greater_equal(self: @Tensor<complex64>, other: @Tensor<complex64>) -> Tensor<usize> {
+        panic(array!['not supported!'])
+    }
+
+    fn less(self: @Tensor<complex64>, other: @Tensor<complex64>) -> Tensor<usize> {
+        panic(array!['not supported!'])
+    }
+
+    fn less_equal(self: @Tensor<complex64>, other: @Tensor<complex64>) -> Tensor<usize> {
+        panic(array!['not supported!'])
+    }
+
+    fn abs(self: @Tensor<complex64>) -> Tensor<complex64> {
+        math::abs::abs(*self)
+    }
+
+    fn neg(self: @Tensor<complex64>) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn ceil(self: @Tensor<complex64>) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn sin(self: @Tensor<complex64>) -> Tensor<complex64> {
+        math::sin::sin(*self)
+    }
+
+    fn cos(self: @Tensor<complex64>) -> Tensor<complex64> {
+        math::cos::cos(*self)
+    }
+
+    fn asin(self: @Tensor<complex64>) -> Tensor<complex64> {
+        math::asin::asin(*self)
+    }
+
+    fn cumsum(
+        self: @Tensor<complex64>, axis: usize, exclusive: Option<bool>, reverse: Option<bool>
+    ) -> Tensor<complex64> {
+        math::cumsum::cumsum(self, axis, exclusive, reverse)
+    }
+
+    fn flatten(self: @Tensor<complex64>, axis: usize) -> Tensor<complex64> {
+        math::flatten::flatten(self, axis)
+    }
+
+    fn sinh(self: @Tensor<complex64>) -> Tensor<complex64> {
+        math::sinh::sinh(*self)
+    }
+
+    fn tanh(self: @Tensor<complex64>) -> Tensor<complex64> {
+        math::tanh::tanh(*self)
+    }
+
+    fn cosh(self: @Tensor<complex64>) -> Tensor<complex64> {
+        math::cosh::cosh(*self)
+    }
+
+    fn acosh(self: @Tensor<complex64>) -> Tensor<complex64> {
+        math::acosh::acosh(*self)
+    }
+
+    fn asinh(self: @Tensor<complex64>) -> Tensor<complex64> {
+        math::asinh::asinh(*self)
+    }
+
+    fn atan(self: @Tensor<complex64>) -> Tensor<complex64> {
+        math::atan::atan(*self)
+    }
+
+    fn xor(self: @Tensor<complex64>, other: @Tensor<complex64>) -> Tensor<usize> {
+        panic(array!['not supported!'])
+    }
+
+    fn or(self: @Tensor<complex64>, other: @Tensor<complex64>) -> Tensor<usize> {
+        panic(array!['not supported!'])
+    }
+
+    fn acos(self: @Tensor<complex64>) -> Tensor<complex64> {
+        math::acos::acos(*self)
+    }
+
+    fn onehot(
+        self: @Tensor<complex64>, depth: usize, axis: Option<usize>, values: Span<usize>
+    ) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn sqrt(self: @Tensor<complex64>) -> Tensor<complex64> {
+        math::sqrt::sqrt(*self)
+    }
+
+    fn concat(tensors: Span<Tensor<complex64>>, axis: usize,) -> Tensor<complex64> {
+        math::concat::concat(tensors, axis)
+    }
+
+    fn quantize_linear(
+        self: @Tensor<complex64>, y_scale: @Tensor<complex64>, y_zero_point: @Tensor<complex64>
+    ) -> Tensor::<i8> {
+        panic(array!['not supported!'])
+    }
+
+    fn dequantize_linear(
+        self: @Tensor<i8>, x_scale: @Tensor<complex64>, x_zero_point: @Tensor<complex64>
+    ) -> Tensor::<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn qlinear_add(
+        self: @Tensor<i8>,
+        a_scale: @Tensor<complex64>,
+        a_zero_point: @Tensor<complex64>,
+        b: @Tensor<i8>,
+        b_scale: @Tensor<complex64>,
+        b_zero_point: @Tensor<complex64>,
+        y_scale: @Tensor<complex64>,
+        y_zero_point: @Tensor<complex64>
+    ) -> Tensor::<i8> {
+        panic(array!['not supported!'])
+    }
+
+    fn qlinear_mul(
+        self: @Tensor<i8>,
+        a_scale: @Tensor<complex64>,
+        a_zero_point: @Tensor<complex64>,
+        b: @Tensor<i8>,
+        b_scale: @Tensor<complex64>,
+        b_zero_point: @Tensor<complex64>,
+        y_scale: @Tensor<complex64>,
+        y_zero_point: @Tensor<complex64>
+    ) -> Tensor::<i8> {
+        panic(array!['not supported!'])
+    }
+
+    fn qlinear_matmul(
+        self: @Tensor<i8>,
+        a_scale: @Tensor<complex64>,
+        a_zero_point: @Tensor<complex64>,
+        b: @Tensor<i8>,
+        b_scale: @Tensor<complex64>,
+        b_zero_point: @Tensor<complex64>,
+        y_scale: @Tensor<complex64>,
+        y_zero_point: @Tensor<complex64>
+    ) -> Tensor::<i8> {
+        panic(array!['not supported!'])
+    }
+
+    fn qlinear_concat(
+        tensors: Span<Tensor<i8>>,
+        scales: Span<Tensor<complex64>>,
+        zero_points: Span<Tensor<complex64>>,
+        y_scale: @Tensor<complex64>,
+        y_zero_point: @Tensor<complex64>,
+        axis: usize
+    ) -> Tensor::<i8> {
+        panic(array!['not supported!'])
+    }
+
+    fn qlinear_leakyrelu(
+        self: @Tensor<i8>,
+        a_scale: @Tensor<complex64>,
+        a_zero_point: @Tensor<complex64>,
+        alpha: complex64
+    ) -> Tensor::<i8> {
+        panic(array!['not supported!'])
+    }
+
+    fn slice(
+        self: @Tensor<complex64>,
+        starts: Span<usize>,
+        ends: Span<usize>,
+        axes: Option<Span<usize>>,
+        steps: Option<Span<usize>>
+    ) -> Tensor<complex64> {
+        core_tensor::slice::<complex64>(self, starts, ends, axes, steps)
+    }
+
+    fn gather(
+        self: @Tensor<complex64>, indices: Tensor<usize>, axis: Option<usize>
+    ) -> Tensor<complex64> {
+        math::gather::gather(self, indices, axis)
+    }
+
+    fn gather_nd(
+        self: @Tensor<complex64>, indices: Tensor<usize>, batch_dims: Option<usize>
+    ) -> Tensor<complex64> {
+        math::gather_nd::gather_nd(self, indices, batch_dims)
+    }
+
+    fn nonzero(self: @Tensor<complex64>) -> Tensor<usize> {
+        core_tensor::nonzero(self)
+    }
+
+    fn squeeze(self: @Tensor<complex64>, axes: Option<Span<i32>>) -> Tensor<complex64> {
+        core_tensor::squeeze(self, axes)
+    }
+
+    fn unsqueeze(self: @Tensor<complex64>, axes: Span<usize>) -> Tensor<complex64> {
+        core_tensor::unsqueeze(self, axes)
+    }
+
+    fn sign(self: @Tensor<complex64>) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn clip(
+        self: @Tensor<complex64>, min: Option<complex64>, max: Option<complex64>
+    ) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn and(self: @Tensor<bool>, other: @Tensor<bool>) -> Tensor<bool> {
+        math::and::and(self, other)
+    }
+
+    fn identity(self: @Tensor<complex64>) -> Tensor<complex64> {
+        core_tensor::identity(self)
+    }
+
+    fn where(
+        self: @Tensor<complex64>, x: @Tensor<complex64>, y: @Tensor<complex64>
+    ) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn bitwise_and(self: @Tensor<complex64>, other: @Tensor<complex64>) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn bitwise_xor(self: @Tensor<complex64>, other: @Tensor<complex64>) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn bitwise_or(self: @Tensor<complex64>, other: @Tensor<complex64>) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn round(self: @Tensor<complex64>) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn reduce_l1(self: @Tensor<complex64>, axis: usize, keepdims: bool) -> Tensor<complex64> {
+        math::reduce_l1::reduce_l1(self, axis, keepdims)
+    }
+
+    fn array_feature_extractor(
+        self: @Tensor<complex64>, indices: Tensor<usize>
+    ) -> Tensor<complex64> {
+        ml::array_feature_extractor::array_feature_extractor(*self, indices)
+    }
+
+    fn binarizer(self: @Tensor<complex64>, threshold: Option<complex64>) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn reduce_sum_square(
+        self: @Tensor<complex64>, axis: usize, keepdims: bool
+    ) -> Tensor<complex64> {
+        math::reduce_sum_square::reduce_sum_square(self, axis, keepdims)
+    }
+
+    fn reduce_l2(self: @Tensor<complex64>, axis: usize, keepdims: bool) -> Tensor<complex64> {
+        math::reduce_l2::reduce_l2_complex(self, axis, keepdims)
+    }
+
+    fn trilu(self: @Tensor<complex64>, upper: bool, k: i64) -> Tensor<complex64> {
+        linalg::trilu::trilu(self, upper, k)
+    }
+
+    fn scatter(
+        self: @Tensor<complex64>,
+        updates: Tensor<complex64>,
+        indices: Tensor<usize>,
+        axis: Option<usize>,
+        reduction: Option<usize>
+    ) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn not(self: @Tensor<complex64>) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+
+    fn gather_elements(
+        self: @Tensor<complex64>, indices: Tensor<usize>, axis: Option<usize>
+    ) -> Tensor<complex64> {
+        math::gather_elements::gather_elements(self, indices, axis)
+    }
+
+    fn shrink(
+        self: Tensor<complex64>, bias: Option<complex64>, lambd: Option<complex64>
+    ) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn reduce_mean(
+        self: @Tensor<complex64>,
+        axes: Option<Span<usize>>,
+        keepdims: Option<bool>,
+        noop_with_empty_axes: Option<bool>
+    ) -> Tensor<complex64> {
+        math::reduce_mean::reduce_mean(self, axes, keepdims, noop_with_empty_axes)
+    }
+
+    fn reduce_min(
+        self: @Tensor<complex64>,
+        axes: Option<Span<usize>>,
+        keepdims: Option<bool>,
+        noop_with_empty_axes: Option<bool>
+    ) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn pow(self: @Tensor<complex64>, other: @Tensor<complex64>) -> Tensor<complex64> {
+        math::pow::pow(self, other)
+    }
+
+    fn is_inf(
+        self: @Tensor<complex64>, detect_negative: Option<u8>, detect_positive: Option<u8>
+    ) -> Tensor<bool> {
+        panic(array!['not supported!'])
+    }
+
+    fn is_nan(self: @Tensor<complex64>) -> Tensor<bool> {
+        panic(array!['not supported!'])
+    }
+
+    fn reduce_log_sum(self: @Tensor<complex64>, axis: usize, keepdims: bool) -> Tensor<complex64> {
+        math::reduce_log_sum::reduce_log_sum(self, axis, keepdims)
+    }
+
+
+    fn erf(self: @Tensor<complex64>) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+
+    fn unique(
+        self: @Tensor<complex64>, axis: Option<usize>, sorted: Option<bool>
+    ) -> (Tensor<complex64>, Tensor<i32>, Tensor<i32>, Tensor<i32>) {
+        panic(array!['not supported!'])
+    }
+
+    fn compress(
+        self: @Tensor<complex64>, condition: Tensor<usize>, axis: Option<usize>
+    ) -> Tensor<complex64> {
+        math::compress::compress(self, condition, axis)
+    }
+
+    fn layer_normalization(
+        self: @Tensor<complex64>,
+        scale: @Tensor<complex64>,
+        B: Option<@Tensor<complex64>>,
+        axis: Option<i32>,
+        epsilon: Option<complex64>,
+        stash_type: Option<usize>,
+    ) -> (Tensor<complex64>, Tensor<complex64>, Tensor<complex64>) {
+        panic(array!['not supported!'])
+    }
+
+    fn split(
+        self: @Tensor<complex64>,
+        axis: usize,
+        num_outputs: Option<usize>,
+        spl: Option<Tensor<usize>>
+    ) -> Array<Tensor<complex64>> {
+        panic(array!['not supported!'])
+    }
+
+    fn resize(
+        self: @Tensor<complex64>,
+        roi: Option<Tensor<complex64>>,
+        scales: Option<Span<complex64>>,
+        sizes: Option<Span<usize>>,
+        antialias: Option<usize>,
+        axes: Option<Span<usize>>,
+        coordinate_transformation_mode: Option<math::resize::TRANSFORMATION_MODE>,
+        cubic_coeff_a: Option<complex64>,
+        exclude_outside: Option<bool>,
+        extrapolation_value: Option<complex64>,
+        keep_aspect_ratio_policy: Option<math::resize::KEEP_ASPECT_RATIO_POLICY>,
+        mode: Option<math::resize::MODE>,
+        nearest_mode: Option<math::resize::NEAREST_MODE>,
+    ) -> Tensor<complex64> {
+        panic(array!['not supported!'])
+    }
+}
+
+/// Implements addition for `Tensor<complex64>` using the `Add` trait.
+impl Complex64TensorAdd of Add<Tensor<complex64>> {
+    /// Adds two `Tensor<complex64>` instances element-wise.
+    ///
+    /// # Arguments
+    /// * `lhs` - The first tensor.
+    /// * `rhs` - The second tensor.
+    ///
+    /// # Returns
+    /// * A `Tensor<complex64>` instance representing the result of the element-wise addition.
+    fn add(lhs: Tensor<complex64>, rhs: Tensor<complex64>) -> Tensor<complex64> {
+        math::arithmetic::add(@lhs, @rhs)
+    }
+}
+
+/// Implements subtraction for `Tensor<complex64>` using the `Sub` trait.
+impl Complex64TensorSub of Sub<Tensor<complex64>> {
+    /// Subtracts two `Tensor<complex64>` instances element-wise.
+    ///
+    /// # Arguments
+    /// * `lhs` - The first tensor.
+    /// * `rhs` - The second tensor.
+    ///
+    /// # Returns
+    /// * A `Tensor<complex64>` instance representing the result of the element-wise subtraction.
+    fn sub(lhs: Tensor<complex64>, rhs: Tensor<complex64>) -> Tensor<complex64> {
+        math::arithmetic::sub(@lhs, @rhs)
+    }
+}
+
+/// Implements multiplication for `Tensor<complex64>` using the `Mul` trait.
+impl Complex64TensorMul of Mul<Tensor<complex64>> {
+    /// Multiplies two `Tensor<complex64>` instances element-wise.
+    ///
+    /// # Arguments
+    /// * `lhs` - The first tensor.
+    /// * `rhs` - The second tensor.
+    ///
+    /// # Returns
+    /// * A `Tensor<complex64>` instance representing the result of the element-wise multiplication.
+    fn mul(lhs: Tensor<complex64>, rhs: Tensor<complex64>) -> Tensor<complex64> {
+        math::arithmetic::mul(@lhs, @rhs)
+    }
+}
+
+/// Implements division for `Tensor<complex64>` using the `Div` trait.
+impl Complex64TensorDiv of Div<Tensor<complex64>> {
+    /// Divides two `Tensor<complex64>` instances element-wise.
+    ///
+    /// # Arguments
+    /// * `lhs` - The first tensor.
+    /// * `rhs` - The second tensor.
+    ///
+    /// # Returns
+    /// * A `Tensor<complex64>` instance representing the result of the element-wise division.
+    fn div(lhs: Tensor<complex64>, rhs: Tensor<complex64>) -> Tensor<complex64> {
+        math::arithmetic::div(@lhs, @rhs)
+    }
+}
+
+/// Implements partial equal for two `Tensor<complex64>` using the `complex64` trait.
+impl Complex64TensorPartialEq of PartialEq<Tensor<complex64>> {
+    fn eq(lhs: @Tensor<complex64>, rhs: @Tensor<complex64>) -> bool {
+        tensor_eq(*lhs, *rhs)
+    }
+
+    fn ne(lhs: @Tensor<complex64>, rhs: @Tensor<complex64>) -> bool {
+        !tensor_eq(*lhs, *rhs)
+    }
+}
+
+
+// Internals
+
+fn eq(lhs: @complex64, rhs: @complex64) -> bool {
+    let eq = (*lhs.real == *rhs.real) && (*lhs.img == *rhs.img);
+    eq
+}
+
+fn tensor_eq(mut lhs: Tensor<complex64>, mut rhs: Tensor<complex64>,) -> bool {
+    let mut is_eq = true;
+
+    loop {
+        if lhs.shape.len() == 0 || !is_eq {
+            break;
+        }
+
+        is_eq = lhs.shape.pop_front().unwrap() == rhs.shape.pop_front().unwrap();
+    };
+
+    if !is_eq {
+        return false;
+    }
+
+    loop {
+        if lhs.data.len() == 0 || !is_eq {
+            break;
+        }
+
+        is_eq = eq(lhs.data.pop_front().unwrap(), rhs.data.pop_front().unwrap());
+    };
+
+    return is_eq;
+}
+
diff --git a/src/operators/tensor/implementations/tensor_fp16x16.cairo b/src/operators/tensor/implementations/tensor_fp16x16.cairo
index ccaf5903d..cbe7fdfcc 100644
--- a/src/operators/tensor/implementations/tensor_fp16x16.cairo
+++ b/src/operators/tensor/implementations/tensor_fp16x16.cairo
@@ -448,28 +448,12 @@ impl FP16x16Tensor of TensorTrait<FP16x16> {
         math::gather_elements::gather_elements(self, indices, axis)
     }
 
-    fn sequence_length(self: Array<Tensor<FP16x16>>) -> Tensor<u32> {
-        math::sequence_length::sequence_length(self)
-    }
-
     fn shrink(
         self: Tensor<FP16x16>, bias: Option<FP16x16>, lambd: Option<FP16x16>
     ) -> Tensor<FP16x16> {
         math::shrink::shrink(self, bias, lambd)
     }
 
-    fn sequence_at(sequence: Array<Tensor<FP16x16>>, position: Tensor<i32>) -> Tensor<FP16x16> {
-        math::sequence_at::sequence_at(sequence, position)
-    }
-
-    fn sequence_construct(tensors: Array<Tensor<FP16x16>>) -> Array<Tensor<FP16x16>> {
-        math::sequence_construct::sequence_construct(tensors)
-    }
-
-    fn sequence_empty() -> Array<Tensor<FP16x16>> {
-        math::sequence_empty::sequence_empty::<FP16x16>()
-    }
-
     fn reduce_mean(
         self: @Tensor<FP16x16>,
         axes: Option<Span<usize>>,
@@ -492,18 +476,6 @@ impl FP16x16Tensor of TensorTrait<FP16x16> {
         math::pow::pow(self, other)
     }
 
-    fn sequence_erase(
-        sequence: Array<Tensor<FP16x16>>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<FP16x16>> {
-        math::sequence_erase::sequence_erase(sequence, position)
-    }
-
-    fn sequence_insert(
-        self: Array<Tensor<FP16x16>>, tensor: @Tensor<FP16x16>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<FP16x16>> {
-        math::sequence_insert::sequence_insert(self, tensor, position)
-    }
-
     fn is_inf(
         self: @Tensor<FP16x16>, detect_negative: Option<u8>, detect_positive: Option<u8>
     ) -> Tensor<bool> {
@@ -514,16 +486,12 @@ impl FP16x16Tensor of TensorTrait<FP16x16> {
         math::is_nan::is_nan(self)
     }
 
-    fn concat_from_sequence(
-        sequence: Array<Tensor<FP16x16>>, axis: i32, new_axis: Option<usize>
+    fn gather_nd(
+        self: @Tensor<FP16x16>, indices: Tensor<usize>, batch_dims: Option<usize>
     ) -> Tensor<FP16x16> {
-        math::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
-    }
-
-    fn gather_nd(self: @Tensor<FP16x16>, indices: Tensor<usize>, batch_dims: Option<usize>) -> Tensor<FP16x16> {
         math::gather_nd::gather_nd(self, indices, batch_dims)
     }
-    
+
     fn reduce_log_sum(self: @Tensor<FP16x16>, axis: usize, keepdims: bool) -> Tensor<FP16x16> {
         math::reduce_log_sum::reduce_log_sum(self, axis, keepdims)
     }
@@ -537,6 +505,61 @@ impl FP16x16Tensor of TensorTrait<FP16x16> {
     ) -> (Tensor<FP16x16>, Tensor<i32>, Tensor<i32>, Tensor<i32>) {
         manipulation::unique::unique(self, axis, sorted)
     }
+
+    fn layer_normalization(
+        self: @Tensor<FP16x16>,
+        scale: @Tensor<FP16x16>,
+        B: Option<@Tensor<FP16x16>>,
+        axis: Option<i32>,
+        epsilon: Option<FP16x16>,
+        stash_type: Option<usize>,
+    ) -> (Tensor<FP16x16>, Tensor<FP16x16>, Tensor<FP16x16>) {
+        math::layer_normalization::layer_normalization(self, scale, B, axis, epsilon, stash_type)
+    }
+
+    fn resize(
+        self: @Tensor<FP16x16>,
+        roi: Option<Tensor<FP16x16>>,
+        scales: Option<Span<FP16x16>>,
+        sizes: Option<Span<usize>>,
+        antialias: Option<usize>,
+        axes: Option<Span<usize>>,
+        coordinate_transformation_mode: Option<math::resize::TRANSFORMATION_MODE>,
+        cubic_coeff_a: Option<FP16x16>,
+        exclude_outside: Option<bool>,
+        extrapolation_value: Option<FP16x16>,
+        keep_aspect_ratio_policy: Option<math::resize::KEEP_ASPECT_RATIO_POLICY>,
+        mode: Option<math::resize::MODE>,
+        nearest_mode: Option<math::resize::NEAREST_MODE>,
+    ) -> Tensor<FP16x16> {
+        math::resize::resize(
+            self,
+            roi,
+            scales,
+            sizes,
+            antialias,
+            axes,
+            coordinate_transformation_mode,
+            cubic_coeff_a,
+            exclude_outside,
+            extrapolation_value,
+            keep_aspect_ratio_policy,
+            mode,
+            nearest_mode
+        )
+    }
+
+    fn compress(
+        self: @Tensor<FP16x16>, condition: Tensor<usize>, axis: Option<usize>
+    ) -> Tensor<FP16x16> {
+        math::compress::compress(self, condition, axis)
+    }
+
+    fn split(
+        self: @Tensor<FP16x16>, axis: usize, num_outputs: Option<usize>, spl: Option<Tensor<usize>>
+    ) -> Array<Tensor<FP16x16>> {
+        manipulation::split::split(self, axis, num_outputs, spl)
+    }
 }
 
 /// Implements addition for `Tensor<FP16x16>` using the `Add` trait.
diff --git a/src/operators/tensor/implementations/tensor_fp16x16wide.cairo b/src/operators/tensor/implementations/tensor_fp16x16wide.cairo
index dc32202ed..584abf5c1 100644
--- a/src/operators/tensor/implementations/tensor_fp16x16wide.cairo
+++ b/src/operators/tensor/implementations/tensor_fp16x16wide.cairo
@@ -414,28 +414,12 @@ impl FP16x16WTensor of TensorTrait<FP16x16W> {
         math::gather_elements::gather_elements(self, indices, axis)
     }
 
-    fn sequence_length(self: Array<Tensor<FP16x16W>>) -> Tensor<u32> {
-        math::sequence_length::sequence_length(self)
-    }
-
     fn shrink(
         self: Tensor<FP16x16W>, bias: Option<FP16x16W>, lambd: Option<FP16x16W>
     ) -> Tensor<FP16x16W> {
         math::shrink::shrink(self, bias, lambd)
     }
 
-    fn sequence_at(sequence: Array<Tensor<FP16x16W>>, position: Tensor<i32>) -> Tensor<FP16x16W> {
-        math::sequence_at::sequence_at(sequence, position)
-    }
-
-    fn sequence_construct(tensors: Array<Tensor<FP16x16W>>) -> Array<Tensor<FP16x16W>> {
-        math::sequence_construct::sequence_construct(tensors)
-    }
-
-    fn sequence_empty() -> Array<Tensor<FP16x16W>> {
-        math::sequence_empty::sequence_empty::<FP16x16W>()
-    }
-
     fn reduce_mean(
         self: @Tensor<FP16x16W>,
         axes: Option<Span<usize>>,
@@ -458,18 +442,6 @@ impl FP16x16WTensor of TensorTrait<FP16x16W> {
         math::pow::pow(self, other)
     }
 
-    fn sequence_erase(
-        sequence: Array<Tensor<FP16x16W>>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<FP16x16W>> {
-        math::sequence_erase::sequence_erase(sequence, position)
-    }
-
-    fn sequence_insert(
-        self: Array<Tensor<FP16x16W>>, tensor: @Tensor<FP16x16W>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<FP16x16W>> {
-        math::sequence_insert::sequence_insert(self, tensor, position)
-    }
-
     fn is_inf(
         self: @Tensor<FP16x16W>, detect_negative: Option<u8>, detect_positive: Option<u8>
     ) -> Tensor<bool> {
@@ -480,16 +452,12 @@ impl FP16x16WTensor of TensorTrait<FP16x16W> {
         math::is_nan::is_nan(self)
     }
 
-    fn concat_from_sequence(
-        sequence: Array<Tensor<FP16x16W>>, axis: i32, new_axis: Option<usize>
+    fn gather_nd(
+        self: @Tensor<FP16x16W>, indices: Tensor<usize>, batch_dims: Option<usize>
     ) -> Tensor<FP16x16W> {
-        math::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
-    }
-
-    fn gather_nd(self: @Tensor<FP16x16W>, indices: Tensor<usize>, batch_dims: Option<usize>) -> Tensor<FP16x16W> {
         math::gather_nd::gather_nd(self, indices, batch_dims)
     }
-    
+
     fn reduce_log_sum(self: @Tensor<FP16x16W>, axis: usize, keepdims: bool) -> Tensor<FP16x16W> {
         math::reduce_log_sum::reduce_log_sum(self, axis, keepdims)
     }
@@ -503,6 +471,47 @@ impl FP16x16WTensor of TensorTrait<FP16x16W> {
     ) -> (Tensor<FP16x16W>, Tensor<i32>, Tensor<i32>, Tensor<i32>) {
         manipulation::unique::unique(self, axis, sorted)
     }
+
+    fn compress(
+        self: @Tensor<FP16x16W>, condition: Tensor<usize>, axis: Option<usize>
+    ) -> Tensor<FP16x16W> {
+        math::compress::compress(self, condition, axis)
+    }
+
+    fn layer_normalization(
+        self: @Tensor<FP16x16W>,
+        scale: @Tensor<FP16x16W>,
+        B: Option<@Tensor<FP16x16W>>,
+        axis: Option<i32>,
+        epsilon: Option<FP16x16W>,
+        stash_type: Option<usize>,
+    ) -> (Tensor<FP16x16W>, Tensor<FP16x16W>, Tensor<FP16x16W>) {
+        math::layer_normalization::layer_normalization(self, scale, B, axis, epsilon, stash_type)
+    }
+
+    fn resize(
+        self: @Tensor<FP16x16W>,
+        roi: Option<Tensor<FP16x16W>>,
+        scales: Option<Span<FP16x16W>>,
+        sizes: Option<Span<usize>>,
+        antialias: Option<usize>,
+        axes: Option<Span<usize>>,
+        coordinate_transformation_mode: Option<math::resize::TRANSFORMATION_MODE>,
+        cubic_coeff_a: Option<FP16x16W>,
+        exclude_outside: Option<bool>,
+        extrapolation_value: Option<FP16x16W>,
+        keep_aspect_ratio_policy: Option<math::resize::KEEP_ASPECT_RATIO_POLICY>,
+        mode: Option<math::resize::MODE>,
+        nearest_mode: Option<math::resize::NEAREST_MODE>,
+    ) -> Tensor<FP16x16W> {
+        panic(array!['not supported!'])
+    }
+
+    fn split(
+        self: @Tensor<FP16x16W>, axis: usize, num_outputs: Option<usize>, spl: Option<Tensor<usize>>
+    ) -> Array<Tensor<FP16x16W>> {
+        manipulation::split::split(self, axis, num_outputs, spl)
+    }
 }
 
 /// Implements addition for `Tensor<FP16x16W>` using the `Add` trait.
diff --git a/src/operators/tensor/implementations/tensor_fp32x32.cairo b/src/operators/tensor/implementations/tensor_fp32x32.cairo
index 9100d6f82..e7b517eb4 100644
--- a/src/operators/tensor/implementations/tensor_fp32x32.cairo
+++ b/src/operators/tensor/implementations/tensor_fp32x32.cairo
@@ -449,28 +449,12 @@ impl FP32x32Tensor of TensorTrait<FP32x32> {
         math::gather_elements::gather_elements(self, indices, axis)
     }
 
-    fn sequence_length(self: Array<Tensor<FP32x32>>) -> Tensor<u32> {
-        math::sequence_length::sequence_length(self)
-    }
-
     fn shrink(
         self: Tensor<FP32x32>, bias: Option<FP32x32>, lambd: Option<FP32x32>
     ) -> Tensor<FP32x32> {
         math::shrink::shrink(self, bias, lambd)
     }
 
-    fn sequence_at(sequence: Array<Tensor<FP32x32>>, position: Tensor<i32>) -> Tensor<FP32x32> {
-        math::sequence_at::sequence_at(sequence, position)
-    }
-
-    fn sequence_construct(tensors: Array<Tensor<FP32x32>>) -> Array<Tensor<FP32x32>> {
-        math::sequence_construct::sequence_construct(tensors)
-    }
-
-    fn sequence_empty() -> Array<Tensor<FP32x32>> {
-        math::sequence_empty::sequence_empty::<FP32x32>()
-    }
-
     fn reduce_mean(
         self: @Tensor<FP32x32>,
         axes: Option<Span<usize>>,
@@ -493,18 +477,6 @@ impl FP32x32Tensor of TensorTrait<FP32x32> {
         math::pow::pow(self, other)
     }
 
-    fn sequence_erase(
-        sequence: Array<Tensor<FP32x32>>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<FP32x32>> {
-        math::sequence_erase::sequence_erase(sequence, position)
-    }
-
-    fn sequence_insert(
-        self: Array<Tensor<FP32x32>>, tensor: @Tensor<FP32x32>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<FP32x32>> {
-        math::sequence_insert::sequence_insert(self, tensor, position)
-    }
-
     fn is_inf(
         self: @Tensor<FP32x32>, detect_negative: Option<u8>, detect_positive: Option<u8>
     ) -> Tensor<bool> {
@@ -515,16 +487,12 @@ impl FP32x32Tensor of TensorTrait<FP32x32> {
         math::is_nan::is_nan(self)
     }
 
-    fn concat_from_sequence(
-        sequence: Array<Tensor<FP32x32>>, axis: i32, new_axis: Option<usize>
+    fn gather_nd(
+        self: @Tensor<FP32x32>, indices: Tensor<usize>, batch_dims: Option<usize>
     ) -> Tensor<FP32x32> {
-        math::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
-    }
-
-    fn gather_nd(self: @Tensor<FP32x32>, indices: Tensor<usize>, batch_dims: Option<usize>) -> Tensor<FP32x32> {
         math::gather_nd::gather_nd(self, indices, batch_dims)
     }
-    
+
     fn reduce_log_sum(self: @Tensor<FP32x32>, axis: usize, keepdims: bool) -> Tensor<FP32x32> {
         math::reduce_log_sum::reduce_log_sum(self, axis, keepdims)
     }
@@ -538,6 +506,61 @@ impl FP32x32Tensor of TensorTrait<FP32x32> {
     ) -> (Tensor<FP32x32>, Tensor<i32>, Tensor<i32>, Tensor<i32>) {
         manipulation::unique::unique(self, axis, sorted)
     }
+
+    fn layer_normalization(
+        self: @Tensor<FP32x32>,
+        scale: @Tensor<FP32x32>,
+        B: Option<@Tensor<FP32x32>>,
+        axis: Option<i32>,
+        epsilon: Option<FP32x32>,
+        stash_type: Option<usize>,
+    ) -> (Tensor<FP32x32>, Tensor<FP32x32>, Tensor<FP32x32>) {
+        math::layer_normalization::layer_normalization(self, scale, B, axis, epsilon, stash_type)
+    }
+
+    fn resize(
+        self: @Tensor<FP32x32>,
+        roi: Option<Tensor<FP32x32>>,
+        scales: Option<Span<FP32x32>>,
+        sizes: Option<Span<usize>>,
+        antialias: Option<usize>,
+        axes: Option<Span<usize>>,
+        coordinate_transformation_mode: Option<math::resize::TRANSFORMATION_MODE>,
+        cubic_coeff_a: Option<FP32x32>,
+        exclude_outside: Option<bool>,
+        extrapolation_value: Option<FP32x32>,
+        keep_aspect_ratio_policy: Option<math::resize::KEEP_ASPECT_RATIO_POLICY>,
+        mode: Option<math::resize::MODE>,
+        nearest_mode: Option<math::resize::NEAREST_MODE>,
+    ) -> Tensor<FP32x32> {
+        math::resize::resize(
+            self,
+            roi,
+            scales,
+            sizes,
+            antialias,
+            axes,
+            coordinate_transformation_mode,
+            cubic_coeff_a,
+            exclude_outside,
+            extrapolation_value,
+            keep_aspect_ratio_policy,
+            mode,
+            nearest_mode
+        )
+    }
+
+    fn compress(
+        self: @Tensor<FP32x32>, condition: Tensor<usize>, axis: Option<usize>
+    ) -> Tensor<FP32x32> {
+        math::compress::compress(self, condition, axis)
+    }
+
+    fn split(
+        self: @Tensor<FP32x32>, axis: usize, num_outputs: Option<usize>, spl: Option<Tensor<usize>>
+    ) -> Array<Tensor<FP32x32>> {
+        manipulation::split::split(self, axis, num_outputs, spl)
+    }
 }
 
 /// Implements addition for `Tensor<FP32x32>` using the `Add` trait.
diff --git a/src/operators/tensor/implementations/tensor_fp64x64.cairo b/src/operators/tensor/implementations/tensor_fp64x64.cairo
index ee6441058..a8121fc31 100644
--- a/src/operators/tensor/implementations/tensor_fp64x64.cairo
+++ b/src/operators/tensor/implementations/tensor_fp64x64.cairo
@@ -449,29 +449,12 @@ impl FP64x64Tensor of TensorTrait<FP64x64> {
         math::gather_elements::gather_elements(self, indices, axis)
     }
 
-    fn sequence_length(self: Array<Tensor<FP64x64>>) -> Tensor<u32> {
-        math::sequence_length::sequence_length(self)
-    }
-
     fn shrink(
         self: Tensor<FP64x64>, bias: Option<FP64x64>, lambd: Option<FP64x64>
     ) -> Tensor<FP64x64> {
         math::shrink::shrink(self, bias, lambd)
     }
 
-    fn sequence_at(sequence: Array<Tensor<FP64x64>>, position: Tensor<i32>) -> Tensor<FP64x64> {
-        math::sequence_at::sequence_at(sequence, position)
-    }
-
-    fn sequence_construct(tensors: Array<Tensor<FP64x64>>) -> Array<Tensor<FP64x64>> {
-        math::sequence_construct::sequence_construct(tensors)
-    }
-
-
-    fn sequence_empty() -> Array<Tensor<FP64x64>> {
-        math::sequence_empty::sequence_empty::<FP64x64>()
-    }
-
     fn reduce_mean(
         self: @Tensor<FP64x64>,
         axes: Option<Span<usize>>,
@@ -494,18 +477,6 @@ impl FP64x64Tensor of TensorTrait<FP64x64> {
         math::pow::pow(self, other)
     }
 
-    fn sequence_erase(
-        sequence: Array<Tensor<FP64x64>>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<FP64x64>> {
-        math::sequence_erase::sequence_erase(sequence, position)
-    }
-
-    fn sequence_insert(
-        self: Array<Tensor<FP64x64>>, tensor: @Tensor<FP64x64>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<FP64x64>> {
-        math::sequence_insert::sequence_insert(self, tensor, position)
-    }
-
     fn is_inf(
         self: @Tensor<FP64x64>, detect_negative: Option<u8>, detect_positive: Option<u8>
     ) -> Tensor<bool> {
@@ -516,16 +487,12 @@ impl FP64x64Tensor of TensorTrait<FP64x64> {
         math::is_nan::is_nan(self)
     }
 
-    fn concat_from_sequence(
-        sequence: Array<Tensor<FP64x64>>, axis: i32, new_axis: Option<usize>
+    fn gather_nd(
+        self: @Tensor<FP64x64>, indices: Tensor<usize>, batch_dims: Option<usize>
     ) -> Tensor<FP64x64> {
-        math::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
-    }
-
-    fn gather_nd(self: @Tensor<FP64x64>, indices: Tensor<usize>, batch_dims: Option<usize>) -> Tensor<FP64x64> {
         math::gather_nd::gather_nd(self, indices, batch_dims)
     }
-    
+
     fn reduce_log_sum(self: @Tensor<FP64x64>, axis: usize, keepdims: bool) -> Tensor<FP64x64> {
         math::reduce_log_sum::reduce_log_sum(self, axis, keepdims)
     }
@@ -539,6 +506,61 @@ impl FP64x64Tensor of TensorTrait<FP64x64> {
     ) -> (Tensor<FP64x64>, Tensor<i32>, Tensor<i32>, Tensor<i32>) {
         manipulation::unique::unique(self, axis, sorted)
     }
+
+    fn layer_normalization(
+        self: @Tensor<FP64x64>,
+        scale: @Tensor<FP64x64>,
+        B: Option<@Tensor<FP64x64>>,
+        axis: Option<i32>,
+        epsilon: Option<FP64x64>,
+        stash_type: Option<usize>,
+    ) -> (Tensor<FP64x64>, Tensor<FP64x64>, Tensor<FP64x64>) {
+        math::layer_normalization::layer_normalization(self, scale, B, axis, epsilon, stash_type)
+    }
+
+    fn resize(
+        self: @Tensor<FP64x64>,
+        roi: Option<Tensor<FP64x64>>,
+        scales: Option<Span<FP64x64>>,
+        sizes: Option<Span<usize>>,
+        antialias: Option<usize>,
+        axes: Option<Span<usize>>,
+        coordinate_transformation_mode: Option<math::resize::TRANSFORMATION_MODE>,
+        cubic_coeff_a: Option<FP64x64>,
+        exclude_outside: Option<bool>,
+        extrapolation_value: Option<FP64x64>,
+        keep_aspect_ratio_policy: Option<math::resize::KEEP_ASPECT_RATIO_POLICY>,
+        mode: Option<math::resize::MODE>,
+        nearest_mode: Option<math::resize::NEAREST_MODE>,
+    ) -> Tensor<FP64x64> {
+        math::resize::resize(
+            self,
+            roi,
+            scales,
+            sizes,
+            antialias,
+            axes,
+            coordinate_transformation_mode,
+            cubic_coeff_a,
+            exclude_outside,
+            extrapolation_value,
+            keep_aspect_ratio_policy,
+            mode,
+            nearest_mode
+        )
+    }
+
+    fn compress(
+        self: @Tensor<FP64x64>, condition: Tensor<usize>, axis: Option<usize>
+    ) -> Tensor<FP64x64> {
+        math::compress::compress(self, condition, axis)
+    }
+
+    fn split(
+        self: @Tensor<FP64x64>, axis: usize, num_outputs: Option<usize>, spl: Option<Tensor<usize>>
+    ) -> Array<Tensor<FP64x64>> {
+        manipulation::split::split(self, axis, num_outputs, spl)
+    }
 }
 
 /// Implements addition for `Tensor<FP64x64>` using the `Add` trait.
diff --git a/src/operators/tensor/implementations/tensor_fp8x23.cairo b/src/operators/tensor/implementations/tensor_fp8x23.cairo
index 17a601f7b..9f3e78573 100644
--- a/src/operators/tensor/implementations/tensor_fp8x23.cairo
+++ b/src/operators/tensor/implementations/tensor_fp8x23.cairo
@@ -436,27 +436,10 @@ impl FP8x23Tensor of TensorTrait<FP8x23> {
         math::gather_elements::gather_elements(self, indices, axis)
     }
 
-    fn sequence_length(self: Array<Tensor<FP8x23>>) -> Tensor<u32> {
-        math::sequence_length::sequence_length(self)
-    }
-
     fn shrink(self: Tensor<FP8x23>, bias: Option<FP8x23>, lambd: Option<FP8x23>) -> Tensor<FP8x23> {
         math::shrink::shrink(self, bias, lambd)
     }
 
-    fn sequence_at(sequence: Array<Tensor<FP8x23>>, position: Tensor<i32>) -> Tensor<FP8x23> {
-        math::sequence_at::sequence_at(sequence, position)
-    }
-
-    fn sequence_construct(tensors: Array<Tensor<FP8x23>>) -> Array<Tensor<FP8x23>> {
-        math::sequence_construct::sequence_construct(tensors)
-    }
-
-
-    fn sequence_empty() -> Array<Tensor<FP8x23>> {
-        math::sequence_empty::sequence_empty::<FP8x23>()
-    }
-
     fn reduce_mean(
         self: @Tensor<FP8x23>,
         axes: Option<Span<usize>>,
@@ -492,18 +475,6 @@ impl FP8x23Tensor of TensorTrait<FP8x23> {
         math::pow::pow(self, other)
     }
 
-    fn sequence_erase(
-        sequence: Array<Tensor<FP8x23>>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<FP8x23>> {
-        math::sequence_erase::sequence_erase(sequence, position)
-    }
-
-    fn sequence_insert(
-        self: Array<Tensor<FP8x23>>, tensor: @Tensor<FP8x23>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<FP8x23>> {
-        math::sequence_insert::sequence_insert(self, tensor, position)
-    }
-
     fn is_inf(
         self: @Tensor<FP8x23>, detect_negative: Option<u8>, detect_positive: Option<u8>
     ) -> Tensor<bool> {
@@ -514,16 +485,12 @@ impl FP8x23Tensor of TensorTrait<FP8x23> {
         math::is_nan::is_nan(self)
     }
 
-    fn concat_from_sequence(
-        sequence: Array<Tensor<FP8x23>>, axis: i32, new_axis: Option<usize>
+    fn gather_nd(
+        self: @Tensor<FP8x23>, indices: Tensor<usize>, batch_dims: Option<usize>
     ) -> Tensor<FP8x23> {
-        math::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
-    }
-
-    fn gather_nd(self: @Tensor<FP8x23>, indices: Tensor<usize>, batch_dims: Option<usize>) -> Tensor<FP8x23> {
         math::gather_nd::gather_nd(self, indices, batch_dims)
     }
-    
+
     fn reduce_log_sum(self: @Tensor<FP8x23>, axis: usize, keepdims: bool) -> Tensor<FP8x23> {
         math::reduce_log_sum::reduce_log_sum(self, axis, keepdims)
     }
@@ -537,6 +504,61 @@ impl FP8x23Tensor of TensorTrait<FP8x23> {
     ) -> (Tensor<FP8x23>, Tensor<i32>, Tensor<i32>, Tensor<i32>) {
         manipulation::unique::unique(self, axis, sorted)
     }
+
+    fn layer_normalization(
+        self: @Tensor<FP8x23>,
+        scale: @Tensor<FP8x23>,
+        B: Option<@Tensor<FP8x23>>,
+        axis: Option<i32>,
+        epsilon: Option<FP8x23>,
+        stash_type: Option<usize>,
+    ) -> (Tensor<FP8x23>, Tensor<FP8x23>, Tensor<FP8x23>) {
+        math::layer_normalization::layer_normalization(self, scale, B, axis, epsilon, stash_type)
+    }
+
+    fn resize(
+        self: @Tensor<FP8x23>,
+        roi: Option<Tensor<FP8x23>>,
+        scales: Option<Span<FP8x23>>,
+        sizes: Option<Span<usize>>,
+        antialias: Option<usize>,
+        axes: Option<Span<usize>>,
+        coordinate_transformation_mode: Option<math::resize::TRANSFORMATION_MODE>,
+        cubic_coeff_a: Option<FP8x23>,
+        exclude_outside: Option<bool>,
+        extrapolation_value: Option<FP8x23>,
+        keep_aspect_ratio_policy: Option<math::resize::KEEP_ASPECT_RATIO_POLICY>,
+        mode: Option<math::resize::MODE>,
+        nearest_mode: Option<math::resize::NEAREST_MODE>,
+    ) -> Tensor<FP8x23> {
+        math::resize::resize(
+            self,
+            roi,
+            scales,
+            sizes,
+            antialias,
+            axes,
+            coordinate_transformation_mode,
+            cubic_coeff_a,
+            exclude_outside,
+            extrapolation_value,
+            keep_aspect_ratio_policy,
+            mode,
+            nearest_mode
+        )
+    }
+
+    fn compress(
+        self: @Tensor<FP8x23>, condition: Tensor<usize>, axis: Option<usize>
+    ) -> Tensor<FP8x23> {
+        math::compress::compress(self, condition, axis)
+    }
+
+    fn split(
+        self: @Tensor<FP8x23>, axis: usize, num_outputs: Option<usize>, spl: Option<Tensor<usize>>
+    ) -> Array<Tensor<FP8x23>> {
+        manipulation::split::split(self, axis, num_outputs, spl)
+    }
 }
 
 /// Implements addition for `Tensor<FP8x23>` using the `Add` trait.
diff --git a/src/operators/tensor/implementations/tensor_fp8x23wide.cairo b/src/operators/tensor/implementations/tensor_fp8x23wide.cairo
index a7d19901b..6a7ca5ba4 100644
--- a/src/operators/tensor/implementations/tensor_fp8x23wide.cairo
+++ b/src/operators/tensor/implementations/tensor_fp8x23wide.cairo
@@ -400,29 +400,12 @@ impl FP8x23WTensor of TensorTrait<FP8x23W> {
         math::gather_elements::gather_elements(self, indices, axis)
     }
 
-    fn sequence_length(self: Array<Tensor<FP8x23W>>) -> Tensor<u32> {
-        math::sequence_length::sequence_length(self)
-    }
-
     fn shrink(
         self: Tensor<FP8x23W>, bias: Option<FP8x23W>, lambd: Option<FP8x23W>
     ) -> Tensor<FP8x23W> {
         math::shrink::shrink(self, bias, lambd)
     }
 
-    fn sequence_at(sequence: Array<Tensor<FP8x23W>>, position: Tensor<i32>) -> Tensor<FP8x23W> {
-        math::sequence_at::sequence_at(sequence, position)
-    }
-
-    fn sequence_construct(tensors: Array<Tensor<FP8x23W>>) -> Array<Tensor<FP8x23W>> {
-        math::sequence_construct::sequence_construct(tensors)
-    }
-
-
-    fn sequence_empty() -> Array<Tensor<FP8x23W>> {
-        math::sequence_empty::sequence_empty::<FP8x23W>()
-    }
-
     fn reduce_mean(
         self: @Tensor<FP8x23W>,
         axes: Option<Span<usize>>,
@@ -445,18 +428,6 @@ impl FP8x23WTensor of TensorTrait<FP8x23W> {
         math::pow::pow(self, other)
     }
 
-    fn sequence_erase(
-        sequence: Array<Tensor<FP8x23W>>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<FP8x23W>> {
-        math::sequence_erase::sequence_erase(sequence, position)
-    }
-
-    fn sequence_insert(
-        self: Array<Tensor<FP8x23W>>, tensor: @Tensor<FP8x23W>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<FP8x23W>> {
-        math::sequence_insert::sequence_insert(self, tensor, position)
-    }
-
     fn is_inf(
         self: @Tensor<FP8x23W>, detect_negative: Option<u8>, detect_positive: Option<u8>
     ) -> Tensor<bool> {
@@ -467,16 +438,12 @@ impl FP8x23WTensor of TensorTrait<FP8x23W> {
         math::is_nan::is_nan(self)
     }
 
-    fn concat_from_sequence(
-        sequence: Array<Tensor<FP8x23W>>, axis: i32, new_axis: Option<usize>
+    fn gather_nd(
+        self: @Tensor<FP8x23W>, indices: Tensor<usize>, batch_dims: Option<usize>
     ) -> Tensor<FP8x23W> {
-        math::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
-    }
-
-    fn gather_nd(self: @Tensor<FP8x23W>, indices: Tensor<usize>, batch_dims: Option<usize>) -> Tensor<FP8x23W> {
         math::gather_nd::gather_nd(self, indices, batch_dims)
     }
-    
+
     fn reduce_log_sum(self: @Tensor<FP8x23W>, axis: usize, keepdims: bool) -> Tensor<FP8x23W> {
         math::reduce_log_sum::reduce_log_sum(self, axis, keepdims)
     }
@@ -490,6 +457,47 @@ impl FP8x23WTensor of TensorTrait<FP8x23W> {
     ) -> (Tensor<FP8x23W>, Tensor<i32>, Tensor<i32>, Tensor<i32>) {
         manipulation::unique::unique(self, axis, sorted)
     }
+
+    fn layer_normalization(
+        self: @Tensor<FP8x23W>,
+        scale: @Tensor<FP8x23W>,
+        B: Option<@Tensor<FP8x23W>>,
+        axis: Option<i32>,
+        epsilon: Option<FP8x23W>,
+        stash_type: Option<usize>,
+    ) -> (Tensor<FP8x23W>, Tensor<FP8x23W>, Tensor<FP8x23W>) {
+        math::layer_normalization::layer_normalization(self, scale, B, axis, epsilon, stash_type)
+    }
+
+    fn resize(
+        self: @Tensor<FP8x23W>,
+        roi: Option<Tensor<FP8x23W>>,
+        scales: Option<Span<FP8x23W>>,
+        sizes: Option<Span<usize>>,
+        antialias: Option<usize>,
+        axes: Option<Span<usize>>,
+        coordinate_transformation_mode: Option<math::resize::TRANSFORMATION_MODE>,
+        cubic_coeff_a: Option<FP8x23W>,
+        exclude_outside: Option<bool>,
+        extrapolation_value: Option<FP8x23W>,
+        keep_aspect_ratio_policy: Option<math::resize::KEEP_ASPECT_RATIO_POLICY>,
+        mode: Option<math::resize::MODE>,
+        nearest_mode: Option<math::resize::NEAREST_MODE>,
+    ) -> Tensor<FP8x23W> {
+        panic(array!['not supported!'])
+    }
+
+    fn compress(
+        self: @Tensor<FP8x23W>, condition: Tensor<usize>, axis: Option<usize>
+    ) -> Tensor<FP8x23W> {
+        math::compress::compress(self, condition, axis)
+    }
+
+    fn split(
+        self: @Tensor<FP8x23W>, axis: usize, num_outputs: Option<usize>, spl: Option<Tensor<usize>>
+    ) -> Array<Tensor<FP8x23W>> {
+        manipulation::split::split(self, axis, num_outputs, spl)
+    }
 }
 
 /// Implements addition for `Tensor<FP8x23W>` using the `Add` trait.
diff --git a/src/operators/tensor/implementations/tensor_i32.cairo b/src/operators/tensor/implementations/tensor_i32.cairo
index a987b0633..2e2ae267d 100644
--- a/src/operators/tensor/implementations/tensor_i32.cairo
+++ b/src/operators/tensor/implementations/tensor_i32.cairo
@@ -446,27 +446,10 @@ impl I32Tensor of TensorTrait<i32> {
         math::gather_elements::gather_elements(self, indices, axis)
     }
 
-    fn sequence_length(self: Array<Tensor<i32>>) -> Tensor<u32> {
-        math::sequence_length::sequence_length(self)
-    }
-
     fn shrink(self: Tensor<i32>, bias: Option<i32>, lambd: Option<i32>) -> Tensor<i32> {
         panic(array!['not supported!'])
     }
 
-    fn sequence_at(sequence: Array<Tensor<i32>>, position: Tensor<i32>) -> Tensor<i32> {
-        math::sequence_at::sequence_at(sequence, position)
-    }
-
-    fn sequence_construct(tensors: Array<Tensor<i32>>) -> Array<Tensor<i32>> {
-        math::sequence_construct::sequence_construct(tensors)
-    }
-
-
-    fn sequence_empty() -> Array<Tensor<i32>> {
-        math::sequence_empty::sequence_empty::<i32>()
-    }
-
     fn reduce_mean(
         self: @Tensor<i32>,
         axes: Option<Span<usize>>,
@@ -489,18 +472,6 @@ impl I32Tensor of TensorTrait<i32> {
         panic(array!['not supported!'])
     }
 
-    fn sequence_erase(
-        sequence: Array<Tensor<i32>>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<i32>> {
-        math::sequence_erase::sequence_erase(sequence, position)
-    }
-
-    fn sequence_insert(
-        self: Array<Tensor<i32>>, tensor: @Tensor<i32>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<i32>> {
-        math::sequence_insert::sequence_insert(self, tensor, position)
-    }
-
     fn is_inf(
         self: @Tensor<i32>, detect_negative: Option<u8>, detect_positive: Option<u8>
     ) -> Tensor<bool> {
@@ -511,16 +482,12 @@ impl I32Tensor of TensorTrait<i32> {
         panic(array!['not supported!'])
     }
 
-    fn concat_from_sequence(
-        sequence: Array<Tensor<i32>>, axis: i32, new_axis: Option<usize>
+    fn gather_nd(
+        self: @Tensor<i32>, indices: Tensor<usize>, batch_dims: Option<usize>
     ) -> Tensor<i32> {
-        math::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
-    }
-
-    fn gather_nd(self: @Tensor<i32>, indices: Tensor<usize>, batch_dims: Option<usize>) -> Tensor<i32> {
         math::gather_nd::gather_nd(self, indices, batch_dims)
     }
-    
+
     fn reduce_log_sum(self: @Tensor<i32>, axis: usize, keepdims: bool) -> Tensor<i32> {
         panic(array!['not supported!'])
     }
@@ -534,6 +501,45 @@ impl I32Tensor of TensorTrait<i32> {
     ) -> (Tensor<i32>, Tensor<i32>, Tensor<i32>, Tensor<i32>) {
         manipulation::unique::unique(self, axis, sorted)
     }
+
+    fn resize(
+        self: @Tensor<i32>,
+        roi: Option<Tensor<i32>>,
+        scales: Option<Span<i32>>,
+        sizes: Option<Span<usize>>,
+        antialias: Option<usize>,
+        axes: Option<Span<usize>>,
+        coordinate_transformation_mode: Option<math::resize::TRANSFORMATION_MODE>,
+        cubic_coeff_a: Option<i32>,
+        exclude_outside: Option<bool>,
+        extrapolation_value: Option<i32>,
+        keep_aspect_ratio_policy: Option<math::resize::KEEP_ASPECT_RATIO_POLICY>,
+        mode: Option<math::resize::MODE>,
+        nearest_mode: Option<math::resize::NEAREST_MODE>,
+    ) -> Tensor<i32> {
+        panic(array!['not supported!'])
+    }
+
+    fn compress(self: @Tensor<i32>, condition: Tensor<usize>, axis: Option<usize>) -> Tensor<i32> {
+        math::compress::compress(self, condition, axis)
+    }
+
+    fn layer_normalization(
+        self: @Tensor<i32>,
+        scale: @Tensor<i32>,
+        B: Option<@Tensor<i32>>,
+        axis: Option<i32>,
+        epsilon: Option<i32>,
+        stash_type: Option<usize>,
+    ) -> (Tensor<i32>, Tensor<i32>, Tensor<i32>) {
+        panic(array!['not supported!'])
+    }
+
+    fn split(
+        self: @Tensor<i32>, axis: usize, num_outputs: Option<usize>, spl: Option<Tensor<usize>>
+    ) -> Array<Tensor<i32>> {
+        manipulation::split::split(self, axis, num_outputs, spl)
+    }
 }
 
 /// Implements addition for `Tensor<i32>` using the `Add` trait.
diff --git a/src/operators/tensor/implementations/tensor_i8.cairo b/src/operators/tensor/implementations/tensor_i8.cairo
index 8c1e2fd32..769368166 100644
--- a/src/operators/tensor/implementations/tensor_i8.cairo
+++ b/src/operators/tensor/implementations/tensor_i8.cairo
@@ -444,27 +444,10 @@ impl I8Tensor of TensorTrait<i8> {
         math::gather_elements::gather_elements(self, indices, axis)
     }
 
-    fn sequence_length(self: Array<Tensor<i8>>) -> Tensor<u32> {
-        math::sequence_length::sequence_length(self)
-    }
-
     fn shrink(self: Tensor<i8>, bias: Option<i8>, lambd: Option<i8>) -> Tensor<i8> {
         panic(array!['not supported!'])
     }
 
-    fn sequence_at(sequence: Array<Tensor<i8>>, position: Tensor<i32>) -> Tensor<i8> {
-        math::sequence_at::sequence_at(sequence, position)
-    }
-
-    fn sequence_construct(tensors: Array<Tensor<i8>>) -> Array<Tensor<i8>> {
-        math::sequence_construct::sequence_construct(tensors)
-    }
-
-
-    fn sequence_empty() -> Array<Tensor<i8>> {
-        math::sequence_empty::sequence_empty::<i8>()
-    }
-
     fn reduce_mean(
         self: @Tensor<i8>,
         axes: Option<Span<usize>>,
@@ -487,18 +470,6 @@ impl I8Tensor of TensorTrait<i8> {
         panic(array!['not supported!'])
     }
 
-    fn sequence_erase(
-        sequence: Array<Tensor<i8>>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<i8>> {
-        math::sequence_erase::sequence_erase(sequence, position)
-    }
-
-    fn sequence_insert(
-        self: Array<Tensor<i8>>, tensor: @Tensor<i8>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<i8>> {
-        math::sequence_insert::sequence_insert(self, tensor, position)
-    }
-
     fn is_inf(
         self: @Tensor<i8>, detect_negative: Option<u8>, detect_positive: Option<u8>
     ) -> Tensor<bool> {
@@ -509,16 +480,12 @@ impl I8Tensor of TensorTrait<i8> {
         panic(array!['not supported!'])
     }
 
-    fn concat_from_sequence(
-        sequence: Array<Tensor<i8>>, axis: i32, new_axis: Option<usize>
+    fn gather_nd(
+        self: @Tensor<i8>, indices: Tensor<usize>, batch_dims: Option<usize>
     ) -> Tensor<i8> {
-        math::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
-    }
-
-    fn gather_nd(self: @Tensor<i8>, indices: Tensor<usize>, batch_dims: Option<usize>) -> Tensor<i8> {
         math::gather_nd::gather_nd(self, indices, batch_dims)
     }
-    
+
     fn reduce_log_sum(self: @Tensor<i8>, axis: usize, keepdims: bool) -> Tensor<i8> {
         panic(array!['not supported!'])
     }
@@ -532,6 +499,45 @@ impl I8Tensor of TensorTrait<i8> {
     ) -> (Tensor<i8>, Tensor<i32>, Tensor<i32>, Tensor<i32>) {
         manipulation::unique::unique(self, axis, sorted)
     }
+
+    fn resize(
+        self: @Tensor<i8>,
+        roi: Option<Tensor<i8>>,
+        scales: Option<Span<i8>>,
+        sizes: Option<Span<usize>>,
+        antialias: Option<usize>,
+        axes: Option<Span<usize>>,
+        coordinate_transformation_mode: Option<math::resize::TRANSFORMATION_MODE>,
+        cubic_coeff_a: Option<i8>,
+        exclude_outside: Option<bool>,
+        extrapolation_value: Option<i8>,
+        keep_aspect_ratio_policy: Option<math::resize::KEEP_ASPECT_RATIO_POLICY>,
+        mode: Option<math::resize::MODE>,
+        nearest_mode: Option<math::resize::NEAREST_MODE>,
+    ) -> Tensor<i8> {
+        panic(array!['not supported!'])
+    }
+
+    fn compress(self: @Tensor<i8>, condition: Tensor<usize>, axis: Option<usize>) -> Tensor<i8> {
+        math::compress::compress(self, condition, axis)
+    }
+
+    fn layer_normalization(
+        self: @Tensor<i8>,
+        scale: @Tensor<i8>,
+        B: Option<@Tensor<i8>>,
+        axis: Option<i32>,
+        epsilon: Option<i8>,
+        stash_type: Option<usize>,
+    ) -> (Tensor<i8>, Tensor<i8>, Tensor<i8>) {
+        panic(array!['not supported!'])
+    }
+
+    fn split(
+        self: @Tensor<i8>, axis: usize, num_outputs: Option<usize>, spl: Option<Tensor<usize>>
+    ) -> Array<Tensor<i8>> {
+        manipulation::split::split(self, axis, num_outputs, spl)
+    }
 }
 
 /// Implements addition for `Tensor<i8>` using the `Add` trait.
diff --git a/src/operators/tensor/implementations/tensor_u32.cairo b/src/operators/tensor/implementations/tensor_u32.cairo
index 5b2058401..d8e02d490 100644
--- a/src/operators/tensor/implementations/tensor_u32.cairo
+++ b/src/operators/tensor/implementations/tensor_u32.cairo
@@ -388,26 +388,10 @@ impl U32Tensor of TensorTrait<u32> {
         math::gather_elements::gather_elements(self, indices, axis)
     }
 
-    fn sequence_length(self: Array<Tensor<u32>>) -> Tensor<u32> {
-        math::sequence_length::sequence_length(self)
-    }
-
     fn shrink(self: Tensor<u32>, bias: Option<u32>, lambd: Option<u32>) -> Tensor<u32> {
         panic(array!['not supported!'])
     }
 
-    fn sequence_at(sequence: Array<Tensor<u32>>, position: Tensor<i32>) -> Tensor<u32> {
-        math::sequence_at::sequence_at(sequence, position)
-    }
-
-    fn sequence_construct(tensors: Array<Tensor<u32>>) -> Array<Tensor<u32>> {
-        math::sequence_construct::sequence_construct(tensors)
-    }
-
-    fn sequence_empty() -> Array<Tensor<u32>> {
-        math::sequence_empty::sequence_empty::<u32>()
-    }
-
     fn reduce_mean(
         self: @Tensor<u32>,
         axes: Option<Span<usize>>,
@@ -430,18 +414,6 @@ impl U32Tensor of TensorTrait<u32> {
         panic(array!['not supported!'])
     }
 
-    fn sequence_erase(
-        sequence: Array<Tensor<u32>>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<u32>> {
-        math::sequence_erase::sequence_erase(sequence, position)
-    }
-
-    fn sequence_insert(
-        self: Array<Tensor<u32>>, tensor: @Tensor<u32>, position: Option<Tensor<i32>>
-    ) -> Array<Tensor<u32>> {
-        math::sequence_insert::sequence_insert(self, tensor, position)
-    }
-
     fn is_inf(
         self: @Tensor<u32>, detect_negative: Option<u8>, detect_positive: Option<u8>
     ) -> Tensor<bool> {
@@ -452,16 +424,12 @@ impl U32Tensor of TensorTrait<u32> {
         panic(array!['not supported!'])
     }
 
-    fn concat_from_sequence(
-        sequence: Array<Tensor<u32>>, axis: i32, new_axis: Option<usize>
+    fn gather_nd(
+        self: @Tensor<u32>, indices: Tensor<usize>, batch_dims: Option<usize>
     ) -> Tensor<u32> {
-        math::concat_from_sequence::concat_from_sequence(sequence, axis, new_axis)
-    }
-
-    fn gather_nd(self: @Tensor<u32>, indices: Tensor<usize>, batch_dims: Option<usize>) -> Tensor<u32> {
         math::gather_nd::gather_nd(self, indices, batch_dims)
     }
-    
+
     fn reduce_log_sum(self: @Tensor<u32>, axis: usize, keepdims: bool) -> Tensor<u32> {
         panic(array!['not supported!'])
     }
@@ -475,6 +443,45 @@ impl U32Tensor of TensorTrait<u32> {
     ) -> (Tensor<u32>, Tensor<i32>, Tensor<i32>, Tensor<i32>) {
         manipulation::unique::unique(self, axis, sorted)
     }
+
+    fn resize(
+        self: @Tensor<u32>,
+        roi: Option<Tensor<u32>>,
+        scales: Option<Span<u32>>,
+        sizes: Option<Span<usize>>,
+        antialias: Option<usize>,
+        axes: Option<Span<usize>>,
+        coordinate_transformation_mode: Option<math::resize::TRANSFORMATION_MODE>,
+        cubic_coeff_a: Option<u32>,
+        exclude_outside: Option<bool>,
+        extrapolation_value: Option<u32>,
+        keep_aspect_ratio_policy: Option<math::resize::KEEP_ASPECT_RATIO_POLICY>,
+        mode: Option<math::resize::MODE>,
+        nearest_mode: Option<math::resize::NEAREST_MODE>,
+    ) -> Tensor<u32> {
+        panic(array!['not supported!'])
+    }
+
+    fn compress(self: @Tensor<u32>, condition: Tensor<usize>, axis: Option<usize>) -> Tensor<u32> {
+        math::compress::compress(self, condition, axis)
+    }
+
+    fn layer_normalization(
+        self: @Tensor<u32>,
+        scale: @Tensor<u32>,
+        B: Option<@Tensor<u32>>,
+        axis: Option<i32>,
+        epsilon: Option<u32>,
+        stash_type: Option<usize>,
+    ) -> (Tensor<u32>, Tensor<u32>, Tensor<u32>) {
+        panic(array!['not supported!'])
+    }
+
+    fn split(
+        self: @Tensor<u32>, axis: usize, num_outputs: Option<usize>, spl: Option<Tensor<usize>>
+    ) -> Array<Tensor<u32>> {
+        manipulation::split::split(self, axis, num_outputs, spl)
+    }
 }
 
 /// Implements addition for `Tensor<u32>` using the `Add` trait.
diff --git a/src/operators/tensor/linalg/transpose.cairo b/src/operators/tensor/linalg/transpose.cairo
index 44df929ba..c7bb96da7 100644
--- a/src/operators/tensor/linalg/transpose.cairo
+++ b/src/operators/tensor/linalg/transpose.cairo
@@ -11,7 +11,10 @@ use orion::numbers::NumberTrait;
 fn transpose<T, impl TTensor: TensorTrait<T>, impl TCopy: Copy<T>, impl TDrop: Drop<T>>(
     self: @Tensor<T>, axes: Span<usize>
 ) -> Tensor<T> {
-    assert((*self.shape).len() > 1, 'cannot transpose a 1D tensor');
+    if (*self.shape).len() == 1 {
+        return self.identity();
+    }
+
     assert(axes.len() == (*self.shape).len(), 'shape and axes length unequal');
 
     if (*self.shape).len() == 2 {
diff --git a/src/operators/tensor/manipulation.cairo b/src/operators/tensor/manipulation.cairo
index b517c624e..584eae027 100644
--- a/src/operators/tensor/manipulation.cairo
+++ b/src/operators/tensor/manipulation.cairo
@@ -1 +1,2 @@
 mod unique;
+mod split;
diff --git a/src/operators/tensor/manipulation/split.cairo b/src/operators/tensor/manipulation/split.cairo
new file mode 100644
index 000000000..bf0274aec
--- /dev/null
+++ b/src/operators/tensor/manipulation/split.cairo
@@ -0,0 +1,182 @@
+use orion::operators::tensor::{Tensor, TensorTrait, U32Tensor};
+use core::array::{ArrayTrait, SpanTrait};
+use core::option::OptionTrait;
+use orion::operators::matrix::{MutMatrixTrait, MutMatrix, MutMatrixImpl};
+
+/// Cf: NNTrait::split docstring
+fn split<
+    T,
+    +Copy<T>,
+    +Drop<T>,
+    +TensorTrait<T>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +PartialEq<Tensor<T>>,
+    +PartialOrd<Tensor<T>>
+>(
+    self: @Tensor<T>, axis: usize, num_outputs: Option<usize>, split: Option<Tensor<usize>>
+) -> Array<Tensor<T>> {
+    let has_num_outputs = match num_outputs {
+        Option::Some(value) => { true },
+        Option::None => false,
+    };
+    let has_split = match split {
+        Option::Some(value) => { true },
+        Option::None => false,
+    };
+    assert(!(has_num_outputs && has_split), 'split or num_outputs not both.');
+    assert(has_num_outputs || has_split, 'split or num_outputs not both.');
+
+    let mut splited_t: Array<Tensor<T>> = array![];
+
+    let rank = (*self).shape.len();
+    // assert(axis < rank && axis > -rank, 'axis out of dimensions');
+    assert(axis < rank, 'axis out of dimensions');
+
+    if (has_num_outputs) {
+        splited_t = split_num_outputs(self, axis, num_outputs.unwrap());
+    } else {
+        splited_t = split_has_split(self, axis, split.unwrap());
+    }
+    splited_t
+}
+
+/// Subfunction split for tensors (wth num_outputs).
+/// Cf: TensorTrait::split docstring
+fn split_num_outputs<T, +Copy<T>, +Drop<T>, +TensorTrait<T>, +PartialOrd<T>, +PartialEq<T>,>(
+    t: @Tensor<T>, mut axis: usize, num_outputs: usize
+) -> Array<Tensor<T>> {
+    let mut splited_t: Array<Tensor<T>> = array![];
+    let mut div: usize = 0;
+    // consturct split array
+    let mut split: Array<usize> = array![];
+    // if axis==0 {
+    //     axis = 1;
+    // }
+    if (*(*t).shape.at(axis) % num_outputs == 0) {
+        div = *(*t).shape.at(axis) / num_outputs;
+        let mut i = 0;
+        loop {
+            if (i >= num_outputs) {
+                break;
+            }
+            split.append(div);
+            i += 1;
+        };
+    } else {
+        div = *(*t).shape.at(axis) / num_outputs + 1;
+        let mut i = 0;
+        loop {
+            if (i >= num_outputs) {
+                break;
+            }
+            split.append(div);
+            i += 1;
+        };
+        match split.pop_front() {
+            Option::Some(split_last_one) => {
+                split.append(split_last_one + *(*t).shape.at(axis) - div * (num_outputs - 1));
+            },
+            Option::None(_) => { assert(false, 'split is none array'); }
+        }
+    }
+
+    let mut sli: MutMatrix<usize> = MutMatrixImpl::new((*t).shape.len(), 2);
+    let mut pos: usize = 0;
+    let mut i = 0;
+    loop {
+        if (i >= (*t).shape.len()) {
+            break;
+        }
+        let s: usize = *(*t).shape.at(i);
+        sli.set(i, 0, 0);
+        sli.set(i, 1, s);
+        i += 1;
+    };
+    let mut i: usize = 0;
+    loop {
+        if (i >= split.len()) {
+            break;
+        }
+        let spl = *split.at(i);
+        sli.set(axis, 0, pos);
+        pos += spl;
+        sli.set(axis, 1, pos);
+
+        let end_ele_0 = match sli.get(axis, 0) {
+            Option::Some(res) => { res },
+            Option::None(_) => {
+                assert(false, 'Get end_ele_0 is failed');
+                0
+            },
+        };
+        let end_ele_1 = match sli.get(axis, 1) {
+            Option::Some(res) => { res },
+            Option::None(_) => {
+                assert(false, 'Get end_ele_0 is failed');
+                0
+            },
+        };
+        let starts: Span<usize> = array![sli.get(0, 0).unwrap(), end_ele_0].span();
+        let ends: Span<usize> = array![sli.get(0, 1).unwrap(), end_ele_1].span();
+        let axes: Option<Span<usize>> = Option::None(());
+        let steps: Option<Span<usize>> = Option::None(());
+        let sub_t: Tensor<T> = t.slice(starts, ends, axes, steps);
+        splited_t.append(sub_t);
+        i += 1;
+    };
+    splited_t
+}
+
+/// Subfunction split for tensors (wth split).
+/// Cf: TensorTrait::split docstring
+fn split_has_split<T, +Copy<T>, +Drop<T>, +TensorTrait<T>, +PartialOrd<T>, +PartialEq<T>,>(
+    t: @Tensor<T>, axis: usize, split: Tensor<u32>
+) -> Array<Tensor<T>> {
+    let mut splited_t: Array<Tensor<T>> = array![];
+    let mut sli: MutMatrix<usize> = MutMatrixImpl::new((*t).shape.len(), 2);
+    let mut pos: usize = 0;
+    let mut i = 0;
+    loop {
+        if (i >= (*t).shape.len()) {
+            break;
+        }
+        let s: usize = *(*t).shape.at(i);
+        sli.set(i, 0, 0);
+        sli.set(i, 1, s);
+        i += 1;
+    };
+    let mut i: usize = 0;
+    loop {
+        if (i >= split.data.len()) {
+            break;
+        }
+        let spl: usize = split.at(indices: array![i].span());
+        sli.set(axis, 0, pos);
+        pos += spl;
+        sli.set(axis, 1, pos);
+
+        let end_ele_0 = match sli.get(axis, 0) {
+            Option::Some(res) => { res },
+            Option::None(_) => {
+                assert(false, 'Get end_ele_0 is failed');
+                0
+            },
+        };
+        let end_ele_1 = match sli.get(axis, 1) {
+            Option::Some(res) => { res },
+            Option::None(_) => {
+                assert(false, 'Get end_ele_0 is failed');
+                0
+            },
+        };
+        let starts: Span<usize> = array![sli.get(0, 0).unwrap(), end_ele_0].span();
+        let ends: Span<usize> = array![sli.get(0, 1).unwrap(), end_ele_1].span();
+        let axes: Option<Span<usize>> = Option::None(());
+        let steps: Option<Span<usize>> = Option::None(());
+        let sub_t: Tensor<T> = t.slice(starts, ends, axes, steps);
+        splited_t.append(sub_t);
+        i += 1;
+    };
+    splited_t
+}
diff --git a/src/operators/tensor/math.cairo b/src/operators/tensor/math.cairo
index e9822a21f..13c2ca49a 100644
--- a/src/operators/tensor/math.cairo
+++ b/src/operators/tensor/math.cairo
@@ -49,19 +49,15 @@ mod bitwise_and;
 mod bitwise_xor;
 mod bitwise_or;
 mod gather_elements;
-mod sequence_length;
-mod sequence_at;
 mod reduce_min;
-mod sequence_construct;
 mod shrink;
-mod sequence_empty;
 mod reduce_mean;
 mod pow;
-mod sequence_erase;
-mod sequence_insert;
-mod concat_from_sequence;
 mod is_nan;
 mod is_inf;
 mod gather_nd;
 mod reduce_log_sum;
 mod erf;
+mod layer_normalization;
+mod resize;
+mod compress;
diff --git a/src/operators/tensor/math/acos.cairo b/src/operators/tensor/math/acos.cairo
index 1e6fbcfe8..c36260752 100644
--- a/src/operators/tensor/math/acos.cairo
+++ b/src/operators/tensor/math/acos.cairo
@@ -2,6 +2,7 @@ use core::array::ArrayTrait;
 use core::array::SpanTrait;
 use core::option::OptionTrait;
 
+use orion::numbers::NumberTrait;
 use orion::numbers::fixed_point::core::FixedTrait;
 use orion::operators::tensor::core::{Tensor, TensorTrait};
 
@@ -9,10 +10,10 @@ use orion::operators::tensor::core::{Tensor, TensorTrait};
 fn acos<
     T,
     MAG,
-    impl FFixedTrait: FixedTrait<T, MAG>,
-    impl FTensor: TensorTrait<T>,
-    impl FCopy: Copy<T>,
-    impl FDrop: Drop<T>,
+    impl TNumberTrait: NumberTrait<T, MAG>,
+    impl TTensor: TensorTrait<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
 >(
     mut self: Tensor<T>
 ) -> Tensor<T> {
diff --git a/src/operators/tensor/math/acosh.cairo b/src/operators/tensor/math/acosh.cairo
index 78649e620..f486d5609 100644
--- a/src/operators/tensor/math/acosh.cairo
+++ b/src/operators/tensor/math/acosh.cairo
@@ -11,10 +11,10 @@ use orion::operators::tensor::core::{Tensor, TensorTrait};
 fn acosh<
     T,
     MAG,
-    impl FFixedTrait: FixedTrait<T, MAG>,
-    impl FTensor: TensorTrait<T>,
-    impl FCopy: Copy<T>,
-    impl FDrop: Drop<T>,
+    impl TNumberTrait: NumberTrait<T, MAG>,
+    impl TTensor: TensorTrait<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
 >(
     mut self: Tensor<T>
 ) -> Tensor<T> {
diff --git a/src/operators/tensor/math/asin.cairo b/src/operators/tensor/math/asin.cairo
index 7018edcc6..b33132797 100644
--- a/src/operators/tensor/math/asin.cairo
+++ b/src/operators/tensor/math/asin.cairo
@@ -2,6 +2,7 @@ use core::array::ArrayTrait;
 use core::array::SpanTrait;
 use core::option::OptionTrait;
 
+use orion::numbers::NumberTrait;
 use orion::numbers::fixed_point::core::FixedTrait;
 use orion::operators::tensor::core::{Tensor, TensorTrait};
 
@@ -9,10 +10,10 @@ use orion::operators::tensor::core::{Tensor, TensorTrait};
 fn asin<
     T,
     MAG,
-    impl FFixedTrait: FixedTrait<T, MAG>,
-    impl FTensor: TensorTrait<T>,
-    impl FCopy: Copy<T>,
-    impl FDrop: Drop<T>,
+    impl TNumberTrait: NumberTrait<T, MAG>,
+    impl TTensor: TensorTrait<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
 >(
     mut self: Tensor<T>
 ) -> Tensor<T> {
diff --git a/src/operators/tensor/math/asinh.cairo b/src/operators/tensor/math/asinh.cairo
index 28fcb467f..8d015554d 100644
--- a/src/operators/tensor/math/asinh.cairo
+++ b/src/operators/tensor/math/asinh.cairo
@@ -12,10 +12,10 @@ use orion::operators::tensor::core::{Tensor, TensorTrait};
 fn asinh<
     T,
     MAG,
-    impl FFixedTrait: FixedTrait<T, MAG>,
-    impl FTensor: TensorTrait<T>,
-    impl FCopy: Copy<T>,
-    impl FDrop: Drop<T>,
+    impl TNumberTrait: NumberTrait<T, MAG>,
+    impl TTensor: TensorTrait<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
 >(
     mut self: Tensor<T>
 ) -> Tensor<T> {
diff --git a/src/operators/tensor/math/atan.cairo b/src/operators/tensor/math/atan.cairo
index 25e991700..b5f93eb1c 100644
--- a/src/operators/tensor/math/atan.cairo
+++ b/src/operators/tensor/math/atan.cairo
@@ -11,10 +11,10 @@ use orion::operators::tensor::core::{Tensor, TensorTrait};
 fn atan<
     T,
     MAG,
-    impl FFixedTrait: FixedTrait<T, MAG>,
-    impl FTensor: TensorTrait<T>,
-    impl FCopy: Copy<T>,
-    impl FDrop: Drop<T>,
+    impl TNumberTrait: NumberTrait<T, MAG>,
+    impl TTensor: TensorTrait<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
 >(
     mut self: Tensor<T>
 ) -> Tensor<T> {
diff --git a/src/operators/tensor/math/bitwise_and.cairo b/src/operators/tensor/math/bitwise_and.cairo
index c27d77938..e3487568b 100644
--- a/src/operators/tensor/math/bitwise_and.cairo
+++ b/src/operators/tensor/math/bitwise_and.cairo
@@ -20,12 +20,10 @@ fn bitwise_and<
 >(
     y: @Tensor<T>, z: @Tensor<T>
 ) -> Tensor<T> {
-    'check_compatibility'.print();
     let broadcasted_shape = broadcast_shape(*y.shape, *z.shape);
     let mut result: Array<T> = ArrayTrait::<T>::new();
 
     let num_elements = len_from_shape(broadcasted_shape);
-    'checked'.print();
 
     let mut n: usize = 0;
     loop {
diff --git a/src/operators/tensor/math/compress.cairo b/src/operators/tensor/math/compress.cairo
new file mode 100644
index 000000000..d22eb1d82
--- /dev/null
+++ b/src/operators/tensor/math/compress.cairo
@@ -0,0 +1,161 @@
+use alexandria_data_structures::array_ext::SpanTraitExt;
+use core::array::ArrayTrait;
+use core::array::SpanTrait;
+use core::option::OptionTrait;
+
+use core::traits::Into;
+use core::debug::PrintTrait;
+use core::traits::TryInto;
+use core::serde::Serde;
+use core::traits::Destruct;
+
+use orion::numbers::NumberTrait;
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};
+
+/// Cf: TensorTrait::compare docstring
+fn compress<T, impl TTensorTrait: TensorTrait<T>, impl TCopy: Copy<T>, impl TDrop: Drop<T>,>(
+    self: @Tensor<T>, condition: Tensor<usize>, axis: Option<usize>
+) -> Tensor<T> {
+    let axis = match axis {
+        Option::Some(val) => val,
+        Option::None(_) => 999
+    };
+
+    let data_rank = (*self.shape).len();
+    let condition_rank = (condition.shape).len();
+    assert((data_rank >= 1), 'data rank must > 1');
+    assert((condition_rank == 1), 'condition rank must be 1');
+
+    let mut data_shape = *self.shape;
+    let mut condition_shape = condition.shape;
+
+    if (axis != 999) {
+        assert(*data_shape.at(axis) >= condition.data.len(), 'index out of bound');
+    }
+
+    let mut output_shape = ArrayTrait::new();
+    let mut index_data = ArrayTrait::new();
+    let mut output_data = ArrayTrait::new();
+
+    let mut data = *self.data;
+    let mut condition_data = condition.data;
+
+    let mut ind = 0;
+    let mut condition_data_clone = condition_data.clone();
+    let mut output = 0;
+    loop {
+        match condition_data_clone.pop_front() {
+            Option::Some(val) => {
+                if (*val != 0) {
+                    output += 1;
+                }
+                ind += 1;
+            },
+            Option::None(_) => { break; }
+        };
+    };
+
+    if (axis == 999) {
+        output_shape.append(output);
+
+        let mut total_shape = 1;
+        loop {
+            match data_shape.pop_front() {
+                Option::Some(val) => { total_shape *= *val; },
+                Option::None(_) => { break; }
+            };
+        };
+
+        let mut ind = 0;
+        loop {
+            match condition_data.pop_front() {
+                Option::Some(val) => {
+                    if (ind == total_shape) {
+                        break;
+                    }
+                    if (*val != 0) {
+                        output_data.append(*self.data[ind]);
+                    }
+                    ind += 1;
+                },
+                Option::None(_) => { break; }
+            };
+        };
+    } else {
+        let mut ind = 0;
+        let mut loop_breaker = 1;
+        let mut other_loop_breaker = 1;
+        let mut multiplier = 1;
+
+        let mut data_shape_clone = data_shape.clone();
+        loop {
+            match data_shape_clone.pop_front() {
+                Option::Some(val) => {
+                    if (ind == axis) {
+                        output_shape.append(output);
+                    } else {
+                        output_shape.append(*val);
+                        if (ind > axis) {
+                            loop_breaker *= *val;
+                        }
+                        if (ind >= axis) {
+                            multiplier *= *val;
+                        }
+                        if (ind < axis) {
+                            other_loop_breaker *= *val;
+                        }
+                    }
+                    ind += 1;
+                },
+                Option::None(_) => { break; }
+            };
+        };
+
+        let mut ind = 0;
+        let mut ind_loop = 0;
+
+        let mut inner_index: usize = 0;
+        let mut condition_data_clone = condition_data.clone();
+
+        loop {
+            if (ind == other_loop_breaker) {
+                break;
+            }
+            let mut condition_data_clone = condition_data.clone();
+            inner_index = *data_shape.at(axis) * ind;
+            loop {
+                match condition_data_clone.pop_front() {
+                    Option::Some(val) => {
+                        if (*val != 0) {
+                            let result = inner_index * loop_breaker;
+
+                            let mut data_ind: usize = result;
+                            loop {
+                                if data_ind == result + loop_breaker {
+                                    break;
+                                }
+                                index_data.append(data_ind);
+                                data_ind += 1;
+                            };
+                        }
+                        inner_index += 1;
+                    },
+                    Option::None(_) => { break; }
+                };
+            };
+
+            ind += 1;
+        };
+
+        loop {
+            match index_data.pop_front() {
+                Option::Some(val) => { output_data.append(*self.data[val]); },
+                Option::None(_) => { break; }
+            };
+        };
+    }
+
+    let mut output_tensor = TensorTrait::<T>::new(output_shape.span(), output_data.span());
+    return output_tensor;
+}
diff --git a/src/operators/tensor/math/cos.cairo b/src/operators/tensor/math/cos.cairo
index aad6ea925..5abeb327d 100644
--- a/src/operators/tensor/math/cos.cairo
+++ b/src/operators/tensor/math/cos.cairo
@@ -12,10 +12,10 @@ use orion::operators::tensor::core::{Tensor, TensorTrait};
 fn cos<
     T,
     MAG,
-    impl FFixedTrait: FixedTrait<T, MAG>,
-    impl FTensor: TensorTrait<T>,
-    impl FCopy: Copy<T>,
-    impl FDrop: Drop<T>,
+    impl TNumberTrait: NumberTrait<T, MAG>,
+    impl TTensor: TensorTrait<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
 >(
     mut self: Tensor<T>
 ) -> Tensor<T> {
diff --git a/src/operators/tensor/math/cosh.cairo b/src/operators/tensor/math/cosh.cairo
index f3f15a284..08c434f5a 100644
--- a/src/operators/tensor/math/cosh.cairo
+++ b/src/operators/tensor/math/cosh.cairo
@@ -12,10 +12,10 @@ use orion::operators::tensor::core::{Tensor, TensorTrait};
 fn cosh<
     T,
     MAG,
-    impl FFixedTrait: FixedTrait<T, MAG>,
-    impl FTensor: TensorTrait<T>,
-    impl FCopy: Copy<T>,
-    impl FDrop: Drop<T>,
+    impl TNumberTrait: NumberTrait<T, MAG>,
+    impl TTensor: TensorTrait<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
 >(
     mut self: Tensor<T>
 ) -> Tensor<T> {
diff --git a/src/operators/tensor/math/exp.cairo b/src/operators/tensor/math/exp.cairo
index a7e04dca1..889082d56 100644
--- a/src/operators/tensor/math/exp.cairo
+++ b/src/operators/tensor/math/exp.cairo
@@ -12,10 +12,10 @@ use orion::operators::tensor::core::{Tensor, TensorTrait};
 fn exp<
     T,
     MAG,
-    impl FFixedTrait: FixedTrait<T, MAG>,
-    impl FTensor: TensorTrait<T>,
-    impl FCopy: Copy<T>,
-    impl FDrop: Drop<T>,
+    impl TNumberTrait: NumberTrait<T, MAG>,
+    impl TTensor: TensorTrait<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
 >(
     mut self: Tensor<T>
 ) -> Tensor<T> {
diff --git a/src/operators/tensor/math/gather_nd.cairo b/src/operators/tensor/math/gather_nd.cairo
index 120eff5be..737a4fe32 100644
--- a/src/operators/tensor/math/gather_nd.cairo
+++ b/src/operators/tensor/math/gather_nd.cairo
@@ -14,12 +14,7 @@ use orion::operators::tensor::U32TensorPartialEq;
 use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor};
 
 /// Cf: TensorTrait::gather_nd docstring
-fn gather_nd<
-    T,
-    impl TTensorTrait: TensorTrait<T>,
-    impl TCopy: Copy<T>,
-    impl TDrop: Drop<T>,
->(
+fn gather_nd<T, impl TTensorTrait: TensorTrait<T>, impl TCopy: Copy<T>, impl TDrop: Drop<T>,>(
     self: @Tensor<T>, indices: Tensor<usize>, batch_dims: Option<usize>
 ) -> Tensor<T> {
     let batch_dims = match batch_dims {
@@ -29,19 +24,22 @@ fn gather_nd<
 
     let data_rank = (*self.shape).len();
     let indices_rank = (indices.shape).len();
-    assert((data_rank >= 1 ) & (indices_rank >= 1), 'rank must > 1');
-   
+    assert((data_rank >= 1) & (indices_rank >= 1), 'rank must > 1');
+
     let mut data_shape = *self.shape;
     let mut indices_shape = indices.shape;
     let mut data_shape_clone = data_shape.clone();
     let mut indices_shape_clone = indices_shape.clone();
 
     let indices_shape_last = indices_shape_clone.pop_back().unwrap();
-    assert((*indices_shape_last >= 1) & (*indices_shape_last <= data_rank-batch_dims), 'check indices');
+    assert(
+        (*indices_shape_last >= 1) & (*indices_shape_last <= data_rank - batch_dims),
+        'check indices'
+    );
 
     let mut batch_dims_shape = ArrayTrait::new();
     let mut output_shape = ArrayTrait::new();
-    let mut index_data =  ArrayTrait::new();
+    let mut index_data = ArrayTrait::new();
     let mut output_data = ArrayTrait::new();
 
     let mut batch_dims_size = batch_dims;
@@ -51,7 +49,7 @@ fn gather_nd<
     let mut ind = 0;
     loop {
         if (ind == batch_dims) {
-            break();
+            break ();
         }
         match indices_shape_clone.pop_front() {
             Option::Some(val) => {
@@ -65,17 +63,14 @@ fn gather_nd<
 
     loop {
         match indices_shape_clone.pop_front() {
-            Option::Some(val) => {
-                batch_dims_shape.append(*val);
-            },
+            Option::Some(val) => { batch_dims_shape.append(*val); },
             Option::None(_) => { break; }
         };
     };
 
     if (*indices_shape_last == data_rank - batch_dims) {
         output_shape = batch_dims_shape;
-    }
-    else {
+    } else {
         let mut ind = 0;
         let mut multiple = 1;
         output_shape = batch_dims_shape;
@@ -136,16 +131,18 @@ fn gather_nd<
         match data_indices.pop_front() {
             Option::Some(val) => {
                 let index = ind % *indices_shape_last;
-                let incr=  total_data_len * (ind / breaker);
+                let incr = total_data_len * (ind / breaker);
                 result += (*val * total_data_len / *multiple_data_len.at(index));
                 ind += 1;
 
-                if (index == *indices_shape_last-1) {
-                    let mut data_ind:usize = result ;
+                if (index == *indices_shape_last - 1) {
+                    let mut data_ind: usize = result;
                     loop {
-                        if data_ind == result + incrementer { break; }
+                        if data_ind == result + incrementer {
+                            break;
+                        }
                         index_data.append(data_ind + incr);
-                        data_ind+=1;
+                        data_ind += 1;
                     };
                     result = 0;
                 };
@@ -156,13 +153,11 @@ fn gather_nd<
 
     loop {
         match index_data.pop_front() {
-            Option::Some(val) => {
-               output_data.append(*self.data[val]);
-            },
+            Option::Some(val) => { output_data.append(*self.data[val]); },
             Option::None(_) => { break; }
         };
     };
 
     let mut output_tensor = TensorTrait::<T>::new(output_shape.span(), output_data.span());
     return output_tensor;
-}
\ No newline at end of file
+}
diff --git a/src/operators/tensor/math/layer_normalization.cairo b/src/operators/tensor/math/layer_normalization.cairo
new file mode 100644
index 000000000..6a20fd92b
--- /dev/null
+++ b/src/operators/tensor/math/layer_normalization.cairo
@@ -0,0 +1,191 @@
+use core::traits::TryInto;
+use core::array::ArrayTrait;
+use core::array::SpanTrait;
+use core::option::OptionTrait;
+use core::traits::Into;
+use orion::numbers::NumberTrait;
+use orion::operators::tensor::{
+    TensorTrait, Tensor, I8Tensor, I32Tensor, U32Tensor, FP16x16Tensor, BoolTensor
+};
+use orion::numbers::{FP16x16, FP16x16Impl, FP32x32, FP32x32Impl, FixedTrait};
+use core::debug::PrintTrait;
+use orion::numbers::{i8, i32, IntegerTrait};
+use orion::operators::vec::{VecTrait, NullableVec, NullableVecImpl};
+
+
+/// Cf: TensorTrait::layer_normalization docstring
+fn layer_normalization<
+    T,
+    MAG,
+    +TensorTrait<T>,
+    +NumberTrait<T, MAG>,
+    +PartialEq<T>,
+    +Copy<T>,
+    +Drop<T>,
+    +Div<Tensor<T>>,
+    +Sub<Tensor<T>>,
+    +Add<Tensor<T>>,
+    +Mul<Tensor<T>>,
+    +Into<usize, MAG>,
+>(
+    self: @Tensor<T>,
+    scale: @Tensor<T>,
+    B: Option<@Tensor<T>>,
+    axis: Option<i32>,
+    epsilon: Option<T>,
+    stash_type: Option<usize>,
+) -> (Tensor<T>, Tensor<T>, Tensor<T>) {
+    let X_rank = (*self).shape.len();
+    let X_shape = (*self).shape;
+
+    let mut axis = match axis {
+        Option::Some(axis) => axis,
+        Option::None => IntegerTrait::<i32>::new(1, true),
+    };
+    let epsilon = match epsilon {
+        Option::Some(epsilon) => epsilon,
+        Option::None => NumberTrait::zero(), // default of onnx is 1e-05 
+    };
+
+    let stash_type = match stash_type {
+        Option::Some(stash_type) => stash_type,
+        Option::None => 1,
+    };
+
+    let axis = if axis < IntegerTrait::<i32>::new(0, false) {
+        X_rank - axis.mag
+    } else {
+        axis.mag
+    };
+
+    let unsqueezed_rank = X_rank - axis;
+    let mut reduction_shape = ArrayTrait::new();
+    let mut i = 0;
+    loop {
+        if i == axis {
+            break;
+        }
+        reduction_shape.append(*(*self).shape.at(i));
+        i += 1;
+    };
+    let mut i = 0;
+    loop {
+        if i == unsqueezed_rank {
+            break;
+        }
+        reduction_shape.append(1);
+        i += 1;
+    };
+
+    let mut row_number = 1;
+    let mut col_number = 1;
+    let mut i = 0;
+    loop {
+        if i == X_rank {
+            break;
+        }
+        if i < axis {
+            row_number *= *(*self).shape.at(i);
+        } else {
+            col_number *= *(*self).shape.at(i);
+        }
+        i += 1;
+    };
+
+    let mut shape_matrix = ArrayTrait::new();
+    shape_matrix.append(row_number);
+    shape_matrix.append(col_number);
+
+    // Shape [1, 1] to mutiply one element tensors with 2D matrices
+    let mut shape_one = ArrayTrait::new();
+    shape_one.append(1);
+    shape_one.append(1);
+
+    let mut col_number_tensor = ArrayTrait::new();
+    col_number_tensor.append(NumberTrait::new_unscaled(col_number.into(), false));
+
+    let mut epsilon_tensor = ArrayTrait::new();
+    epsilon_tensor.append(epsilon);
+
+    let mut one_tensor = ArrayTrait::new();
+    one_tensor.append(NumberTrait::one());
+
+    let x_mat = self.reshape(shape_matrix.span());
+    let x_mean = x_mat.reduce_sum(1, true)
+        / TensorTrait::new(shape_one.span(), col_number_tensor.span());
+
+    let x_diff = x_mat - x_mean;
+    let x_squared_diff = x_diff * x_diff;
+
+    let variance = x_squared_diff.reduce_sum(1, true)
+        / TensorTrait::new(shape_one.span(), col_number_tensor.span());
+    let variance_eps = variance + TensorTrait::new(shape_one.span(), epsilon_tensor.span());
+
+    let std_dev = variance_eps.sqrt();
+
+    let inv_std_dev = TensorTrait::new(shape_one.span(), one_tensor.span()) / std_dev;
+
+    let y_mat = x_diff * inv_std_dev;
+
+    let scale = if (*scale).shape.len() < (*self).shape.len() {
+        // Append 1 in scale shape to make sure scale has a dimension compatible with Y for multiplication
+        let mut shape = ArrayTrait::new();
+        let mut i = 0;
+        loop {
+            if i == (*self).shape.len() - (*scale).shape.len() {
+                break;
+            }
+            shape.append(1);
+            i += 1;
+        };
+        let mut i = 0;
+        loop {
+            if i == (*scale).shape.len() {
+                break;
+            }
+            shape.append(*(*scale).shape.at(i));
+            i += 1;
+        };
+        TensorTrait::new(shape.span(), (*scale).data)
+    } else {
+        *scale
+    };
+
+    let Y = y_mat.reshape((*self).shape) * scale;
+
+    let Y = match B {
+        Option::Some(B) => {
+            let B = if (*B).shape.len() < (*self).shape.len() {
+                // Append 1 in B shape to make sure scale has a dimension compatible with Y for multiplication
+                let mut shape = ArrayTrait::new();
+                let mut i = 0;
+                loop {
+                    if i == (*self).shape.len() - (*B).shape.len() {
+                        break;
+                    }
+                    shape.append(1);
+                    i += 1;
+                };
+                let mut i = 0;
+                loop {
+                    if i == (*B).shape.len() {
+                        break;
+                    }
+                    shape.append(*(*B).shape.at(i));
+                    i += 1;
+                };
+                TensorTrait::new(shape.span(), (*B).data)
+            } else {
+                *B
+            };
+            Y + B
+        },
+        Option::None => Y,
+    };
+
+    let X_mean = TensorTrait::new(reduction_shape.span(), x_mean.data);
+    let X_inv_std_dev = TensorTrait::new(reduction_shape.span(), inv_std_dev.data);
+
+    return (Y, X_mean, X_inv_std_dev);
+}
+
diff --git a/src/operators/tensor/math/log.cairo b/src/operators/tensor/math/log.cairo
index 817ca135a..e55291fca 100644
--- a/src/operators/tensor/math/log.cairo
+++ b/src/operators/tensor/math/log.cairo
@@ -12,10 +12,10 @@ use orion::operators::tensor::core::{Tensor, TensorTrait};
 fn log<
     T,
     MAG,
-    impl FFixedTrait: FixedTrait<T, MAG>,
-    impl FTensor: TensorTrait<T>,
-    impl FCopy: Copy<T>,
-    impl FDrop: Drop<T>,
+    impl TNumberTrait: NumberTrait<T, MAG>,
+    impl TTensor: TensorTrait<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
 >(
     mut self: Tensor<T>
 ) -> Tensor<T> {
diff --git a/src/operators/tensor/math/reduce_l2.cairo b/src/operators/tensor/math/reduce_l2.cairo
index b18fa704c..d03499fec 100644
--- a/src/operators/tensor/math/reduce_l2.cairo
+++ b/src/operators/tensor/math/reduce_l2.cairo
@@ -11,12 +11,11 @@ use orion::numbers::fixed_point::core::FixedTrait;
 fn square<
     T,
     MAG,
-    impl FTensorTrait: TensorTrait<T>,
-    impl FFixed: FixedTrait<T, MAG>,
-    impl FNumber: NumberTrait<T, MAG>,
+    impl TTensorTrait: TensorTrait<T>,
+    impl TNumber: NumberTrait<T, MAG>,
     impl TMul: Mul<T>,
-    impl FCopy: Copy<T>,
-    impl FDrop: Drop<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
 >(
     self: @Tensor<T>
 ) -> Tensor<T> {
@@ -41,7 +40,6 @@ fn reduce_l2<
     T,
     MAG,
     impl TTensor: TensorTrait<T>,
-    impl FFixed: FixedTrait<T, MAG>,
     impl TNumber: NumberTrait<T, MAG>,
     impl TMul: Mul<T>,
     impl TCopy: Copy<T>,
@@ -53,3 +51,23 @@ fn reduce_l2<
     let tensor_square_sum = tensor_square.reduce_sum(axis: axis, keepdims: keepdims);
     return tensor_square_sum.sqrt();
 }
+
+fn reduce_l2_complex<
+    T,
+    MAG,
+    impl TTensor: TensorTrait<T>,
+    impl TNumber: NumberTrait<T, MAG>,
+    impl TMul: Mul<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
+    impl TPrint: PrintTrait<T>
+>(
+    self: @Tensor<T>, axis: usize, keepdims: bool
+) -> Tensor<T> {
+    let mut tensor_square = square(@self.abs());
+
+    let mut tensor_square_sum = tensor_square.reduce_sum(axis: axis, keepdims: keepdims);
+
+    return tensor_square_sum.sqrt();
+}
+
diff --git a/src/operators/tensor/math/resize.cairo b/src/operators/tensor/math/resize.cairo
new file mode 100644
index 000000000..ecae7b186
--- /dev/null
+++ b/src/operators/tensor/math/resize.cairo
@@ -0,0 +1,1470 @@
+use core::traits::TryInto;
+use core::array::ArrayTrait;
+use core::array::SpanTrait;
+use core::option::OptionTrait;
+use core::traits::Into;
+use orion::numbers::NumberTrait;
+use alexandria_sorting::bubble_sort;
+use orion::operators::tensor::{
+    TensorTrait, Tensor, I8Tensor, I32Tensor, U32Tensor, FP16x16Tensor, BoolTensor
+};
+use orion::numbers::{FP16x16, FP16x16Impl, FP32x32, FP32x32Impl, FixedTrait};
+use core::debug::PrintTrait;
+
+#[derive(Copy, Drop)]
+enum MODE {
+    NEAREST,
+    LINEAR,
+    CUBIC,
+}
+
+#[derive(Copy, Drop)]
+enum NEAREST_MODE {
+    ROUND_PREFER_FLOOR,
+    ROUND_PREFER_CEIL,
+    FLOOR,
+    CEIL
+}
+
+#[derive(Copy, Drop)]
+enum KEEP_ASPECT_RATIO_POLICY {
+    STRETCH,
+    NOT_LARGER,
+    NOT_SMALLER
+}
+
+#[derive(Copy, Drop)]
+enum TRANSFORMATION_MODE {
+    HALF_PIXEL,
+    ALIGN_CORNERS,
+    ASYMMETRIC,
+    TF_CROP_AND_RESIZE,
+    PYTORCH_HALF_PIXEL,
+    HALF_PIXEL_SYMMETRIC
+}
+
+
+/// Cf: TensorTrait::resize docstring
+fn resize<
+    T,
+    MAG,
+    +TensorTrait<T>,
+    +NumberTrait<T, MAG>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +Copy<T>,
+    +Drop<T>,
+    +TryInto<T, usize>,
+    +Into<usize, MAG>,
+    +AddEq<T>,
+    +Add<T>,
+    +Div<T>,
+    +Mul<T>,
+    +Sub<T>,
+>(
+    self: @Tensor<T>,
+    roi: Option<Tensor<T>>,
+    scales: Option<Span<T>>,
+    sizes: Option<Span<usize>>,
+    antialias: Option<usize>,
+    axes: Option<Span<usize>>,
+    coordinate_transformation_mode: Option<TRANSFORMATION_MODE>,
+    cubic_coeff_a: Option<T>,
+    exclude_outside: Option<bool>,
+    extrapolation_value: Option<T>,
+    keep_aspect_ratio_policy: Option<KEEP_ASPECT_RATIO_POLICY>,
+    mode: Option<MODE>,
+    nearest_mode: Option<NEAREST_MODE>,
+) -> Tensor<T> {
+    let output = interpolate_nd(
+        self,
+        antialias,
+        mode,
+        nearest_mode,
+        scales,
+        sizes,
+        roi,
+        keep_aspect_ratio_policy,
+        exclude_outside,
+        coordinate_transformation_mode,
+        extrapolation_value,
+        axes,
+        cubic_coeff_a
+    );
+    return output;
+}
+
+fn interpolate_nd<
+    T,
+    MAG,
+    +TensorTrait<T>,
+    +NumberTrait<T, MAG>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +Copy<T>,
+    +Drop<T>,
+    +TryInto<T, usize>,
+    +Into<usize, MAG>,
+    +AddEq<T>,
+    +Add<T>,
+    +Div<T>,
+    +Mul<T>,
+    +Sub<T>,
+>(
+    data: @Tensor<T>,
+    antialias: Option<usize>,
+    mode: Option<MODE>,
+    nearest_mode: Option<NEAREST_MODE>,
+    scale_factors: Option<Span<T>>,
+    output_size: Option<Span<usize>>,
+    roi: Option<Tensor<T>>,
+    keep_aspect_ratio_policy: Option<KEEP_ASPECT_RATIO_POLICY>,
+    exclude_outside: Option<bool>,
+    coordinate_transformation_mode: Option<TRANSFORMATION_MODE>,
+    extrapolation_value: Option<T>,
+    axes: Option<Span<usize>>,
+    cubic_coeff_a: Option<T>,
+) -> Tensor<T> {
+    let mode = match mode {
+        Option::Some(mode) => mode,
+        Option::None => { MODE::NEAREST },
+    };
+
+    let keep_aspect_ratio_policy = match keep_aspect_ratio_policy {
+        Option::Some(keep_aspect_ratio_policy) => keep_aspect_ratio_policy,
+        Option::None => { KEEP_ASPECT_RATIO_POLICY::STRETCH },
+    };
+
+    let exclude_outside = match exclude_outside {
+        Option::Some(exclude_outside) => exclude_outside,
+        Option::None => { false },
+    };
+
+    let extrapolation_value = match extrapolation_value {
+        Option::Some(extrapolation_value) => extrapolation_value,
+        Option::None => { NumberTrait::zero() },
+    };
+
+    if output_size.is_none() && scale_factors.is_none() {
+        core::panic_with_felt252('size and scale are None');
+    }
+
+    let r = (*data).shape.len();
+
+    let (axes, scale_factors, output_size, roi) = match axes {
+        Option::Some(axes) => {
+            let mut scale_factors = match scale_factors {
+                Option::Some(scale_factors) => {
+                    let mut new_scale_factors = ArrayTrait::<T>::new();
+                    let mut d = 0;
+                    loop {
+                        if d == r {
+                            break;
+                        }
+                        let mut i = 0;
+                        let item = loop {
+                            if i == axes.len() {
+                                break NumberTrait::one();
+                            }
+                            if *axes.at(i) == d {
+                                break *scale_factors.at(i);
+                            }
+                            i += 1;
+                        };
+                        new_scale_factors.append(item);
+                        d += 1;
+                    };
+
+                    Option::Some(new_scale_factors.span())
+                },
+                Option::None => { Option::None },
+            };
+
+            let mut output_size = match output_size {
+                Option::Some(output_size) => {
+                    let mut new_output_size = ArrayTrait::new();
+                    let mut d = 0;
+                    loop {
+                        if d == r {
+                            break;
+                        }
+                        let mut i = 0;
+                        let item = loop {
+                            if i == axes.len() {
+                                break *(*data).shape.at(d);
+                            }
+                            if *axes.at(i) == d {
+                                break *output_size.at(i);
+                            }
+                            i += 1;
+                        };
+                        new_output_size.append(item);
+                        d += 1;
+                    };
+                    Option::Some(new_output_size.span())
+                },
+                Option::None => { Option::None },
+            };
+
+            let mut roi = match roi {
+                Option::Some(roi) => {
+                    let mut new_roi_data = ArrayTrait::new();
+                    let naxes = axes.len();
+                    let mut d = 0;
+                    loop {
+                        if d == r {
+                            break;
+                        }
+                        let mut i = 0;
+                        let item = loop {
+                            if i == axes.len() {
+                                break NumberTrait::zero();
+                            }
+                            if *axes.at(i) == d {
+                                break *roi.data.at(i);
+                            }
+                            i += 1;
+                        };
+                        new_roi_data.append(item);
+                        d += 1;
+                    };
+
+                    let mut d = 0;
+                    loop {
+                        if d == r {
+                            break;
+                        }
+                        let mut i = 0;
+                        let item = loop {
+                            if i == axes.len() {
+                                break NumberTrait::one();
+                            }
+                            if *axes.at(i) == d {
+                                break *roi.data.at(i + naxes);
+                            }
+                            i += 1;
+                        };
+                        new_roi_data.append(item);
+                        d += 1;
+                    };
+                    let mut shape = ArrayTrait::new();
+                    shape.append(r * 2);
+                    Option::Some(TensorTrait::new(shape.span(), new_roi_data.span()))
+                },
+                Option::None => { Option::None },
+            };
+            (axes, scale_factors, output_size, roi)
+        },
+        Option::None => {
+            let mut axes = ArrayTrait::new();
+            let mut i = 0;
+            loop {
+                if i == r {
+                    break;
+                }
+                axes.append(i);
+                i += 1;
+            };
+            (axes.span(), scale_factors, output_size, roi)
+        }
+    };
+    let (mut output_size, mut scale_factors) = match output_size {
+        Option::Some(output_size) => {
+            let mut scale_factors = ArrayTrait::<T>::new();
+            let mut i = 0;
+            loop {
+                if i == r {
+                    break;
+                }
+
+                let output_size_i: T = NumberTrait::new_unscaled(
+                    (*output_size.at(i)).into(), false
+                );
+                let data_shape_i: T = NumberTrait::new_unscaled(
+                    (*(*data).shape.at(i)).into(), false
+                );
+
+                scale_factors.append(output_size_i / data_shape_i);
+                i += 1;
+            };
+
+            let (mut output_size, mut scale_factors) = match keep_aspect_ratio_policy {
+                KEEP_ASPECT_RATIO_POLICY::STRETCH => { (output_size, scale_factors.span()) },
+                KEEP_ASPECT_RATIO_POLICY::NOT_LARGER => {
+                    let mut scale = *scale_factors.at(*axes.at(0));
+                    let mut i = 1;
+                    loop {
+                        if i == axes.len() {
+                            break;
+                        }
+                        if scale > *scale_factors.at(*axes.at(i)) {
+                            scale = *scale_factors.at(*axes.at(i));
+                        }
+                        i += 1;
+                    };
+
+                    let mut scale_factors = ArrayTrait::<T>::new();
+                    let mut d = 0;
+                    loop {
+                        if d == r {
+                            break;
+                        }
+                        let mut i = 0;
+                        let item = loop {
+                            if i == axes.len() {
+                                break NumberTrait::one();
+                            }
+                            if *axes.at(i) == d {
+                                break scale;
+                            }
+                            i += 1;
+                        };
+                        scale_factors.append(item);
+                        d += 1;
+                    };
+
+                    let mut output_size = ArrayTrait::new();
+                    let mut d = 0;
+                    loop {
+                        if d == r {
+                            break;
+                        }
+                        let mut i = 0;
+                        let item = loop {
+                            if i == axes.len() {
+                                break *(*data).shape.at(d);
+                            }
+                            if *axes.at(i) == d {
+                                break NumberTrait::round(
+                                    scale
+                                        * NumberTrait::new_unscaled(
+                                            (*(*data).shape.at(d)).into(), false
+                                        )
+                                )
+                                    .try_into()
+                                    .unwrap();
+                            }
+                            i += 1;
+                        };
+                        output_size.append(item);
+                        d += 1;
+                    };
+                    (output_size.span(), scale_factors.span())
+                },
+                KEEP_ASPECT_RATIO_POLICY::NOT_SMALLER => {
+                    let mut scale = *scale_factors.at(*axes.at(0));
+                    let mut i = 1;
+                    loop {
+                        if i == axes.len() {
+                            break;
+                        }
+                        if scale < *scale_factors.at(*axes.at(i)) {
+                            scale = *scale_factors.at(*axes.at(i));
+                        }
+                        i += 1;
+                    };
+                    let mut scale_factors = ArrayTrait::<T>::new();
+                    let mut d = 0;
+                    loop {
+                        if d == r {
+                            break;
+                        }
+                        let mut i = 0;
+                        let item = loop {
+                            if i == axes.len() {
+                                break NumberTrait::one();
+                            }
+                            if *axes.at(i) == d {
+                                break scale;
+                            }
+                            i += 1;
+                        };
+                        scale_factors.append(item);
+                        d += 1;
+                    };
+
+                    let mut output_size = ArrayTrait::new();
+                    let mut d = 0;
+                    loop {
+                        if d == r {
+                            break;
+                        }
+                        let mut i = 0;
+                        let item = loop {
+                            if i == axes.len() {
+                                break *(*data).shape.at(d);
+                            }
+                            if *axes.at(i) == d {
+                                break NumberTrait::round(
+                                    scale
+                                        * NumberTrait::new_unscaled(
+                                            (*(*data).shape.at(d)).into(), false
+                                        )
+                                )
+                                    .try_into()
+                                    .unwrap();
+                            }
+                            i += 1;
+                        };
+                        output_size.append(item);
+                        d += 1;
+                    };
+                    (output_size.span(), scale_factors.span())
+                },
+            };
+
+            (output_size, scale_factors)
+        },
+        Option::None => {
+            let mut output_size = ArrayTrait::<usize>::new();
+
+            let scale_factors = match scale_factors {
+                Option::Some(scale_factors) => scale_factors,
+                Option::None => { core::panic_with_felt252('size and scale None') },
+            };
+
+            let mut i = 0;
+            loop {
+                if i == scale_factors.len() {
+                    break;
+                }
+                let scale: usize = (*scale_factors.at(i)).try_into().unwrap();
+
+                let item = *scale_factors.at(i)
+                    * NumberTrait::new_unscaled((*(*(data).shape).at(i)).into(), false);
+                output_size.append(item.try_into().unwrap());
+                i += 1;
+            };
+            (output_size.span(), scale_factors)
+        },
+    };
+
+    let mut ret = ArrayTrait::<Span<usize>>::new();
+    let mut i = 0;
+    loop {
+        let mut temp = ArrayTrait::<usize>::new();
+        if i == output_size.len() {
+            break;
+        }
+        let mut j = 0;
+        loop {
+            if j == *output_size.at(i) {
+                break;
+            }
+            temp.append(j);
+            j += 1;
+        };
+        ret.append(temp.span());
+        i += 1;
+    };
+
+    let mut ret = cartesian(ret.span());
+    let mut ret_data = ArrayTrait::new();
+
+    loop {
+        match ret.pop_front() {
+            Option::Some(X) => {
+                let mut x = ArrayTrait::<T>::new();
+                let mut i = 0;
+                loop {
+                    if i == X.len() {
+                        break;
+                    }
+                    x.append(NumberTrait::new_unscaled((*X.at(i)).into(), false));
+                    i += 1;
+                };
+                let mut x = x.span();
+                let item = interpolate_nd_with_x(
+                    data,
+                    (*data).shape.len(),
+                    scale_factors,
+                    output_size,
+                    x,
+                    antialias,
+                    mode,
+                    nearest_mode,
+                    roi,
+                    extrapolation_value,
+                    coordinate_transformation_mode,
+                    exclude_outside,
+                    cubic_coeff_a
+                );
+
+                ret_data.append(*item.data.at(0));
+            },
+            Option::None => { break; }
+        }
+    };
+
+    let mut shape = ArrayTrait::new();
+    shape.append(ret_data.len());
+    return TensorTrait::new(output_size, ret_data.span());
+}
+
+fn cartesian(mut arrays: Span<Span<usize>>,) -> Array<Array<usize>> {
+    let mut n = 1;
+    let mut i = arrays.len() - 1;
+    loop {
+        n = n * (*(arrays.at(i))).len();
+        if i == 0 {
+            break;
+        }
+        i -= 1;
+    };
+
+    let mut i = 0;
+    let mut size_arrays = ArrayTrait::new();
+    let mut m = n;
+    loop {
+        if i == arrays.len() {
+            break;
+        }
+        size_arrays.append((*(arrays.at(i))).len());
+
+        i += 1;
+    };
+    let size_arrays = size_arrays.span();
+    let mut output_arrays = ArrayTrait::<Array<usize>>::new();
+    let mut m = n;
+
+    let mut i = 0;
+    loop {
+        if i == arrays.len() {
+            break;
+        }
+        m = m / (*(arrays.at(i))).len();
+        let mut out = repeat(*(arrays.at(i)), m);
+        out = repeat_2(out, size_arrays, i);
+
+        output_arrays.append(out);
+        i += 1;
+    };
+    let output_arrays = output_arrays.span();
+
+    let mut i = 0;
+    let mut ret = ArrayTrait::<Array<usize>>::new();
+    loop {
+        if i == n {
+            break;
+        }
+        let mut j = 0;
+        let mut x = ArrayTrait::new();
+        loop {
+            if j == arrays.len() {
+                break;
+            }
+
+            x.append(*(output_arrays.at(j)).at(i));
+            j += 1;
+        };
+        ret.append(x);
+        i += 1;
+    };
+
+    return ret;
+}
+
+
+fn repeat_2(mut array: Array<usize>, size_array: Span<usize>, index: usize) -> Array<usize> {
+    let mut size = array.len();
+    let mut i = 0;
+    loop {
+        if i == index {
+            break;
+        }
+        let mut j = 1;
+        loop {
+            if j == *size_array.at(index - 1 - i) {
+                break;
+            }
+            let mut k = 0;
+            loop {
+                if k == size {
+                    break;
+                }
+                array.append(*array.at(k));
+                k += 1;
+            };
+            j += 1;
+        };
+        size = size * *size_array.at(index - 1 - i);
+        i += 1;
+    };
+    array
+}
+
+fn repeat(array: Span<usize>, m: usize,) -> Array<usize> {
+    let mut out = ArrayTrait::new();
+    let mut j = 0;
+    loop {
+        if j == array.len() {
+            break;
+        }
+        let mut k = 0;
+        loop {
+            if k == m {
+                break;
+            }
+            out.append(*array.at(j));
+            k += 1;
+        };
+        j += 1;
+    };
+
+    out
+}
+
+fn interpolate_nd_with_x<
+    T,
+    MAG,
+    +TensorTrait<T>,
+    +NumberTrait<T, MAG>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +Copy<T>,
+    +Drop<T>,
+    +TryInto<T, usize>,
+    +Into<usize, MAG>,
+    +AddEq<T>,
+    +Add<T>,
+    +Div<T>,
+    +Mul<T>,
+    +Sub<T>,
+>(
+    data: @Tensor<T>,
+    n: usize,
+    mut scale_factor: Span<T>,
+    mut output_size: Span<usize>,
+    mut x: Span<T>,
+    antialias: Option<usize>,
+    mode: MODE,
+    nearest_mode: Option<NEAREST_MODE>,
+    roi: Option<Tensor<T>>,
+    extrapolation_value: T,
+    coordinate_transformation_mode: Option<TRANSFORMATION_MODE>,
+    exclude_outside: bool,
+    cubic_coeff_a: Option<T>,
+) -> Tensor<T> {
+    if n == 1 {
+        return interpolate_1d_with_x(
+            data,
+            *scale_factor.at(0),
+            *output_size.at(0),
+            *x.at(0),
+            antialias,
+            mode,
+            nearest_mode,
+            roi,
+            extrapolation_value,
+            coordinate_transformation_mode,
+            exclude_outside,
+            cubic_coeff_a
+        );
+    }
+    let mut res1d = ArrayTrait::new();
+
+    let scale_factor_zero = match scale_factor.pop_front() {
+        Option::Some(item) => { *item },
+        Option::None => core::panic_with_felt252('scale factor empty')
+    };
+    let output_size_zero = match output_size.pop_front() {
+        Option::Some(item) => { *item },
+        Option::None => core::panic_with_felt252('output_size empty')
+    };
+    let x_zero = match x.pop_front() {
+        Option::Some(item) => { *item },
+        Option::None => core::panic_with_felt252('x empty')
+    };
+
+    let reduced_roi = match roi {
+        Option::Some(roi) => {
+            let mut reduced_roi = ArrayTrait::new();
+            let mut reduced_roi_shape = ArrayTrait::new();
+            reduced_roi_shape.append(roi.data.len() - 2);
+
+            let mut i = 1;
+            loop {
+                if i == 2 * n {
+                    break;
+                }
+                if i != n {
+                    reduced_roi.append(*roi.data.at(i));
+                }
+                i += 1;
+            };
+            Option::Some(TensorTrait::new(reduced_roi_shape.span(), reduced_roi.span()))
+        },
+        Option::None => { Option::None }
+    };
+
+    let mut i = 0;
+    loop {
+        if i == *(*data).shape.at(0) {
+            break;
+        }
+        let data = get_row_n(data, i);
+
+        let mut r = interpolate_nd_with_x(
+            @data,
+            n - 1,
+            scale_factor,
+            output_size,
+            x,
+            antialias,
+            mode,
+            nearest_mode,
+            reduced_roi,
+            extrapolation_value,
+            coordinate_transformation_mode,
+            exclude_outside,
+            cubic_coeff_a
+        );
+        loop {
+            match r.data.pop_front() {
+                Option::Some(item) => { res1d.append(*item); },
+                Option::None => { break; }
+            }
+        };
+        i += 1;
+    };
+
+    let mut shape = ArrayTrait::new();
+    shape.append(res1d.len());
+
+    let res1d = TensorTrait::new(shape.span(), res1d.span());
+
+    let reduced_roi = match roi {
+        Option::Some(roi) => {
+            let mut reduced_roi = ArrayTrait::new();
+            let mut reduced_roi_shape = ArrayTrait::new();
+
+            reduced_roi_shape.append(2);
+            reduced_roi.append(*roi.data.at(0));
+            reduced_roi.append(*roi.data.at(n));
+
+            Option::Some(TensorTrait::new(reduced_roi_shape.span(), reduced_roi.span()))
+        },
+        Option::None => { Option::None }
+    };
+
+    let a = interpolate_1d_with_x(
+        @res1d,
+        scale_factor_zero,
+        output_size_zero,
+        x_zero,
+        antialias,
+        mode,
+        nearest_mode,
+        reduced_roi,
+        extrapolation_value,
+        coordinate_transformation_mode,
+        exclude_outside,
+        cubic_coeff_a
+    );
+
+    //let mut ret = ArrayTrait::new();
+    //let mut shape = ArrayTrait::new();
+    //shape.append(2);
+    //ret.append(NumberTrait::zero());
+    return a;
+}
+
+fn get_row_n<T, +TensorTrait<T>, +Copy<T>, +Drop<T>,>(
+    data: @Tensor<T>, index: usize,
+) -> Tensor<T> {
+    let mut output_data = ArrayTrait::new();
+    let mut output_shape = ArrayTrait::new();
+    let mut stride_output = 1;
+    let mut n: usize = 0;
+
+    let mut i = 0;
+    loop {
+        if i == (*data).shape.len() {
+            break;
+        }
+        if i != 0 {
+            output_shape.append(*(*data).shape.at(i));
+            stride_output = stride_output * *(*data).shape.at(i);
+        }
+        i += 1;
+    };
+
+    let mut i = 0;
+    loop {
+        if i == stride_output {
+            break;
+        }
+        output_data.append(*(*data).data.at(index * stride_output + i));
+        i += 1;
+    };
+
+    return TensorTrait::new(output_shape.span(), output_data.span());
+}
+
+
+fn interpolate_1d_with_x<
+    T,
+    MAG,
+    +TensorTrait<T>,
+    +NumberTrait<T, MAG>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +Copy<T>,
+    +Drop<T>,
+    +TryInto<T, usize>,
+    +Into<usize, MAG>,
+    +AddEq<T>,
+    +Add<T>,
+    +Div<T>,
+    +Mul<T>,
+    +Sub<T>,
+>(
+    data: @Tensor<T>,
+    scale_factor: T,
+    output_width_int: usize,
+    x: T,
+    antialias: Option<usize>,
+    mode: MODE,
+    nearest_mode: Option<NEAREST_MODE>,
+    roi: Option<Tensor<T>>,
+    extrapolation_value: T,
+    coordinate_transformation_mode: Option<TRANSFORMATION_MODE>,
+    exclude_outside: bool,
+    cubic_coeff_a: Option<T>,
+) -> Tensor<T> {
+    let coordinate_transformation_mode = match coordinate_transformation_mode {
+        Option::Some(coordinate_transformation_mode) => coordinate_transformation_mode,
+        Option::None(_) => { TRANSFORMATION_MODE::HALF_PIXEL },
+    };
+
+    let input_width = (*data).data.len();
+    let output_width = (scale_factor * NumberTrait::new_unscaled((input_width).into(), false));
+
+    let x_ori: T = match coordinate_transformation_mode {
+        TRANSFORMATION_MODE::HALF_PIXEL => {
+            (x + NumberTrait::half()) / scale_factor - NumberTrait::half()
+        },
+        TRANSFORMATION_MODE::ALIGN_CORNERS => {
+            let mut x_ori = NumberTrait::zero();
+            if output_width != NumberTrait::one() {
+                x_ori = x
+                    * (NumberTrait::new_unscaled(input_width.into(), false) - NumberTrait::one())
+                    / (output_width - NumberTrait::one());
+            }
+            x_ori
+        },
+        TRANSFORMATION_MODE::ASYMMETRIC => { x / scale_factor },
+        TRANSFORMATION_MODE::TF_CROP_AND_RESIZE => {
+            let x_ori = match roi {
+                Option::Some(roi) => {
+                    let mut x_ori = if output_width == NumberTrait::one() {
+                        (*roi.data.at(1) - *roi.data.at(0))
+                            * (NumberTrait::new_unscaled(input_width.into(), false)
+                                - NumberTrait::one())
+                            / (NumberTrait::one() + NumberTrait::one())
+                    } else {
+                        x
+                            * (*roi.data.at(1) - *roi.data.at(0))
+                            * (NumberTrait::new_unscaled(input_width.into(), false)
+                                - NumberTrait::one())
+                            / (output_width - NumberTrait::one())
+                    };
+
+                    x_ori = x_ori
+                        + *roi.data.at(0)
+                            * (NumberTrait::new_unscaled(input_width.into(), false)
+                                - NumberTrait::one());
+
+                    if x_ori < NumberTrait::zero()
+                        || x_ori > (NumberTrait::new_unscaled(input_width.into(), false)
+                            - NumberTrait::one()) {
+                        let mut ret = ArrayTrait::new();
+                        let mut shape = ArrayTrait::new();
+                        shape.append(1);
+                        ret.append(extrapolation_value);
+                        return TensorTrait::new(shape.span(), ret.span());
+                    };
+                    x_ori
+                },
+                Option::None(_) => { core::panic_with_felt252('roi cannot be None.') },
+            };
+            x_ori
+        },
+        TRANSFORMATION_MODE::PYTORCH_HALF_PIXEL => {
+            if output_width == NumberTrait::one() {
+                NumberTrait::neg(NumberTrait::<T>::half())
+            } else {
+                (x + NumberTrait::half()) / scale_factor - NumberTrait::half()
+            }
+        },
+        TRANSFORMATION_MODE::HALF_PIXEL_SYMMETRIC => {
+            let adjustement: T = NumberTrait::new_unscaled(output_width_int.into(), false)
+                / output_width;
+            let center: T = NumberTrait::new_unscaled(input_width.into(), false)
+                / (NumberTrait::one() + NumberTrait::one());
+            let offset = center * (NumberTrait::one() - adjustement);
+            offset + (x + NumberTrait::half()) / scale_factor - NumberTrait::half()
+        },
+    };
+
+    let x_ori_int = x_ori.floor();
+
+    let ratio = if x_ori_int.try_into().unwrap() == x_ori {
+        NumberTrait::one()
+    } else {
+        x_ori - x_ori_int.try_into().unwrap()
+    };
+
+    let mut coeffs = match mode {
+        MODE::NEAREST => {
+            let coeffs = match antialias {
+                Option::Some(antialias) => core::panic_with_felt252(
+                    'antialias not for mode NEAREST'
+                ),
+                Option::None(_) => { nearest_coeffs(ratio, nearest_mode) },
+            };
+            coeffs
+        },
+        MODE::LINEAR => {
+            let coeffs = match antialias {
+                Option::Some(antialias) => {
+                    let coeffs = if antialias == 0 {
+                        linear_coeffs(ratio)
+                    } else {
+                        linear_coeffs_antialias(ratio, scale_factor)
+                    };
+                    coeffs
+                },
+                Option::None(_) => { linear_coeffs(ratio) },
+            };
+            coeffs
+        },
+        MODE::CUBIC => {
+            let coeffs = match antialias {
+                Option::Some(antialias) => {
+                    cubic_coeffs_antialias(ratio, scale_factor, cubic_coeff_a)
+                },
+                Option::None(_) => { cubic_coeffs(ratio, cubic_coeff_a) },
+            };
+            coeffs
+        },
+    };
+
+    let n = coeffs.data.len();
+
+    let (idxes, points) = get_neighbor(x_ori, n, data);
+
+    if exclude_outside {
+        let mut coeffs_exclude_outside = ArrayTrait::<T>::new();
+        let mut sum = NumberTrait::zero();
+        let mut i = 0;
+        loop {
+            if i == idxes.data.len() {
+                break;
+            }
+            if *idxes.data.at(i) {
+                coeffs_exclude_outside.append(NumberTrait::zero());
+                sum += NumberTrait::zero();
+            } else {
+                coeffs_exclude_outside.append(*coeffs.data.at(i));
+                sum += *coeffs.data.at(i);
+            }
+            i += 1;
+        };
+
+        let mut coeff_div = ArrayTrait::<T>::new();
+        let mut i = 0;
+        loop {
+            if i == n {
+                break;
+            }
+            coeff_div.append(*coeffs_exclude_outside.at(i) / sum);
+            i += 1;
+        };
+        coeffs = TensorTrait::new(coeffs.shape, coeff_div.span());
+    }
+    return TensorTrait::matmul(@coeffs, @points);
+}
+
+
+fn get_neighbor<
+    T,
+    MAG,
+    +TensorTrait<T>,
+    +NumberTrait<T, MAG>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +Copy<T>,
+    +Drop<T>,
+    +TryInto<T, usize>,
+    +Into<usize, MAG>,
+    +AddEq<T>,
+    +Add<T>,
+    +Div<T>,
+    +Mul<T>,
+    +Sub<T>,
+>(
+    mut x: T, n: usize, data: @Tensor<T>,
+) -> (Tensor<bool>, Tensor<T>) {
+    let pad_width: usize = NumberTrait::ceil(
+        NumberTrait::new_unscaled(n.into(), false)
+            / (NumberTrait::<T>::one() + NumberTrait::<T>::one())
+    )
+        .try_into()
+        .unwrap();
+    let mut padded = ArrayTrait::new();
+
+    let mut i = 0;
+    loop {
+        if i == pad_width {
+            break;
+        }
+        padded.append(*(*data).data.at(0));
+        i += 1;
+    };
+    let mut i = 0;
+    loop {
+        if i == (*data).data.len() {
+            break;
+        }
+        padded.append(*(*data).data.at(i));
+        i += 1;
+    };
+    let mut i = 0;
+    loop {
+        if i == pad_width {
+            break;
+        }
+        padded.append(*(*data).data.at((*data).data.len() - 1));
+        i += 1;
+    };
+
+    x = x + NumberTrait::new_unscaled(pad_width.into(), false);
+
+    let mut idxes = get_neighbor_idxes(x, n, padded.len());
+
+    let mut idxes_centered = ArrayTrait::new();
+    let mut ret = ArrayTrait::new();
+    let mut i = 0;
+    loop {
+        if i == idxes.data.len() {
+            break;
+        }
+        ret.append(*padded.at(*idxes.data.at(i)));
+
+        if *idxes.data.at(i) >= pad_width {
+            if (*idxes.data.at(i) - pad_width) >= (*data).data.len() {
+                idxes_centered.append(true);
+            } else {
+                idxes_centered.append(false);
+            }
+        } else {
+            idxes_centered.append(true);
+        }
+        i += 1;
+    };
+
+    let mut shape = ArrayTrait::new();
+    shape.append(idxes.data.len());
+
+    return (
+        TensorTrait::new(shape.span(), idxes_centered.span()),
+        TensorTrait::new(shape.span(), ret.span())
+    );
+}
+
+fn get_neighbor_idxes<
+    T,
+    MAG,
+    +TensorTrait<T>,
+    +NumberTrait<T, MAG>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +Copy<T>,
+    +Drop<T>,
+    +TryInto<T, usize>,
+    +Into<usize, MAG>,
+    +AddEq<T>,
+    +Add<T>,
+    +Div<T>,
+    +Mul<T>,
+    +Sub<T>,
+>(
+    mut x: T, n: usize, limit: usize,
+) -> Tensor<usize> {
+    let pad_width: usize = NumberTrait::<
+        T
+    >::ceil(
+        NumberTrait::new_unscaled(n.into(), false)
+            / (NumberTrait::<T>::one() + NumberTrait::<T>::one())
+    )
+        .try_into()
+        .unwrap();
+    let mut idxes = ArrayTrait::new();
+
+    if n % 2 == 0 {
+        let (mut i_low, mut i_high) = if x < NumberTrait::zero() {
+            (0, 1)
+        } else {
+            (NumberTrait::floor(x).try_into().unwrap(), NumberTrait::ceil(x).try_into().unwrap())
+        };
+
+        if i_high >= limit {
+            i_low = limit - 2;
+            i_high = limit - 1;
+        }
+
+        if i_low == i_high {
+            if i_low == 0 {
+                i_high = i_high + 1;
+            } else {
+                i_low = i_low - 1;
+            }
+        }
+
+        let mut i = 0;
+        loop {
+            if i == n / 2 {
+                break;
+            }
+            if i_low - i < 0 {
+                idxes.append(i_high + i);
+                i_high += 1;
+            } else {
+                idxes.append(i_low - i);
+            }
+            if i_high + i >= limit {
+                i_low -= 1;
+                idxes.append(i_low - i);
+            } else {
+                idxes.append(i_high + i);
+            }
+            i += 1;
+        }
+    } else {
+        core::panic_with_felt252('MUST BE EVEN');
+    }
+
+    idxes = bubble_sort::bubble_sort_elements(idxes);
+
+    let mut shape = ArrayTrait::new();
+    shape.append(n);
+
+    return TensorTrait::new(shape.span(), idxes.span());
+}
+
+fn linear_coeffs<
+    T,
+    MAG,
+    +NumberTrait<T, MAG>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +TensorTrait<T>,
+    +Copy<T>,
+    +Drop<T>,
+    +Sub<T>
+>(
+    mut ratio: T
+) -> Tensor<T> {
+    let mut ret = ArrayTrait::new();
+    let mut shape = ArrayTrait::new();
+    shape.append(2);
+    ret.append(NumberTrait::one() - ratio);
+    ret.append(ratio);
+    return TensorTrait::new(shape.span(), ret.span());
+}
+
+
+fn linear_coeffs_antialias<
+    T,
+    MAG,
+    +TensorTrait<T>,
+    +NumberTrait<T, MAG>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +Copy<T>,
+    +Drop<T>,
+    +TryInto<T, usize>,
+    +Into<usize, MAG>,
+    +AddEq<T>,
+    +Add<T>,
+    +Div<T>,
+    +Mul<T>,
+    +Sub<T>,
+>(
+    mut ratio: T, scale: T
+) -> Tensor<T> {
+    let scale = NumberTrait::min(scale, NumberTrait::one());
+    let start = (NumberTrait::floor(NumberTrait::neg(NumberTrait::one()) / scale)
+        + NumberTrait::one());
+    let footprint = (NumberTrait::one() + NumberTrait::one())
+        - (NumberTrait::one() + NumberTrait::one()) * start;
+
+    let mut coeffs = ArrayTrait::<T>::new();
+    let mut sum = NumberTrait::zero();
+
+    // arange and clip + compute sum
+    let mut i = start;
+    loop {
+        if i == start + footprint {
+            break;
+        }
+        let value = NumberTrait::one() - NumberTrait::abs((i - ratio) * scale);
+
+        if value < NumberTrait::zero() {
+            coeffs.append(NumberTrait::zero());
+        } else if value > NumberTrait::one() {
+            coeffs.append(NumberTrait::one());
+            sum += NumberTrait::one();
+        } else {
+            coeffs.append(value);
+            sum += value;
+        }
+        i += NumberTrait::one();
+    };
+
+    let n = coeffs.len();
+
+    let mut coeff_div = ArrayTrait::<T>::new();
+    let mut i = 0;
+    loop {
+        if i == n {
+            break;
+        }
+        coeff_div.append(*coeffs.at(i) / sum);
+        i += 1;
+    };
+
+    let mut shape = ArrayTrait::new();
+    shape.append(n);
+
+    return TensorTrait::new(shape.span(), coeff_div.span());
+}
+
+fn cubic_coeffs<
+    T,
+    MAG,
+    +TensorTrait<T>,
+    +NumberTrait<T, MAG>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +Copy<T>,
+    +Drop<T>,
+    +AddEq<T>,
+    +Add<T>,
+    +Div<T>,
+    +Mul<T>,
+    +Sub<T>,
+>(
+    mut ratio: T, A: Option<T>
+) -> Tensor<T> {
+    let one = NumberTrait::one();
+    let two = one + NumberTrait::one();
+    let three = two + NumberTrait::one();
+    let four = three + NumberTrait::one();
+    let five = four + NumberTrait::one();
+    let five = four + NumberTrait::one();
+    let eigth = four + four;
+
+    let A = match A {
+        Option::Some(A) => A,
+        Option::None(_) => { NumberTrait::neg(three / four) },
+    };
+
+    let mut coeffs = ArrayTrait::new();
+    let mut shape = ArrayTrait::new();
+
+    coeffs
+        .append(
+            ((A * (ratio + one) - five * A) * (ratio + one) + eigth * A) * (ratio + one) - four * A
+        );
+    coeffs.append(((A + two) * ratio - (A + three)) * ratio * ratio + one);
+    coeffs.append(((A + two) * (one - ratio) - (A + three)) * (one - ratio) * (one - ratio) + one);
+    coeffs
+        .append(
+            ((A * ((one - ratio) + one) - five * A) * ((one - ratio) + one) + eigth * A)
+                * ((one - ratio) + one)
+                - four * A
+        );
+
+    shape.append(4);
+    return TensorTrait::new(shape.span(), coeffs.span());
+}
+
+fn cubic_coeffs_antialias<
+    T,
+    MAG,
+    +TensorTrait<T>,
+    +NumberTrait<T, MAG>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +Copy<T>,
+    +Drop<T>,
+    +TryInto<T, usize>,
+    +Into<usize, MAG>,
+    +AddEq<T>,
+    +Add<T>,
+    +Div<T>,
+    +Mul<T>,
+    +Sub<T>,
+>(
+    mut ratio: T, scale: T, A: Option<T>
+) -> Tensor<T> {
+    let one = NumberTrait::one();
+    let two = one + NumberTrait::one();
+    let three = two + NumberTrait::one();
+    let four = three + NumberTrait::one();
+    let five = four + NumberTrait::one();
+    let five = four + NumberTrait::one();
+    let eigth = four + four;
+
+    let scale = NumberTrait::min(scale, NumberTrait::one());
+
+    let i_start = NumberTrait::floor(NumberTrait::neg(two) / scale) + NumberTrait::one();
+    let i_end = two - i_start;
+    assert(i_end > i_start, 'i_end must be greater');
+
+    let A = match A {
+        Option::Some(A) => A,
+        Option::None(_) => { NumberTrait::neg(three / four) },
+    };
+
+    let mut coeffs = ArrayTrait::new();
+    let mut sum = NumberTrait::zero();
+
+    let mut i = i_start;
+    loop {
+        if i == i_end {
+            break;
+        }
+        let value = compute_coeff(scale * (i - ratio), A);
+        coeffs.append(value);
+        sum += value;
+
+        i += NumberTrait::one();
+    };
+
+    let n = coeffs.len();
+
+    let mut coeff_div = ArrayTrait::<T>::new();
+    let mut i = 0;
+    loop {
+        if i == n {
+            break;
+        }
+        coeff_div.append(*coeffs.at(i) / sum);
+        i += 1;
+    };
+
+    let mut shape = ArrayTrait::new();
+    shape.append(n);
+
+    return TensorTrait::new(shape.span(), coeff_div.span());
+}
+
+fn compute_coeff<
+    T,
+    MAG,
+    +TensorTrait<T>,
+    +NumberTrait<T, MAG>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +Copy<T>,
+    +Drop<T>,
+    +AddEq<T>,
+    +Add<T>,
+    +Div<T>,
+    +Mul<T>,
+    +Sub<T>,
+>(
+    mut x: T, A: T
+) -> T {
+    let one = NumberTrait::one();
+    let two = one + NumberTrait::one();
+    let three = two + NumberTrait::one();
+    let four = three + NumberTrait::one();
+    let five = four + NumberTrait::one();
+    let eigth = four + four;
+
+    x = x.abs();
+    let mut x_2 = x * x;
+    let mut x_3 = x * x_2;
+    if x <= one {
+        return (A + two) * x_3 - (A + three) * x_2 + one;
+    }
+    if x < two {
+        return A * x_3 - five * A * x_2 + eigth * A * x - four * A;
+    }
+    return NumberTrait::zero();
+}
+
+
+fn nearest_coeffs<
+    T,
+    MAG,
+    +TensorTrait<T>,
+    +NumberTrait<T, MAG>,
+    +PartialOrd<T>,
+    +PartialEq<T>,
+    +Copy<T>,
+    +Drop<T>,
+    +AddEq<T>,
+    +Add<T>,
+    +Div<T>,
+    +Mul<T>,
+    +Sub<T>,
+>(
+    mut ratio: T, nearest_mode: Option<NEAREST_MODE>
+) -> Tensor<T> {
+    let nearest_mode = match nearest_mode {
+        Option::Some(nearest_mode) => { nearest_mode },
+        Option::None(_) => { NEAREST_MODE::ROUND_PREFER_FLOOR },
+    };
+
+    let mut ret = ArrayTrait::new();
+    let mut shape = ArrayTrait::new();
+    shape.append(2);
+
+    // CHECK SI C'EST UNE CONDITION ASSEZ GENERALE
+    if ratio == NumberTrait::one() {
+        ret.append(NumberTrait::zero());
+        ret.append(NumberTrait::one());
+        return TensorTrait::new(shape.span(), ret.span());
+    }
+
+    match nearest_mode {
+        NEAREST_MODE::ROUND_PREFER_FLOOR => {
+            if ratio <= NumberTrait::half() {
+                ret.append(NumberTrait::one());
+                ret.append(NumberTrait::zero());
+                return TensorTrait::new(shape.span(), ret.span());
+            } else {
+                ret.append(NumberTrait::zero());
+                ret.append(NumberTrait::one());
+                return TensorTrait::new(shape.span(), ret.span());
+            }
+        },
+        NEAREST_MODE::ROUND_PREFER_CEIL => {
+            if ratio < NumberTrait::half() {
+                ret.append(NumberTrait::one());
+                ret.append(NumberTrait::zero());
+                return TensorTrait::new(shape.span(), ret.span());
+            } else {
+                ret.append(NumberTrait::zero());
+                ret.append(NumberTrait::one());
+                return TensorTrait::new(shape.span(), ret.span());
+            }
+        },
+        NEAREST_MODE::FLOOR => {
+            ret.append(NumberTrait::one());
+            ret.append(NumberTrait::zero());
+            return TensorTrait::new(shape.span(), ret.span());
+        },
+        NEAREST_MODE::CEIL => {
+            ret.append(NumberTrait::zero());
+            ret.append(NumberTrait::one());
+            return TensorTrait::new(shape.span(), ret.span());
+        },
+    }
+}
+
diff --git a/src/operators/tensor/math/sin.cairo b/src/operators/tensor/math/sin.cairo
index b1471d77e..cc810eab7 100644
--- a/src/operators/tensor/math/sin.cairo
+++ b/src/operators/tensor/math/sin.cairo
@@ -12,10 +12,10 @@ use orion::operators::tensor::core::{Tensor, TensorTrait};
 fn sin<
     T,
     MAG,
-    impl FFixedTrait: FixedTrait<T, MAG>,
-    impl FTensor: TensorTrait<T>,
-    impl FCopy: Copy<T>,
-    impl FDrop: Drop<T>,
+    impl TNumberTrait: NumberTrait<T, MAG>,
+    impl TTensor: TensorTrait<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
 >(
     mut self: Tensor<T>
 ) -> Tensor<T> {
diff --git a/src/operators/tensor/math/sinh.cairo b/src/operators/tensor/math/sinh.cairo
index 5d3ef828b..7c3373288 100644
--- a/src/operators/tensor/math/sinh.cairo
+++ b/src/operators/tensor/math/sinh.cairo
@@ -12,10 +12,10 @@ use orion::operators::tensor::core::{Tensor, TensorTrait};
 fn sinh<
     T,
     MAG,
-    impl FFixedTrait: FixedTrait<T, MAG>,
-    impl FTensor: TensorTrait<T>,
-    impl FCopy: Copy<T>,
-    impl FDrop: Drop<T>,
+    impl TNumberTrait: NumberTrait<T, MAG>,
+    impl TTensor: TensorTrait<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
 >(
     mut self: Tensor<T>
 ) -> Tensor<T> {
diff --git a/src/operators/tensor/math/sqrt.cairo b/src/operators/tensor/math/sqrt.cairo
index 84d23c150..f3111bed9 100644
--- a/src/operators/tensor/math/sqrt.cairo
+++ b/src/operators/tensor/math/sqrt.cairo
@@ -11,10 +11,10 @@ use orion::operators::tensor::core::{Tensor, TensorTrait};
 fn sqrt<
     T,
     MAG,
-    impl FFixedTrait: FixedTrait<T, MAG>,
-    impl FTensor: TensorTrait<T>,
-    impl FCopy: Copy<T>,
-    impl FDrop: Drop<T>,
+    impl TNumberTrait: NumberTrait<T, MAG>,
+    impl TTensor: TensorTrait<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
 >(
     mut self: Tensor<T>
 ) -> Tensor<T> {
diff --git a/src/operators/tensor/math/tanh.cairo b/src/operators/tensor/math/tanh.cairo
index b94fba485..f6f3eb6e1 100644
--- a/src/operators/tensor/math/tanh.cairo
+++ b/src/operators/tensor/math/tanh.cairo
@@ -12,10 +12,10 @@ use orion::operators::tensor::core::{Tensor, TensorTrait};
 fn tanh<
     T,
     MAG,
-    impl FFixedTrait: FixedTrait<T, MAG>,
-    impl FTensor: TensorTrait<T>,
-    impl FCopy: Copy<T>,
-    impl FDrop: Drop<T>,
+    impl TNumberTrait: NumberTrait<T, MAG>,
+    impl TTensor: TensorTrait<T>,
+    impl TCopy: Copy<T>,
+    impl TDrop: Drop<T>,
 >(
     mut self: Tensor<T>
 ) -> Tensor<T> {
diff --git a/tests/ml.cairo b/tests/ml.cairo
index bb54ef56f..4a5abf9a8 100644
--- a/tests/ml.cairo
+++ b/tests/ml.cairo
@@ -1,4 +1,5 @@
 mod tree_ensemble_classifier;
 mod tree_ensemble_regressor;
 mod linear_regressor_test;
-
+mod linear_classifier_test;
+mod svm_regressor_test;
diff --git a/tests/ml/linear_classifier_test.cairo b/tests/ml/linear_classifier_test.cairo
new file mode 100644
index 000000000..e0c892328
--- /dev/null
+++ b/tests/ml/linear_classifier_test.cairo
@@ -0,0 +1,379 @@
+use orion::numbers::FP16x16;
+use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor, U32Tensor};
+
+use orion::operators::ml::linear::linear_classifier::{
+    LinearClassifierTrait, POST_TRANSFORM, LinearClassifier
+};
+use core::debug::PrintTrait;
+
+#[test]
+#[available_gas(200000000000)]
+fn test_linear_classifier_multi_none() {
+    let (mut classifier, X) = linear_classifier_helper(POST_TRANSFORM::NONE);
+
+    let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+
+    // ASSERT LABELS
+    assert(*labels[0] == 0, 'labels[0]');
+    assert(*labels[1] == 2, 'labels[1]');
+    assert(*labels[2] == 2, 'labels[2]');
+    assert(labels.len() == 3, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(*scores.data[0] == FP16x16 { mag: 157942, sign: false }, '*scores[0] == 2.41');
+    assert(*scores.data[1] == FP16x16 { mag: 138936, sign: true }, '*scores[1] == -2.12');
+    assert(*scores.data[2] == FP16x16 { mag: 38666, sign: false }, '*scores[2] == 0.59');
+    assert(*scores.data[3] == FP16x16 { mag: 43910, sign: false }, '*scores[3] == 0.67');
+    assert(*scores.data[4] == FP16x16 { mag: 74710, sign: true }, '*scores[4] == -1.14');
+    assert(*scores.data[5] == FP16x16 { mag: 88472, sign: false }, '*scores[5] == 1.35');
+    assert(*scores.data[6] == FP16x16 { mag: 70122, sign: true }, '*scores[6] == -1.07');
+    assert(*scores.data[7] == FP16x16 { mag: 10484, sign: true }, '*scores[7] == -0.16');
+    assert(*scores.data[8] == FP16x16 { mag: 138278, sign: false }, '*scores[8] == 2.11');
+}
+
+
+#[test]
+#[available_gas(200000000000)]
+fn test_linear_classifier_multi_softmax() {
+    let (mut classifier, X) = linear_classifier_helper(POST_TRANSFORM::SOFTMAX);
+
+    let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+
+    // ASSERT LABELS
+    assert(*labels[0] == 0, 'labels[0]');
+    assert(*labels[1] == 2, 'labels[1]');
+    assert(*labels[2] == 2, 'labels[2]');
+    assert(labels.len() == 3, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(*scores.data[0] == FP16x16 { mag: 55879, sign: false }, '*scores[0] == 0.852656');
+    assert(*scores.data[1] == FP16x16 { mag: 602, sign: false }, '*scores[1] == 0.009192');
+    assert(*scores.data[2] == FP16x16 { mag: 9053, sign: false }, '*scores[2] == 0.138152');
+    assert(*scores.data[3] == FP16x16 { mag: 20888, sign: false }, '*scores[3] == 0.318722');
+    assert(*scores.data[4] == FP16x16 { mag: 3418, sign: false }, '*scores[4] == 0.05216');
+    assert(*scores.data[5] == FP16x16 { mag: 41229, sign: false }, '*scores[5] == 0.629118');
+    assert(*scores.data[6] == FP16x16 { mag: 2380, sign: false }, '*scores[6] == 0.036323');
+    assert(*scores.data[7] == FP16x16 { mag: 5914, sign: false }, '*scores[7] == 0.090237');
+    assert(*scores.data[8] == FP16x16 { mag: 57241, sign: false }, '*scores[8] == 0.87344');
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_linear_classifier_multi_softmax_zero() {
+    let (mut classifier, X) = linear_classifier_helper(POST_TRANSFORM::SOFTMAXZERO);
+
+    let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+
+    // ASSERT LABELS
+    assert(*labels[0] == 0, 'labels[0]');
+    assert(*labels[1] == 2, 'labels[1]');
+    assert(*labels[2] == 2, 'labels[2]');
+    assert(labels.len() == 3, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(*scores.data[0] == FP16x16 { mag: 55879, sign: false }, '*scores[0] == 0.852656');
+    assert(*scores.data[1] == FP16x16 { mag: 602, sign: false }, '*scores[1] == 0.009192');
+    assert(*scores.data[2] == FP16x16 { mag: 9053, sign: false }, '*scores[2] == 0.138152');
+    assert(*scores.data[3] == FP16x16 { mag: 20888, sign: false }, '*scores[3] == 0.318722');
+    assert(*scores.data[4] == FP16x16 { mag: 3418, sign: false }, '*scores[4] == 0.05216');
+    assert(*scores.data[5] == FP16x16 { mag: 41229, sign: false }, '*scores[5] == 0.629118');
+    assert(*scores.data[6] == FP16x16 { mag: 2380, sign: false }, '*scores[6] == 0.036323');
+    assert(*scores.data[7] == FP16x16 { mag: 5914, sign: false }, '*scores[7] == 0.090237');
+    assert(*scores.data[8] == FP16x16 { mag: 57241, sign: false }, '*scores[8] == 0.87344');
+}
+
+
+#[test]
+#[available_gas(200000000000)]
+fn test_linear_classifier_multi_logistic() {
+    let (mut classifier, X) = linear_classifier_helper(POST_TRANSFORM::LOGISTIC);
+
+    let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+
+    // ASSERT LABELS
+    assert(*labels[0] == 0, 'labels[0] == 0');
+    assert(*labels[1] == 2, 'labels[1] == 2');
+    assert(*labels[2] == 2, 'labels[2] == 2');
+    assert(labels.len() == 3, 'len(labels) == 3');
+
+    // ASSERT SCORES
+    assert(*scores.data[0] == FP16x16 { mag: 60135, sign: false }, '*scores[0] == 0.917587');
+    assert(*scores.data[1] == FP16x16 { mag: 7023, sign: false }, '*scores[1] == 0.107168');
+    assert(*scores.data[2] == FP16x16 { mag: 42163, sign: false }, '*scores[2] == 0.643365');
+    assert(*scores.data[3] == FP16x16 { mag: 43351, sign: false }, '*scores[3] == 0.661503');
+    assert(*scores.data[4] == FP16x16 { mag: 15881, sign: false }, '*scores[4] == 0.24232');
+    assert(*scores.data[5] == FP16x16 { mag: 52043, sign: false }, '*scores[5] == 0.79413');
+    assert(*scores.data[6] == FP16x16 { mag: 16738, sign: false }, '*scores[6] == 0.255403');
+    assert(*scores.data[7] == FP16x16 { mag: 30152, sign: false }, '*scores[7] == 0.460085');
+    assert(*scores.data[8] == FP16x16 { mag: 58450, sign: false }, '*scores[8] == 0.891871');
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_linear_classifier_binary_none() {
+    let (mut classifier, X) = linear_classifier_helper_binary(POST_TRANSFORM::NONE);
+
+    let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+
+    // ASSERT LABELS
+    assert(*labels[0] == 1, 'labels[0]');
+    assert(*labels[1] == 1, 'labels[1]');
+    assert(labels.len() == 2, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(*scores.data[0] == FP16x16 { mag: 624559, sign: true }, '*scores[0] == -9.53');
+    assert(*scores.data[1] == FP16x16 { mag: 624559, sign: false }, '*scores[1] == 9.53');
+    assert(*scores.data[2] == FP16x16 { mag: 435817, sign: true }, '*scores[2] == -6.65');
+    assert(*scores.data[3] == FP16x16 { mag: 435817, sign: false }, '*scores[3] == 6.65');
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_linear_classifier_binary_logistic() {
+    let (mut classifier, X) = linear_classifier_helper_binary(POST_TRANSFORM::LOGISTIC);
+
+    let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+
+    // ASSERT LABELS
+    assert(*labels[0] == 1, 'labels[0]');
+    assert(*labels[1] == 1, 'labels[1]');
+    assert(labels.len() == 2, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(*scores.data[0] == FP16x16 { mag: 4, sign: false }, '*scores[0] == 7.263436e-05');
+    assert(*scores.data[1] == FP16x16 { mag: 65532, sign: false }, '*scores[1] == 9.999274e-01');
+    assert(*scores.data[2] == FP16x16 { mag: 84, sign: false }, '*scores[2] == 1.292350e-03');
+    assert(*scores.data[3] == FP16x16 { mag: 65452, sign: false }, '*scores[3] == 9.999983e-01');
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_linear_classifier_binary_softmax() {
+    let (mut classifier, X) = linear_classifier_helper_binary(POST_TRANSFORM::SOFTMAX);
+
+    let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+    // ASSERT LABELS
+    assert(*labels[0] == 1, 'labels[0]');
+    assert(*labels[1] == 1, 'labels[1]');
+    assert(labels.len() == 2, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(*scores.data[0] == FP16x16 { mag: 0, sign: false }, '*scores[0] == 5.276517e-09');
+    assert(*scores.data[1] == FP16x16 { mag: 65535, sign: false }, '*scores[1] == 1.000000');
+    assert(*scores.data[2] == FP16x16 { mag: 0, sign: false }, '*scores[2] == 1.674492e-06');
+    assert(*scores.data[3] == FP16x16 { mag: 65535, sign: false }, '*scores[3] ==  9.999983e-01');
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_linear_classifier_binary_softmax_zero() {
+    let (mut classifier, X) = linear_classifier_helper_binary(POST_TRANSFORM::SOFTMAXZERO);
+
+    let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+    // ASSERT LABELS
+    assert(*labels[0] == 1, 'labels[0]');
+    assert(*labels[1] == 1, 'labels[1]');
+    assert(labels.len() == 2, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(*scores.data[0] == FP16x16 { mag: 0, sign: false }, '*scores[0] == 5.276517e-09');
+    assert(*scores.data[1] == FP16x16 { mag: 65535, sign: false }, '*scores[1] == 1.000000');
+    assert(*scores.data[2] == FP16x16 { mag: 0, sign: false }, '*scores[2] == 1.674492e-06');
+    assert(*scores.data[3] == FP16x16 { mag: 65535, sign: false }, '*scores[3] ==  9.999983e-01');
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_linear_classifier_unary_none() {
+    let (mut classifier, X) = linear_classifier_helper_unary(POST_TRANSFORM::NONE);
+
+    let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+
+    // ASSERT LABELS
+    assert(*labels[0] == 1, 'labels[0]');
+    assert(*labels[1] == 0, 'labels[1]');
+    assert(labels.len() == 2, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(*scores.data[0] == FP16x16 { mag: 146146, sign: false }, '*scores[0] == 2.23');
+    assert(*scores.data[1] == FP16x16 { mag: 42596, sign: true }, '*scores[1] == -0.65');
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_linear_classifier_unary_logistic() {
+    let (mut classifier, X) = linear_classifier_helper_unary(POST_TRANSFORM::LOGISTIC);
+
+    let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+
+    // ASSERT LABELS
+    assert(*labels[0] == 1, 'labels[0]');
+    assert(*labels[1] == 0, 'labels[1]');
+    assert(labels.len() == 2, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(*scores.data[0] == FP16x16 { mag: 59173, sign: false }, '*scores[0] == 0.902911');
+    assert(*scores.data[1] == FP16x16 { mag: 22479, sign: false }, '*scores[1] == 0.34299');
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_linear_classifier_unary_softmax() {
+    let (mut classifier, X) = linear_classifier_helper_unary(POST_TRANSFORM::SOFTMAX);
+
+    let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+
+    // ASSERT LABELS
+    assert(*labels[0] == 1, 'labels[0]');
+    assert(*labels[1] == 1, 'labels[1]');
+    assert(labels.len() == 2, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(*scores.data[0] == FP16x16 { mag: 65536, sign: false }, '*scores[0] == 1');
+    assert(*scores.data[1] == FP16x16 { mag: 65536, sign: false }, '*scores[1] == 1');
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_linear_classifier_unary_softmax_zero() {
+    let (mut classifier, X) = linear_classifier_helper_unary(POST_TRANSFORM::SOFTMAXZERO);
+
+    let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+
+    // ASSERT LABELS
+    assert(*labels[0] == 1, 'labels[0]');
+    assert(*labels[1] == 1, 'labels[1]');
+    assert(labels.len() == 2, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(*scores.data[0] == FP16x16 { mag: 65536, sign: false }, '*scores[0] == 1');
+    assert(*scores.data[1] == FP16x16 { mag: 65536, sign: false }, '*scores[1] == 1');
+}
+
+
+// ============ HELPER ============ //
+
+fn linear_classifier_helper(
+    post_transform: POST_TRANSFORM
+) -> (LinearClassifier<FP16x16>, Tensor<FP16x16>) {
+    let classlabels: Span<usize> = array![0, 1, 2].span();
+    let classlabels = Option::Some(classlabels);
+
+    let classlabels_strings: Option<Span<FP16x16>> = Option::None;
+
+    let coefficients: Span<FP16x16> = array![
+        FP16x16 { mag: 38011, sign: true },
+        FP16x16 { mag: 19005, sign: true },
+        FP16x16 { mag: 5898, sign: true },
+        FP16x16 { mag: 38011, sign: false },
+        FP16x16 { mag: 19005, sign: false },
+        FP16x16 { mag: 5898, sign: false },
+    ]
+        .span();
+
+    let intercepts: Span<FP16x16> = array![
+        FP16x16 { mag: 176947, sign: false },
+        FP16x16 { mag: 176947, sign: true },
+        FP16x16 { mag: 32768, sign: false },
+    ]
+        .span();
+    let intercepts = Option::Some(intercepts);
+
+    let multi_class: usize = 0;
+
+    let mut classifier: LinearClassifier<FP16x16> = LinearClassifier {
+        classlabels, coefficients, intercepts, multi_class, post_transform
+    };
+
+    let mut X: Tensor<FP16x16> = TensorTrait::new(
+        array![3, 2].span(),
+        array![
+            FP16x16 { mag: 0, sign: false },
+            FP16x16 { mag: 65536, sign: false },
+            FP16x16 { mag: 131072, sign: false },
+            FP16x16 { mag: 196608, sign: false },
+            FP16x16 { mag: 262144, sign: false },
+            FP16x16 { mag: 327680, sign: false },
+        ]
+            .span()
+    );
+
+    (classifier, X)
+}
+
+
+fn linear_classifier_helper_binary(
+    post_transform: POST_TRANSFORM
+) -> (LinearClassifier<FP16x16>, Tensor<FP16x16>) {
+    let classlabels: Span<usize> = array![0, 1].span();
+    let classlabels = Option::Some(classlabels);
+
+    let coefficients: Span<FP16x16> = array![
+        FP16x16 { mag: 38011, sign: true },
+        FP16x16 { mag: 19005, sign: true },
+        FP16x16 { mag: 5898, sign: true },
+    ]
+        .span();
+
+    let intercepts: Span<FP16x16> = array![FP16x16 { mag: 655360, sign: false },].span();
+    let intercepts = Option::Some(intercepts);
+
+    let multi_class: usize = 0;
+
+    let mut classifier: LinearClassifier<FP16x16> = LinearClassifier {
+        classlabels, coefficients, intercepts, multi_class, post_transform
+    };
+
+    let mut X: Tensor<FP16x16> = TensorTrait::new(
+        array![2, 3].span(),
+        array![
+            FP16x16 { mag: 0, sign: false },
+            FP16x16 { mag: 65536, sign: false },
+            FP16x16 { mag: 131072, sign: false },
+            FP16x16 { mag: 196608, sign: false },
+            FP16x16 { mag: 262144, sign: false },
+            FP16x16 { mag: 327680, sign: false },
+        ]
+            .span()
+    );
+
+    (classifier, X)
+}
+
+fn linear_classifier_helper_unary(
+    post_transform: POST_TRANSFORM
+) -> (LinearClassifier<FP16x16>, Tensor<FP16x16>) {
+    let classlabels: Span<usize> = array![1].span();
+    let classlabels = Option::Some(classlabels);
+
+    let coefficients: Span<FP16x16> = array![
+        FP16x16 { mag: 38011, sign: true },
+        FP16x16 { mag: 19005, sign: true },
+        FP16x16 { mag: 5898, sign: true },
+    ]
+        .span();
+
+    let intercepts: Span<FP16x16> = array![FP16x16 { mag: 176947, sign: false },].span();
+    let intercepts = Option::Some(intercepts);
+
+    let multi_class: usize = 0;
+
+    let mut classifier: LinearClassifier<FP16x16> = LinearClassifier {
+        classlabels, coefficients, intercepts, multi_class, post_transform
+    };
+
+    let mut X: Tensor<FP16x16> = TensorTrait::new(
+        array![2, 3].span(),
+        array![
+            FP16x16 { mag: 0, sign: false },
+            FP16x16 { mag: 65536, sign: false },
+            FP16x16 { mag: 131072, sign: false },
+            FP16x16 { mag: 196608, sign: false },
+            FP16x16 { mag: 262144, sign: false },
+            FP16x16 { mag: 327680, sign: false },
+        ]
+            .span()
+    );
+
+    (classifier, X)
+}
diff --git a/tests/ml/svm_regressor_test.cairo b/tests/ml/svm_regressor_test.cairo
new file mode 100644
index 000000000..afa924e56
--- /dev/null
+++ b/tests/ml/svm_regressor_test.cairo
@@ -0,0 +1,284 @@
+use orion::numbers::FP16x16;
+use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor, U32Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+
+use orion::operators::ml::svm::svm_regressor::{SVMRegressorTrait, POST_TRANSFORM, SVMRegressor};
+use orion::operators::ml::svm::core::{KERNEL_TYPE};
+
+
+#[test]
+#[available_gas(200000000000)]
+fn test_svm_regressor_linear() {
+    let kernel_params: Span<FP16x16> = array![
+        FP16x16 { mag: 27812, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 196608, sign: false }
+    ]
+        .span();
+    let kernel_type = KERNEL_TYPE::LINEAR;
+    let (mut regressor, X) = svm_regressor_helper(kernel_type, kernel_params);
+
+    let scores = SVMRegressorTrait::predict(ref regressor, X);
+
+    let mut expected_scores: Tensor<FP16x16> = TensorTrait::new(
+        array![3, 1].span(),
+        array![
+            FP16x16 { mag: 30684, sign: true },
+            FP16x16 { mag: 14908, sign: false },
+            FP16x16 { mag: 60501, sign: false },
+        ]
+            .span()
+    );
+    assert_eq(scores, expected_scores);
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_svm_regressor_poly() {
+    let kernel_params: Span<FP16x16> = array![
+        FP16x16 { mag: 22456, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 196608, sign: false }
+    ]
+        .span();
+
+    let kernel_type = KERNEL_TYPE::POLY;
+    let (mut regressor, X) = svm_regressor_helper(kernel_type, kernel_params);
+
+    let scores = SVMRegressorTrait::predict(ref regressor, X);
+
+    let mut expected_scores: Tensor<FP16x16> = TensorTrait::new(
+        array![3, 1].span(),
+        array![
+            FP16x16 { mag: 34542, sign: false },
+            FP16x16 { mag: 35623, sign: false },
+            FP16x16 { mag: 35815, sign: false },
+        ]
+            .span()
+    );
+    assert_eq(scores, expected_scores);
+}
+
+
+#[test]
+#[available_gas(200000000000)]
+fn test_svm_regressor_rbf() {
+    let kernel_params: Span<FP16x16> = array![
+        FP16x16 { mag: 19848, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 196608, sign: false }
+    ]
+        .span();
+    let kernel_type = KERNEL_TYPE::RBF;
+    let (mut regressor, X) = svm_regressor_helper(kernel_type, kernel_params);
+
+    let scores = SVMRegressorTrait::predict(ref regressor, X);
+
+    let mut expected_scores: Tensor<FP16x16> = TensorTrait::new(
+        array![3, 1].span(),
+        array![
+            FP16x16 { mag: 19376, sign: false },
+            FP16x16 { mag: 31318, sign: false },
+            FP16x16 { mag: 45566, sign: false },
+        ]
+            .span()
+    );
+    assert_eq(scores, expected_scores);
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_svm_regressor_sigmoid() {
+    let kernel_params: Span<FP16x16> = array![
+        FP16x16 { mag: 20108, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 196608, sign: false }
+    ]
+        .span();
+    let kernel_type = KERNEL_TYPE::SIGMOID;
+    let (mut regressor, X) = svm_regressor_helper(kernel_type, kernel_params);
+
+    let scores = SVMRegressorTrait::predict(ref regressor, X);
+
+    let mut expected_scores: Tensor<FP16x16> = TensorTrait::new(
+        array![3, 1].span(),
+        array![
+            FP16x16 { mag: 15683, sign: false },
+            FP16x16 { mag: 29421, sign: false },
+            FP16x16 { mag: 43364, sign: false },
+        ]
+            .span()
+    );
+    assert_eq(scores, expected_scores);
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_svm_regressor_linear_one_class_0() {
+    let post_transform = POST_TRANSFORM::NONE;
+    let one_class = 0;
+    let (mut regressor, X) = svm_regressor_linear_helper(post_transform, one_class);
+
+    let scores = SVMRegressorTrait::predict(ref regressor, X);
+
+    let mut expected_scores: Tensor<FP16x16> = TensorTrait::new(
+        array![3, 1].span(),
+        array![
+            FP16x16 { mag: 63484, sign: false },
+            FP16x16 { mag: 74218, sign: false },
+            FP16x16 { mag: 84953, sign: false },
+        ]
+            .span()
+    );
+    assert_eq(scores, expected_scores);
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_svm_regressor_linear_one_class_1() {
+    let post_transform = POST_TRANSFORM::NONE;
+    let one_class = 1;
+    let (mut regressor, X) = svm_regressor_linear_helper(post_transform, one_class);
+
+    let scores = SVMRegressorTrait::predict(ref regressor, X);
+
+    let mut expected_scores: Tensor<FP16x16> = TensorTrait::new(
+        array![3, 1].span(),
+        array![
+            FP16x16 { mag: 65536, sign: false },
+            FP16x16 { mag: 65536, sign: false },
+            FP16x16 { mag: 65536, sign: false },
+        ]
+            .span()
+    );
+    assert_eq(scores, expected_scores);
+}
+
+
+// ============ HELPER ============ //
+
+fn svm_regressor_helper(
+    kernel_type: KERNEL_TYPE, kernel_params: Span<FP16x16>
+) -> (SVMRegressor<FP16x16>, Tensor<FP16x16>) {
+    let coefficients: Span<FP16x16> = array![
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 65536, sign: true },
+        FP16x16 { mag: 54959, sign: false },
+        FP16x16 { mag: 54959, sign: true },
+        FP16x16 { mag: 29299, sign: false },
+        FP16x16 { mag: 65536, sign: true },
+        FP16x16 { mag: 36236, sign: false }
+    ]
+        .span();
+
+    let n_supports: usize = 7;
+    let one_class: usize = 0;
+    let rho: Span<FP16x16> = array![FP16x16 { mag: 35788, sign: false }].span();
+
+    let support_vectors: Span<FP16x16> = array![
+        FP16x16 { mag: 8421, sign: true },
+        FP16x16 { mag: 5842, sign: false },
+        FP16x16 { mag: 4510, sign: false },
+        FP16x16 { mag: 5202, sign: true },
+        FP16x16 { mag: 14783, sign: true },
+        FP16x16 { mag: 17380, sign: true },
+        FP16x16 { mag: 60595, sign: false },
+        FP16x16 { mag: 1674, sign: true },
+        FP16x16 { mag: 38669, sign: true },
+        FP16x16 { mag: 63803, sign: false },
+        FP16x16 { mag: 87720, sign: true },
+        FP16x16 { mag: 22236, sign: false },
+        FP16x16 { mag: 61816, sign: false },
+        FP16x16 { mag: 34267, sign: true },
+        FP16x16 { mag: 36418, sign: false },
+        FP16x16 { mag: 27471, sign: false },
+        FP16x16 { mag: 28421, sign: false },
+        FP16x16 { mag: 69270, sign: true },
+        FP16x16 { mag: 152819, sign: false },
+        FP16x16 { mag: 4065, sign: false },
+        FP16x16 { mag: 62274, sign: true }
+    ]
+        .span();
+
+    let post_transform = POST_TRANSFORM::NONE;
+
+    let mut regressor: SVMRegressor<FP16x16> = SVMRegressor {
+        coefficients,
+        kernel_params,
+        kernel_type,
+        n_supports,
+        one_class,
+        post_transform,
+        rho,
+        support_vectors,
+    };
+
+    let mut X: Tensor<FP16x16> = TensorTrait::new(
+        array![3, 3].span(),
+        array![
+            FP16x16 { mag: 32768, sign: true },
+            FP16x16 { mag: 26214, sign: true },
+            FP16x16 { mag: 19660, sign: true },
+            FP16x16 { mag: 13107, sign: true },
+            FP16x16 { mag: 6553, sign: true },
+            FP16x16 { mag: 0, sign: false },
+            FP16x16 { mag: 6553, sign: false },
+            FP16x16 { mag: 13107, sign: false },
+            FP16x16 { mag: 19660, sign: false },
+        ]
+            .span()
+    );
+
+    (regressor, X)
+}
+
+fn svm_regressor_linear_helper(
+    post_transform: POST_TRANSFORM, one_class: usize
+) -> (SVMRegressor<FP16x16>, Tensor<FP16x16>) {
+    let coefficients: Span<FP16x16> = array![
+        FP16x16 { mag: 18540, sign: false },
+        FP16x16 { mag: 1746, sign: true },
+        FP16x16 { mag: 1097, sign: false }
+    ]
+        .span();
+    let kernel_params: Span<FP16x16> = array![
+        FP16x16 { mag: 65, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 196608, sign: false }
+    ]
+        .span();
+    let kernel_type = KERNEL_TYPE::LINEAR;
+    let n_supports: usize = 0;
+    let rho: Span<FP16x16> = array![FP16x16 { mag: 81285, sign: false }].span();
+
+    let support_vectors: Span<FP16x16> = array![].span();
+
+    let mut regressor: SVMRegressor<FP16x16> = SVMRegressor {
+        coefficients,
+        kernel_params,
+        kernel_type,
+        n_supports,
+        one_class,
+        post_transform,
+        rho,
+        support_vectors,
+    };
+    let mut X: Tensor<FP16x16> = TensorTrait::new(
+        array![3, 3].span(),
+        array![
+            FP16x16 { mag: 65536, sign: true },
+            FP16x16 { mag: 52428, sign: true },
+            FP16x16 { mag: 39321, sign: true },
+            FP16x16 { mag: 26214, sign: true },
+            FP16x16 { mag: 13107, sign: true },
+            FP16x16 { mag: 0, sign: false },
+            FP16x16 { mag: 13107, sign: false },
+            FP16x16 { mag: 26214, sign: false },
+            FP16x16 { mag: 39321, sign: false },
+        ]
+            .span()
+    );
+
+    (regressor, X)
+}
diff --git a/tests/ml/tree_ensemble_classifier.cairo b/tests/ml/tree_ensemble_classifier.cairo
index cee9ee80d..441aabb34 100644
--- a/tests/ml/tree_ensemble_classifier.cairo
+++ b/tests/ml/tree_ensemble_classifier.cairo
@@ -216,6 +216,119 @@ fn test_tree_ensemble_classifier_multi_pt_logistic() {
     );
 }
 
+#[test]
+#[available_gas(200000000000)]
+fn test_tree_ensemble_classifier_binary_none() {
+    let (mut classifier, X) = tree_ensemble_classifier_binary_class_helper(POST_TRANSFORM::NONE);
+
+    let (labels, mut scores) = TreeEnsembleClassifierTrait::predict(ref classifier, X);
+
+    // ASSERT LABELS
+    assert(*labels[0] == 1, 'labels[0]');
+    assert(labels.len() == 1, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(
+        relative_eq(@scores.get(0, 0).unwrap(), @FP16x16 { mag: 0, sign: false }) == true,
+        'score[0, 0]'
+    );
+    assert(
+        relative_eq(@scores.get(0, 1).unwrap(), @FP16x16 { mag: 65536, sign: false }) == true,
+        'score[0, 1]'
+    );
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_tree_ensemble_classifier_binary_logistic() {
+    let (mut classifier, X) = tree_ensemble_classifier_binary_class_helper(
+        POST_TRANSFORM::LOGISTIC
+    );
+
+    let (labels, mut scores) = TreeEnsembleClassifierTrait::predict(ref classifier, X);
+
+    // ASSERT LABELS
+    assert(*labels[0] == 1, 'labels[0]');
+    assert(labels.len() == 1, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(
+        relative_eq(@scores.get(0, 0).unwrap(), @FP16x16 { mag: 17625, sign: false }) == true,
+        'score[0, 0]'
+    );
+    assert(
+        relative_eq(@scores.get(0, 1).unwrap(), @FP16x16 { mag: 47910, sign: false }) == true,
+        'score[0, 1]'
+    );
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_tree_ensemble_classifier_binary_softmax() {
+    let (mut classifier, X) = tree_ensemble_classifier_binary_class_helper(POST_TRANSFORM::SOFTMAX);
+
+    let (labels, mut scores) = TreeEnsembleClassifierTrait::predict(ref classifier, X);
+
+    // ASSERT LABELS
+    assert(*labels[0] == 1, 'labels[0]');
+    assert(labels.len() == 1, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(
+        relative_eq(@scores.get(0, 0).unwrap(), @FP16x16 { mag: 7812, sign: false }) == true,
+        'score[0, 0]'
+    );
+    assert(
+        relative_eq(@scores.get(0, 1).unwrap(), @FP16x16 { mag: 57723, sign: false }) == true,
+        'score[0, 1]'
+    );
+}
+
+#[test]
+#[available_gas(200000000000)]
+fn test_tree_ensemble_classifier_binary_softmax_zero() {
+    let (mut classifier, X) = tree_ensemble_classifier_binary_class_helper(
+        POST_TRANSFORM::SOFTMAXZERO
+    );
+
+    let (labels, mut scores) = TreeEnsembleClassifierTrait::predict(ref classifier, X);
+
+    // ASSERT LABELS
+    assert(*labels[0] == 1, 'labels[0]');
+    assert(labels.len() == 1, 'len(labels)');
+
+    // ASSERT SCORES
+    assert(
+        relative_eq(@scores.get(0, 0).unwrap(), @FP16x16 { mag: 7812, sign: false }) == true,
+        'score[0, 0]'
+    );
+    assert(
+        relative_eq(@scores.get(0, 1).unwrap(), @FP16x16 { mag: 57723, sign: false }) == true,
+        'score[0, 1]'
+    );
+}
+
+// #[test]
+// #[available_gas(200000000000)]
+// fn test_tree_ensemble_classifier_binary_probit() {
+//     let (mut classifier, X) = tree_ensemble_classifier_binary_class_helper(POST_TRANSFORM::PROBIT);
+
+//     let (labels, mut scores) = TreeEnsembleClassifierTrait::predict(ref classifier, X);
+
+//     // ASSERT LABELS
+//     assert(*labels[0] == 1, 'labels[0]');
+//     assert(labels.len() == 1, 'len(labels)');
+
+//     // ASSERT SCORES
+//     assert(
+//         relative_eq(@scores.get(0, 0).unwrap(), @FP16x16 { mag: 0, sign: false }) == true,
+//         'score[0, 0]'
+//     );
+//     assert(
+//         relative_eq(@scores.get(0, 1).unwrap(), @FP16x16 { mag: 65536, sign: false }) == true,
+//         'score[0, 1]'
+//     );
+// }
 
 // ============ HELPER ============ //
 
@@ -369,3 +482,1959 @@ fn tree_ensemble_classifier_helper(
 
     (classifier, X)
 }
+
+// ============ BINARY CLASS HELPER ============ //
+
+fn tree_ensemble_classifier_binary_class_helper(
+    post_transform: POST_TRANSFORM
+) -> (TreeEnsembleClassifier<FP16x16>, Tensor<FP16x16>) {
+    let class_ids: Span<usize> = array![
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0
+    ]
+        .span();
+    let class_nodeids: Span<usize> = array![
+        4,
+        5,
+        7,
+        10,
+        12,
+        13,
+        15,
+        17,
+        19,
+        20,
+        24,
+        26,
+        29,
+        31,
+        32,
+        33,
+        37,
+        38,
+        39,
+        40,
+        46,
+        49,
+        50,
+        52,
+        56,
+        57,
+        58,
+        59,
+        62,
+        64,
+        66,
+        67,
+        68,
+        73,
+        74,
+        75,
+        76,
+        81,
+        82,
+        83,
+        84,
+        88,
+        89,
+        91,
+        93,
+        94,
+        95,
+        98,
+        99,
+        101,
+        104,
+        106,
+        107,
+        108,
+        112,
+        113,
+        114,
+        115,
+        119,
+        121,
+        124,
+        125,
+        127,
+        128,
+        130,
+        131,
+        138,
+        140,
+        141,
+        142,
+        143,
+        148,
+        149,
+        150,
+        151,
+        152,
+        153,
+        154
+    ]
+        .span();
+    let class_treeids: Span<usize> = array![
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0
+    ]
+        .span();
+    let class_weights: Span<FP16x16> = array![
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 43690, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 65536, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 65536, sign: false }
+    ]
+        .span();
+    let classlabels: Span<usize> = array![0, 1].span();
+    let nodes_falsenodeids: Span<usize> = array![
+        116,
+        21,
+        6,
+        5,
+        0,
+        0,
+        8,
+        0,
+        14,
+        11,
+        0,
+        13,
+        0,
+        0,
+        16,
+        0,
+        18,
+        0,
+        20,
+        0,
+        0,
+        41,
+        34,
+        25,
+        0,
+        27,
+        0,
+        33,
+        30,
+        0,
+        32,
+        0,
+        0,
+        0,
+        40,
+        39,
+        38,
+        0,
+        0,
+        0,
+        0,
+        109,
+        96,
+        69,
+        60,
+        47,
+        0,
+        51,
+        50,
+        0,
+        0,
+        53,
+        0,
+        59,
+        58,
+        57,
+        0,
+        0,
+        0,
+        0,
+        68,
+        63,
+        0,
+        65,
+        0,
+        67,
+        0,
+        0,
+        0,
+        77,
+        76,
+        75,
+        74,
+        0,
+        0,
+        0,
+        0,
+        85,
+        84,
+        83,
+        82,
+        0,
+        0,
+        0,
+        0,
+        95,
+        90,
+        89,
+        0,
+        0,
+        92,
+        0,
+        94,
+        0,
+        0,
+        0,
+        100,
+        99,
+        0,
+        0,
+        102,
+        0,
+        108,
+        105,
+        0,
+        107,
+        0,
+        0,
+        0,
+        115,
+        114,
+        113,
+        0,
+        0,
+        0,
+        0,
+        132,
+        129,
+        120,
+        0,
+        122,
+        0,
+        126,
+        125,
+        0,
+        0,
+        128,
+        0,
+        0,
+        131,
+        0,
+        0,
+        154,
+        153,
+        144,
+        143,
+        142,
+        139,
+        0,
+        141,
+        0,
+        0,
+        0,
+        0,
+        152,
+        151,
+        150,
+        149,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0
+    ]
+        .span();
+    let nodes_featureids: Span<usize> = array![
+        3,
+        2,
+        4,
+        8,
+        0,
+        0,
+        1,
+        0,
+        2,
+        7,
+        0,
+        0,
+        0,
+        0,
+        7,
+        0,
+        0,
+        0,
+        6,
+        0,
+        0,
+        8,
+        0,
+        2,
+        0,
+        7,
+        0,
+        7,
+        2,
+        0,
+        2,
+        0,
+        0,
+        0,
+        2,
+        6,
+        7,
+        0,
+        0,
+        0,
+        0,
+        7,
+        7,
+        0,
+        7,
+        1,
+        0,
+        0,
+        2,
+        0,
+        0,
+        2,
+        0,
+        2,
+        2,
+        6,
+        0,
+        0,
+        0,
+        0,
+        2,
+        0,
+        0,
+        1,
+        0,
+        6,
+        0,
+        0,
+        0,
+        0,
+        2,
+        6,
+        7,
+        0,
+        0,
+        0,
+        0,
+        6,
+        7,
+        2,
+        0,
+        0,
+        0,
+        0,
+        0,
+        2,
+        2,
+        7,
+        0,
+        0,
+        2,
+        0,
+        0,
+        0,
+        0,
+        0,
+        6,
+        1,
+        0,
+        0,
+        4,
+        0,
+        2,
+        2,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        2,
+        0,
+        0,
+        0,
+        0,
+        6,
+        0,
+        7,
+        0,
+        0,
+        0,
+        1,
+        3,
+        0,
+        0,
+        2,
+        0,
+        0,
+        8,
+        0,
+        0,
+        2,
+        2,
+        2,
+        4,
+        7,
+        3,
+        0,
+        1,
+        0,
+        0,
+        0,
+        0,
+        4,
+        3,
+        7,
+        8,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0
+    ]
+        .span();
+    let nodes_missing_value_tracks_true: Span<usize> = array![
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0
+    ]
+        .span();
+    let nodes_modes: Span<NODE_MODES> = array![
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::BRANCH_LEQ,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF,
+        NODE_MODES::LEAF
+    ]
+        .span();
+    let nodes_nodeids: Span<usize> = array![
+        0,
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7,
+        8,
+        9,
+        10,
+        11,
+        12,
+        13,
+        14,
+        15,
+        16,
+        17,
+        18,
+        19,
+        20,
+        21,
+        22,
+        23,
+        24,
+        25,
+        26,
+        27,
+        28,
+        29,
+        30,
+        31,
+        32,
+        33,
+        34,
+        35,
+        36,
+        37,
+        38,
+        39,
+        40,
+        41,
+        42,
+        43,
+        44,
+        45,
+        46,
+        47,
+        48,
+        49,
+        50,
+        51,
+        52,
+        53,
+        54,
+        55,
+        56,
+        57,
+        58,
+        59,
+        60,
+        61,
+        62,
+        63,
+        64,
+        65,
+        66,
+        67,
+        68,
+        69,
+        70,
+        71,
+        72,
+        73,
+        74,
+        75,
+        76,
+        77,
+        78,
+        79,
+        80,
+        81,
+        82,
+        83,
+        84,
+        85,
+        86,
+        87,
+        88,
+        89,
+        90,
+        91,
+        92,
+        93,
+        94,
+        95,
+        96,
+        97,
+        98,
+        99,
+        100,
+        101,
+        102,
+        103,
+        104,
+        105,
+        106,
+        107,
+        108,
+        109,
+        110,
+        111,
+        112,
+        113,
+        114,
+        115,
+        116,
+        117,
+        118,
+        119,
+        120,
+        121,
+        122,
+        123,
+        124,
+        125,
+        126,
+        127,
+        128,
+        129,
+        130,
+        131,
+        132,
+        133,
+        134,
+        135,
+        136,
+        137,
+        138,
+        139,
+        140,
+        141,
+        142,
+        143,
+        144,
+        145,
+        146,
+        147,
+        148,
+        149,
+        150,
+        151,
+        152,
+        153,
+        154
+    ]
+        .span();
+    let nodes_treeids: Span<usize> = array![
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0
+    ]
+        .span();
+    let nodes_truenodeids: Span<usize> = array![
+        1,
+        2,
+        3,
+        4,
+        0,
+        0,
+        7,
+        0,
+        9,
+        10,
+        0,
+        12,
+        0,
+        0,
+        15,
+        0,
+        17,
+        0,
+        19,
+        0,
+        0,
+        22,
+        23,
+        24,
+        0,
+        26,
+        0,
+        28,
+        29,
+        0,
+        31,
+        0,
+        0,
+        0,
+        35,
+        36,
+        37,
+        0,
+        0,
+        0,
+        0,
+        42,
+        43,
+        44,
+        45,
+        46,
+        0,
+        48,
+        49,
+        0,
+        0,
+        52,
+        0,
+        54,
+        55,
+        56,
+        0,
+        0,
+        0,
+        0,
+        61,
+        62,
+        0,
+        64,
+        0,
+        66,
+        0,
+        0,
+        0,
+        70,
+        71,
+        72,
+        73,
+        0,
+        0,
+        0,
+        0,
+        78,
+        79,
+        80,
+        81,
+        0,
+        0,
+        0,
+        0,
+        86,
+        87,
+        88,
+        0,
+        0,
+        91,
+        0,
+        93,
+        0,
+        0,
+        0,
+        97,
+        98,
+        0,
+        0,
+        101,
+        0,
+        103,
+        104,
+        0,
+        106,
+        0,
+        0,
+        0,
+        110,
+        111,
+        112,
+        0,
+        0,
+        0,
+        0,
+        117,
+        118,
+        119,
+        0,
+        121,
+        0,
+        123,
+        124,
+        0,
+        0,
+        127,
+        0,
+        0,
+        130,
+        0,
+        0,
+        133,
+        134,
+        135,
+        136,
+        137,
+        138,
+        0,
+        140,
+        0,
+        0,
+        0,
+        0,
+        145,
+        146,
+        147,
+        148,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0
+    ]
+        .span();
+    let nodes_values: Span<FP16x16> = array![
+        FP16x16 { mag: 4096, sign: false },
+        FP16x16 { mag: 22937, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 49152, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 16384, sign: false },
+        FP16x16 { mag: 57344, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 19660, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 8192, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 29491, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 8192, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 24576, sign: false },
+        FP16x16 { mag: 42598, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 62259, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 62259, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 40960, sign: false },
+        FP16x16 { mag: 24576, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 8192, sign: false },
+        FP16x16 { mag: 49152, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 19660, sign: false },
+        FP16x16 { mag: 45875, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 29491, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 49152, sign: false },
+        FP16x16 { mag: 42598, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 36044, sign: false },
+        FP16x16 { mag: 19660, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 49152, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 45875, sign: false },
+        FP16x16 { mag: 29491, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 8192, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 8192, sign: false },
+        FP16x16 { mag: 36044, sign: false },
+        FP16x16 { mag: 58982, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 58982, sign: false },
+        FP16x16 { mag: 29491, sign: false },
+        FP16x16 { mag: 8192, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 45875, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 58982, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 49152, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 42598, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 45875, sign: false },
+        FP16x16 { mag: 49152, sign: false },
+        FP16x16 { mag: 29491, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 45875, sign: false },
+        FP16x16 { mag: 8192, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 49152, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 36044, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 58982, sign: false },
+        FP16x16 { mag: 49152, sign: false },
+        FP16x16 { mag: 36044, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 16384, sign: false },
+        FP16x16 { mag: 20480, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 49152, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 8192, sign: false },
+        FP16x16 { mag: 32768, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false },
+        FP16x16 { mag: 0, sign: false }
+    ]
+        .span();
+    let base_values: Option<Span<FP16x16>> = Option::None;
+
+    let tree_ids: Span<usize> = array![0].span();
+    let mut root_index: Felt252Dict<usize> = Default::default();
+    root_index.insert(0, 0);
+    let mut node_index: Felt252Dict<usize> = Default::default();
+    node_index
+        .insert(2089986280348253421170679821480865132823066470938446095505822317253594081284, 0);
+    node_index
+        .insert(2001140082530619239661729809084578298299223810202097622761632384561112390979, 1);
+    node_index
+        .insert(2592670241084192212354027440049085852792506518781954896144296316131790403900, 2);
+    node_index
+        .insert(2960591271376829378356567803618548672034867345123727178628869426548453833420, 3);
+    node_index
+        .insert(458933264452572171106695256465341160654132084710250671055261382009315664425, 4);
+    node_index
+        .insert(3344223123784052057366048933846905716067140384361791026153972616805110454637, 5);
+    node_index
+        .insert(658476905110174425295568215706634733332002869979287079110965040248935650599, 6);
+    node_index
+        .insert(2836212335642438363012490794290757623813171043187182819737087983331902926990, 7);
+    node_index
+        .insert(3496601277869056110810900082189273917786762659443522403285387602989271154262, 8);
+    node_index
+        .insert(1249294489531540970169611621067106471309281870082955806338234725206665112557, 9);
+    node_index
+        .insert(2161697998033672097816961828039488190903838124365465380011173778905747857792, 10);
+    node_index
+        .insert(1129815197211541481934112806673325772687763881719835256646064516195041515616, 11);
+    node_index
+        .insert(2592593088135949192377729543480191336537305484235681164569491942155715064163, 12);
+    node_index
+        .insert(578223957014284909949571568465953382377214912750427143720957054706073492593, 13);
+    node_index
+        .insert(1645617302026197421098102802983206579163506957138012501615708926120228167528, 14);
+    node_index
+        .insert(2809438816810155970395166036110536928593305127049404137239671320081144123490, 15);
+    node_index
+        .insert(2496308528011391755709310159103918074725328650411689040761791240500618770096, 16);
+    node_index
+        .insert(2003594778587446957576114348312422277631766150749194167061999666337236425714, 17);
+    node_index
+        .insert(2215681478480673835576618830034726157921200517935329010004363713426342305479, 18);
+    node_index
+        .insert(3185925835074464079989752015681272863271067691852543168049845807561733691707, 19);
+    node_index
+        .insert(1207265836470221457484062512091666004839070622130697586496866096347024057755, 20);
+    node_index
+        .insert(1870230949202979679764944800468118671928852128047695497376875566624821494262, 21);
+    node_index
+        .insert(618060852536781954395603948693216564334274573299243914053414488061601327758, 22);
+    node_index
+        .insert(232760707548494477255512699093366059519467428168757247456690480397246371463, 23);
+    node_index
+        .insert(1617386247965480308136742715422077429967341022950306068917456849194882895900, 24);
+    node_index
+        .insert(654822874782506608656472905579051041410086644071534146326024101025575400153, 25);
+    node_index
+        .insert(525638101901638132526332140778087078272370083489998903571807698910013602668, 26);
+    node_index
+        .insert(3091640181556387972179279087539287892670640556085669903494551919685982442095, 27);
+    node_index
+        .insert(1425411460578159050163131982087304445715005458700346341117759372943452688022, 28);
+    node_index
+        .insert(1722933265299553894839124723076027659619615015638971980461286818493531809034, 29);
+    node_index
+        .insert(3325117385742592388671007840076299062858228097051060057749225651290693960897, 30);
+    node_index
+        .insert(1869273998012404873272699831805499731567895666937555882116307079956228100456, 31);
+    node_index
+        .insert(257262395234910825879033951801423835835630270967846664413154594520703929530, 32);
+    node_index
+        .insert(2891500475385583315757684141371327604925143655360011721762142660942782195029, 33);
+    node_index
+        .insert(1257459981124043271342269816753070228024611695909553991758648317372015085782, 34);
+    node_index
+        .insert(3573101724490615587655146760489247477770015274618159524231872921394794809579, 35);
+    node_index
+        .insert(2951401777594449283985541406642940553317465718696638438535370997641527993378, 36);
+    node_index
+        .insert(2436860863451320452900512817385686838091627966322316039332239784330434600829, 37);
+    node_index
+        .insert(3257977356974702770994741663931928753019715185508521958836925918758890988390, 38);
+    node_index
+        .insert(2741853283805093821434776875305720302351684616683152528499335618682018880592, 39);
+    node_index
+        .insert(514567459251558911686762246500770717674979116530125263461114578537254680672, 40);
+    node_index
+        .insert(2119374930171040799805795099091470687208894498354655018353474015395489390434, 41);
+    node_index
+        .insert(3338470191188327918255138125570464269857839379813971679216902484398948556964, 42);
+    node_index
+        .insert(2892272281879752543368066497063301979597320550780387266511926397533716561161, 43);
+    node_index
+        .insert(2855312300216814846973137837923466865382642814675378398541743368270404441020, 44);
+    node_index
+        .insert(3483159989811162048659069774034779954374540681397531094699912464364012442948, 45);
+    node_index
+        .insert(2987290998320166766043911843685118029159841654368226419198314196237253901671, 46);
+    node_index
+        .insert(2925128850088180758852255336587985612621894021863350117875677692518888637440, 47);
+    node_index
+        .insert(2816470536741550741568042622139415760794090671576940833850781679568928363263, 48);
+    node_index
+        .insert(117504025904364990582663097556885493352655695615775952177872159762046032741, 49);
+    node_index
+        .insert(2143228410294149239354901612797540167003066966910132278060626241695943498248, 50);
+    node_index
+        .insert(419311759585766455354017006957403420381614228026953716552023555428752798694, 51);
+    node_index
+        .insert(3050064038480880151202753004776919876287903442365303272956696507808448797287, 52);
+    node_index
+        .insert(1385347512411195789080079656286641766866442255046855963092069449745407366357, 53);
+    node_index
+        .insert(3070310993421490198115289431281422702215620142859327949152517372324361472619, 54);
+    node_index
+        .insert(2913742884576958969164113782587195202828846527657900496424141449477472273564, 55);
+    node_index
+        .insert(2093568472535973986606438755824580633177115509557931302974988564932601955239, 56);
+    node_index
+        .insert(3560543329106347446823281318204312198881533222464682017397248462954529220234, 57);
+    node_index
+        .insert(2258329791422139736262782239641765930569031761627249090322755566443202104242, 58);
+    node_index
+        .insert(780147230530856456622774510057100334628735431063744145772648079601317149643, 59);
+    node_index
+        .insert(2316329094783634722527635915976455864728431870713378530935487247638854220445, 60);
+    node_index
+        .insert(595942459003356191117553450912822964169058193996898486073017533717706655996, 61);
+    node_index
+        .insert(468061318535033931711585815055033307297228787991312757359512916260570188285, 62);
+    node_index
+        .insert(2052204235688624923559873131063770183910134013049526186717275231865702195614, 63);
+    node_index
+        .insert(1699955311620840869165542755053722387608345658646185648087789689690825797785, 64);
+    node_index
+        .insert(3374282522812564185678772854203408947562394461702303390331208821006329361123, 65);
+    node_index
+        .insert(2973169188135795465401576355486514117723575153845438471619715618155257254587, 66);
+    node_index
+        .insert(1933845760462748501896196912926633344425020928596291295340561855718789280752, 67);
+    node_index
+        .insert(1400206374308839959676708676217334569580738052049798766556848516900888958934, 68);
+    node_index
+        .insert(1440488595273849761788031183901254714714513692476890759699232177835922420051, 69);
+    node_index
+        .insert(1765607197782429306903827944694032984087223086461400721152786273443512274576, 70);
+    node_index
+        .insert(1081728107764482028110815183657783965582618309560569428049406599883158895762, 71);
+    node_index
+        .insert(2062101824085365476835789898002802715794623271831111740147610520210138854237, 72);
+    node_index
+        .insert(2074740322618091900768870458741540994849904300182495465356314088191301853065, 73);
+    node_index
+        .insert(3258451235037745323160669027918885172565773098482160366154412360890640013860, 74);
+    node_index
+        .insert(525053653813541387331907730505904505067816165493211829943994988775279102044, 75);
+    node_index
+        .insert(1899573658331441767985549642643113663505618738939032010935036740376062596854, 76);
+    node_index
+        .insert(350484224543766923071449868701665032398970313961410080649918872017849315812, 77);
+    node_index
+        .insert(1950842492180490337143378914485176805944281696420768035114335939818602766139, 78);
+    node_index
+        .insert(1404824782481446239312837894341789608778585592445990662138109764117920511709, 79);
+    node_index
+        .insert(362836422984951199752185473435750713386745407518736982952373985921347236081, 80);
+    node_index
+        .insert(946623025367211063265176586824604502073515634531788667777364911179858705558, 81);
+    node_index
+        .insert(2633163324000277496191816132521100721217797223993064604664039067710591734562, 82);
+    node_index
+        .insert(1801986104078933931671502775029170829560335045042499367678597186639133610708, 83);
+    node_index
+        .insert(1420697278439090953165809531316265389371075037014378922361911811337560296928, 84);
+    node_index
+        .insert(2818913779862691152404893285048164649343019708946413114150419613972391643833, 85);
+    node_index
+        .insert(2117995436013652728497840885480545729833030913486848118093758726746902541269, 86);
+    node_index
+        .insert(127751852951361188238686395231851222850913859197429858579312845246901369178, 87);
+    node_index
+        .insert(2698811633001158191033663638617437313508153976714307643233173949778419312517, 88);
+    node_index
+        .insert(658388282521842455588914251287531837029259203197178137902217792556456503561, 89);
+    node_index
+        .insert(1181527093320872098458354979612125149419384756607076935731557552577945926179, 90);
+    node_index
+        .insert(749436134732178646256740138670151907037714564259781780243747781475007506978, 91);
+    node_index
+        .insert(139527053159256821789882596124320673637475746672994443968014105962305658551, 92);
+    node_index
+        .insert(2256264752321707533173578319742847366660740117899562657584919346001438808295, 93);
+    node_index
+        .insert(1471349294215639651865069312281269029496180149092207674923855978537861742949, 94);
+    node_index
+        .insert(1599527610774916650758786135513735847459194869088601099692148267264507139422, 95);
+    node_index
+        .insert(1348925567371118538973078195838174941892601233016661969987842843098656775084, 96);
+    node_index
+        .insert(3255130909854220350850821724488067913492420563978595271106701962634473840914, 97);
+    node_index
+        .insert(1098499015810170842401428216621470177488952811780672364884710297364076372943, 98);
+    node_index
+        .insert(2666902303639302012507119689908308317608522901613536135678723310999647515155, 99);
+    node_index
+        .insert(907997515879651052705985194221621380802961721264372722705825219340461809200, 100);
+    node_index
+        .insert(2124360554325144308113106422635485756539471211141315552843423768396084888273, 101);
+    node_index
+        .insert(3598736440043009208771817410113758019876931018927260161846683440123219507147, 102);
+    node_index
+        .insert(1237113034722832488580561245188430373504295256910735188987019984096012001931, 103);
+    node_index
+        .insert(884558344049768836371555446021588200903052780339208951904957349404044037185, 104);
+    node_index
+        .insert(784280321344489256066716285882203121428790637989919760379274813665427427262, 105);
+    node_index
+        .insert(3472551952588748711709398308465335743810517871695257916614928877311914574241, 106);
+    node_index
+        .insert(1579363348100943961344032004617708767155021524242506190674861550786419896732, 107);
+    node_index
+        .insert(653576968777651719072715499492112313607520878545254037043893560183879857489, 108);
+    node_index
+        .insert(2633327961579170199842757290989312779085828750765842327985383652720803061926, 109);
+    node_index
+        .insert(3101204920253220343970782457572784926765600523633379722044614528209389590915, 110);
+    node_index
+        .insert(2537565394330405662800880050062241097694806466900452037378113841155978555645, 111);
+    node_index
+        .insert(306955559655552244989220345789093187601563118591829582730637833945761653350, 112);
+    node_index
+        .insert(1144065212212058748489308207801098564095305699242880891977316839573431241916, 113);
+    node_index
+        .insert(3478181491851418723342103101321490659650934149094649769124337426850038155270, 114);
+    node_index
+        .insert(3419621624676637660673415219086314486713019053519954317586073983685881930356, 115);
+    node_index
+        .insert(2426908011370291613447136873176769136554489197972200481728552402228021778402, 116);
+    node_index
+        .insert(1916122042123370178944690083048900704842269230325086549679099089416174875473, 117);
+    node_index
+        .insert(2057207652658215393591191155928140567561900227203223756539551876829334137660, 118);
+    node_index
+        .insert(2722034389703601317070746005702467061064354401688341549606678773616189196490, 119);
+    node_index
+        .insert(1171026027377763359814377926117880688616494219551682642535759838199732407496, 120);
+    node_index
+        .insert(3507234282031533800397666430789917374211847440333243952151005899337152633413, 121);
+    node_index
+        .insert(591003147462937848375161803108517142253138969543815135207326321181858185919, 122);
+    node_index
+        .insert(182069734527202013451813026473135702900640769187641767871411473365447302169, 123);
+    node_index
+        .insert(1195243682249232878341146428166676460720423167409013083888435705219134747702, 124);
+    node_index
+        .insert(1793425644853312386902998134061844248823841892125424765064687913085130719534, 125);
+    node_index
+        .insert(1983622665815164792580256365519803214027269990384198703315493315153573288434, 126);
+    node_index
+        .insert(3615973154491344159350153395208055142342062736505558158666764642048838175685, 127);
+    node_index
+        .insert(2751715913626909804252433699602081411293721754810298670422380863932998088133, 128);
+    node_index
+        .insert(186918881712189523740089713555196200069231794627360499557319265374750577226, 129);
+    node_index
+        .insert(696585542544434929491503209053317581175146475161262066468664234437983008675, 130);
+    node_index
+        .insert(4359830495913805154545225899592517767672472055784183911796827820518038513, 131);
+    node_index
+        .insert(2954335207058000607751727656601539819316106074875304820535376873121805433820, 132);
+    node_index
+        .insert(2510390039949230255082316953804013731253145558531652907601250263563528226672, 133);
+    node_index
+        .insert(3226995230854300551967642178527450300960499043510855212238369890580256668532, 134);
+    node_index
+        .insert(1620924075233065517364532267959798304439946408626316544761884056227131075831, 135);
+    node_index
+        .insert(1610900122192929153657761847202689179268074338802437933866337242354758101660, 136);
+    node_index
+        .insert(2565949095169598991903537465065584077778440646580025930326495506484329892725, 137);
+    node_index
+        .insert(1012362975819634411571869839734809106575285344002573666983595104659295812607, 138);
+    node_index
+        .insert(242312010918799555845832460483650516749990744287009628468613253461264531026, 139);
+    node_index
+        .insert(1104776796569046483584574115975216172161469015460244982207905888870418040487, 140);
+    node_index
+        .insert(3289555912992777681578950209252840071327866822704829766247386311885634446673, 141);
+    node_index
+        .insert(3133389957643610781371406448279843175887428913359743769920083259111437722268, 142);
+    node_index
+        .insert(1169918710119352022244140656086831769713178729571654411898266328562003734517, 143);
+    node_index
+        .insert(3592039235252149652556167686570045881877115549259769455422056097903987237819, 144);
+    node_index
+        .insert(2048175709145840597887667330964815895803568760936075562647625937161113445908, 145);
+    node_index
+        .insert(602222645962845554276438041138511866776339653340605661136009451417275008940, 146);
+    node_index
+        .insert(3318742320906017551291978242369663702298606650330380959683585594592748661010, 147);
+    node_index
+        .insert(564160996724923690963741657975239836484028160385417016805513722318839327322, 148);
+    node_index
+        .insert(656294390376267384135628810815504467149264887388377312825033341338166573620, 149);
+    node_index
+        .insert(1201592236750942207412694706123654466634588634474700675083122904145559965915, 150);
+    node_index
+        .insert(2141408926815137181004274624388915700231991905288681935478972043994347966006, 151);
+    node_index
+        .insert(1440847977042239464860406726605567303568767649154338464116083965986084755262, 152);
+    node_index
+        .insert(950585553138591375958592507876257987416844837045084288783892644487908218679, 153);
+    node_index
+        .insert(257643451533833048856069434258149588745628261389615631070776723485957908127, 154);
+
+    let atts = TreeEnsembleAttributes {
+        nodes_falsenodeids,
+        nodes_featureids,
+        nodes_missing_value_tracks_true,
+        nodes_modes,
+        nodes_nodeids,
+        nodes_treeids,
+        nodes_truenodeids,
+        nodes_values
+    };
+
+    let mut ensemble: TreeEnsemble<FP16x16> = TreeEnsemble {
+        atts, tree_ids, root_index, node_index
+    };
+
+    let mut classifier: TreeEnsembleClassifier<FP16x16> = TreeEnsembleClassifier {
+        ensemble,
+        class_ids,
+        class_nodeids,
+        class_treeids,
+        class_weights,
+        classlabels,
+        base_values,
+        post_transform
+    };
+
+    let mut X = TensorTrait::new(
+        array![1, 9].span(),
+        array![
+            FP16x16 { mag: 39321, sign: false },
+            FP16x16 { mag: 32768, sign: false },
+            FP16x16 { mag: 52428, sign: false },
+            FP16x16 { mag: 16384, sign: false },
+            FP16x16 { mag: 0, sign: false },
+            FP16x16 { mag: 65536, sign: false },
+            FP16x16 { mag: 0, sign: false },
+            FP16x16 { mag: 16384, sign: false },
+            FP16x16 { mag: 0, sign: false },
+        ]
+            .span()
+    );
+
+    (classifier, X)
+}
diff --git a/tests/ml/tree_ensemble_regressor.cairo b/tests/ml/tree_ensemble_regressor.cairo
index 23c9274a7..2ee505774 100644
--- a/tests/ml/tree_ensemble_regressor.cairo
+++ b/tests/ml/tree_ensemble_regressor.cairo
@@ -38,7 +38,6 @@ fn test_tree_ensemble_regressor_AVERAGE() {
     let mut res = TreeEnsembleRegressorTrait::predict(ref regressor, X);
 
     let check = @res.get(1, 0).unwrap().mag;
-    (*check).print();
 
     // ASSERT RES
     assert(
@@ -63,7 +62,6 @@ fn test_tree_ensemble_regressor_MIN() {
     let mut res = TreeEnsembleRegressorTrait::predict(ref regressor, X);
 
     let check = @res.get(1, 0).unwrap().mag;
-    (*check).print();
 
     // ASSERT RES
     assert(
@@ -88,7 +86,6 @@ fn test_tree_ensemble_regressor_MAX() {
     let mut res = TreeEnsembleRegressorTrait::predict(ref regressor, X);
 
     let check = @res.get(1, 0).unwrap().mag;
-    (*check).print();
 
     // ASSERT RES
     assert(
diff --git a/tests/nodes.cairo b/tests/nodes.cairo
index c7155e942..e06f9efde 100644
--- a/tests/nodes.cairo
+++ b/tests/nodes.cairo
@@ -850,3 +850,89 @@ mod gather_nd_i8_3d_batch_dims1;
 mod gather_nd_u32_default;
 mod gather_nd_u32_batch_dims1;
 mod gather_nd_u32_batch_dims2;
+mod resize_upsample_scales_nearest;
+mod resize_downsample_scales_cubic;
+mod resize_downsample_scales_cubic_A_n0p5_exclude_outside;
+mod resize_downsample_scales_cubic_align_corners;
+mod resize_upsample_scales_linear;
+mod resize_downsample_scales_linear_align_corners;
+mod resize_downsample_scales_nearest;
+mod resize_upsample_scales_cubic;
+mod resize_upsample_scales_cubic_A_n0p5_exclude_outside;
+mod resize_upsample_scales_cubic_align_corners;
+mod resize_upsample_scales_cubic_asymmetric;
+mod resize_upsample_scales_linear_align_corners;
+mod resize_upsample_sizes_nearest;
+mod resize_upsample_sizes_cubic;
+mod resize_downsample_sizes_cubic;
+mod resize_downsample_sizes_nearest;
+mod resize_upsample_scales_linear_half_pixel_symmetric;
+mod resize_downsample_scales_cubic_antialias;
+mod resize_downsample_scales_linear_antialias;
+mod resize_downsample_sizes_cubic_antialias;
+mod resize_downsample_sizes_linear_pytorch_half_pixel;
+mod resize_tf_crop_and_resize;
+mod resize_tf_crop_and_resize_extrapolation_value;
+mod resize_upsample_scales_nearest_axes_2_3;
+mod resize_upsample_scales_nearest_axes_3_2;
+mod resize_upsample_sizes_nearest_axes_2_3;
+mod resize_upsample_sizes_nearest_ceil_half_pixel;
+mod resize_upsample_sizes_nearest_floor_align_corners;
+mod resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric;
+mod resize_downsample_scales_linear_half_pixel_symmetric;
+mod resize_downsample_sizes_nearest_not_larger;
+mod resize_downsample_sizes_nearest_not_smaller;
+mod resize_tf_crop_and_resize_axes_2_3;
+mod resize_tf_crop_and_resize_axes_3_2;
+mod resize_upsample_sizes_nearest_axes_3_2;
+mod resize_upsample_sizes_nearest_not_larger;
+mod resize_upsample_sizes_nearest_not_smaller;
+mod compress_fp16x16_3d_default;
+mod compress_fp16x16_3d_axis1;
+mod compress_fp16x16_3d_axis2;
+mod compress_fp16x16_3d_axis3;
+mod compress_fp16x16_3d_noaxis;
+mod compress_fp8x23_3d_default;
+mod compress_fp8x23_3d_axis1;
+mod compress_fp8x23_3d_axis2;
+mod compress_i32_3d_default;
+mod compress_i32_3d_axis1;
+mod compress_i32_3d_axis2;
+mod compress_i8_3d_default;
+mod compress_i8_3d_axis1;
+mod compress_i8_3d_axis2;
+mod compress_u32_3d_default;
+mod compress_u32_3d_axis1;
+mod compress_u32_3d_axis2;
+mod compress_u32_3d_axis2_2;
+mod compress_u32_3d_axis3;
+mod layer_normalization_default_axis;
+mod layer_normalization_4d_axis0;
+mod layer_normalization_4d_axis1;
+mod layer_normalization_4d_axis2;
+mod layer_normalization_4d_axis3;
+mod layer_normalization_3d_axis0_epsilon;
+mod layer_normalization_3d_axis1_epsilon;
+mod layer_normalization_3d_axis2_epsilon;
+mod layer_normalization_4d_axis_negative_4;
+mod layer_normalization_4d_axis_negative_3;
+mod layer_normalization_4d_axis_negative_2;
+mod layer_normalization_4d_axis_negative_1;
+mod layer_normalization_3d_axis_negative_3_epsilon;
+mod layer_normalization_3d_axis_negative_2_epsilon;
+mod layer_normalization_3d_axis_negative_1_epsilon;
+mod layer_normalization_test;
+mod split_u32_1d_equal_parts;
+mod split_u32_2d_equal_parts;
+mod split_u32_zero_size;
+mod split_u32_1d_variable_parts;
+mod split_u32_2d_variable_parts;
+mod split_u32_1d_uneven;
+mod split_u32_2d_uneven;
+mod split_fp16x16_1d_equal_parts;
+mod split_fp16x16_1d_variable_parts;
+mod split_fp16x16_2d_equal_parts;
+mod split_fp16x16_2d_variable_parts;
+mod split_fp16x16_zero_size;
+mod split_fp16x16_1d_uneven;
+mod split_fp16x16_2d_uneven;
diff --git a/tests/nodes/compress_fp16x16_3d_axis1.cairo b/tests/nodes/compress_fp16x16_3d_axis1.cairo
new file mode 100644
index 000000000..f110fd66d
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_axis1.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_fp16x16_3d_axis1() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(1));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_fp16x16_3d_axis1/input_0.cairo b/tests/nodes/compress_fp16x16_3d_axis1/input_0.cairo
new file mode 100644
index 000000000..46162489f
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_axis1/input_0.cairo
@@ -0,0 +1,195 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 0, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1114112, sign: false });
+    data.append(FP16x16 { mag: 1179648, sign: false });
+    data.append(FP16x16 { mag: 1245184, sign: false });
+    data.append(FP16x16 { mag: 1310720, sign: false });
+    data.append(FP16x16 { mag: 1376256, sign: false });
+    data.append(FP16x16 { mag: 1441792, sign: false });
+    data.append(FP16x16 { mag: 1507328, sign: false });
+    data.append(FP16x16 { mag: 1572864, sign: false });
+    data.append(FP16x16 { mag: 1638400, sign: false });
+    data.append(FP16x16 { mag: 1703936, sign: false });
+    data.append(FP16x16 { mag: 1769472, sign: false });
+    data.append(FP16x16 { mag: 1835008, sign: false });
+    data.append(FP16x16 { mag: 1900544, sign: false });
+    data.append(FP16x16 { mag: 1966080, sign: false });
+    data.append(FP16x16 { mag: 2031616, sign: false });
+    data.append(FP16x16 { mag: 2097152, sign: false });
+    data.append(FP16x16 { mag: 2162688, sign: false });
+    data.append(FP16x16 { mag: 2228224, sign: false });
+    data.append(FP16x16 { mag: 2293760, sign: false });
+    data.append(FP16x16 { mag: 2359296, sign: false });
+    data.append(FP16x16 { mag: 2424832, sign: false });
+    data.append(FP16x16 { mag: 2490368, sign: false });
+    data.append(FP16x16 { mag: 2555904, sign: false });
+    data.append(FP16x16 { mag: 2621440, sign: false });
+    data.append(FP16x16 { mag: 2686976, sign: false });
+    data.append(FP16x16 { mag: 2752512, sign: false });
+    data.append(FP16x16 { mag: 2818048, sign: false });
+    data.append(FP16x16 { mag: 2883584, sign: false });
+    data.append(FP16x16 { mag: 2949120, sign: false });
+    data.append(FP16x16 { mag: 3014656, sign: false });
+    data.append(FP16x16 { mag: 3080192, sign: false });
+    data.append(FP16x16 { mag: 3145728, sign: false });
+    data.append(FP16x16 { mag: 3211264, sign: false });
+    data.append(FP16x16 { mag: 3276800, sign: false });
+    data.append(FP16x16 { mag: 3342336, sign: false });
+    data.append(FP16x16 { mag: 3407872, sign: false });
+    data.append(FP16x16 { mag: 3473408, sign: false });
+    data.append(FP16x16 { mag: 3538944, sign: false });
+    data.append(FP16x16 { mag: 3604480, sign: false });
+    data.append(FP16x16 { mag: 3670016, sign: false });
+    data.append(FP16x16 { mag: 3735552, sign: false });
+    data.append(FP16x16 { mag: 3801088, sign: false });
+    data.append(FP16x16 { mag: 3866624, sign: false });
+    data.append(FP16x16 { mag: 3932160, sign: false });
+    data.append(FP16x16 { mag: 3997696, sign: false });
+    data.append(FP16x16 { mag: 4063232, sign: false });
+    data.append(FP16x16 { mag: 4128768, sign: false });
+    data.append(FP16x16 { mag: 4194304, sign: false });
+    data.append(FP16x16 { mag: 4259840, sign: false });
+    data.append(FP16x16 { mag: 4325376, sign: false });
+    data.append(FP16x16 { mag: 4390912, sign: false });
+    data.append(FP16x16 { mag: 4456448, sign: false });
+    data.append(FP16x16 { mag: 4521984, sign: false });
+    data.append(FP16x16 { mag: 4587520, sign: false });
+    data.append(FP16x16 { mag: 4653056, sign: false });
+    data.append(FP16x16 { mag: 4718592, sign: false });
+    data.append(FP16x16 { mag: 4784128, sign: false });
+    data.append(FP16x16 { mag: 4849664, sign: false });
+    data.append(FP16x16 { mag: 4915200, sign: false });
+    data.append(FP16x16 { mag: 4980736, sign: false });
+    data.append(FP16x16 { mag: 5046272, sign: false });
+    data.append(FP16x16 { mag: 5111808, sign: false });
+    data.append(FP16x16 { mag: 5177344, sign: false });
+    data.append(FP16x16 { mag: 5242880, sign: false });
+    data.append(FP16x16 { mag: 5308416, sign: false });
+    data.append(FP16x16 { mag: 5373952, sign: false });
+    data.append(FP16x16 { mag: 5439488, sign: false });
+    data.append(FP16x16 { mag: 5505024, sign: false });
+    data.append(FP16x16 { mag: 5570560, sign: false });
+    data.append(FP16x16 { mag: 5636096, sign: false });
+    data.append(FP16x16 { mag: 5701632, sign: false });
+    data.append(FP16x16 { mag: 5767168, sign: false });
+    data.append(FP16x16 { mag: 5832704, sign: false });
+    data.append(FP16x16 { mag: 5898240, sign: false });
+    data.append(FP16x16 { mag: 5963776, sign: false });
+    data.append(FP16x16 { mag: 6029312, sign: false });
+    data.append(FP16x16 { mag: 6094848, sign: false });
+    data.append(FP16x16 { mag: 6160384, sign: false });
+    data.append(FP16x16 { mag: 6225920, sign: false });
+    data.append(FP16x16 { mag: 6291456, sign: false });
+    data.append(FP16x16 { mag: 6356992, sign: false });
+    data.append(FP16x16 { mag: 6422528, sign: false });
+    data.append(FP16x16 { mag: 6488064, sign: false });
+    data.append(FP16x16 { mag: 6553600, sign: false });
+    data.append(FP16x16 { mag: 6619136, sign: false });
+    data.append(FP16x16 { mag: 6684672, sign: false });
+    data.append(FP16x16 { mag: 6750208, sign: false });
+    data.append(FP16x16 { mag: 6815744, sign: false });
+    data.append(FP16x16 { mag: 6881280, sign: false });
+    data.append(FP16x16 { mag: 6946816, sign: false });
+    data.append(FP16x16 { mag: 7012352, sign: false });
+    data.append(FP16x16 { mag: 7077888, sign: false });
+    data.append(FP16x16 { mag: 7143424, sign: false });
+    data.append(FP16x16 { mag: 7208960, sign: false });
+    data.append(FP16x16 { mag: 7274496, sign: false });
+    data.append(FP16x16 { mag: 7340032, sign: false });
+    data.append(FP16x16 { mag: 7405568, sign: false });
+    data.append(FP16x16 { mag: 7471104, sign: false });
+    data.append(FP16x16 { mag: 7536640, sign: false });
+    data.append(FP16x16 { mag: 7602176, sign: false });
+    data.append(FP16x16 { mag: 7667712, sign: false });
+    data.append(FP16x16 { mag: 7733248, sign: false });
+    data.append(FP16x16 { mag: 7798784, sign: false });
+    data.append(FP16x16 { mag: 7864320, sign: false });
+    data.append(FP16x16 { mag: 7929856, sign: false });
+    data.append(FP16x16 { mag: 7995392, sign: false });
+    data.append(FP16x16 { mag: 8060928, sign: false });
+    data.append(FP16x16 { mag: 8126464, sign: false });
+    data.append(FP16x16 { mag: 8192000, sign: false });
+    data.append(FP16x16 { mag: 8257536, sign: false });
+    data.append(FP16x16 { mag: 8323072, sign: false });
+    data.append(FP16x16 { mag: 8388608, sign: false });
+    data.append(FP16x16 { mag: 8454144, sign: false });
+    data.append(FP16x16 { mag: 8519680, sign: false });
+    data.append(FP16x16 { mag: 8585216, sign: false });
+    data.append(FP16x16 { mag: 8650752, sign: false });
+    data.append(FP16x16 { mag: 8716288, sign: false });
+    data.append(FP16x16 { mag: 8781824, sign: false });
+    data.append(FP16x16 { mag: 8847360, sign: false });
+    data.append(FP16x16 { mag: 8912896, sign: false });
+    data.append(FP16x16 { mag: 8978432, sign: false });
+    data.append(FP16x16 { mag: 9043968, sign: false });
+    data.append(FP16x16 { mag: 9109504, sign: false });
+    data.append(FP16x16 { mag: 9175040, sign: false });
+    data.append(FP16x16 { mag: 9240576, sign: false });
+    data.append(FP16x16 { mag: 9306112, sign: false });
+    data.append(FP16x16 { mag: 9371648, sign: false });
+    data.append(FP16x16 { mag: 9437184, sign: false });
+    data.append(FP16x16 { mag: 9502720, sign: false });
+    data.append(FP16x16 { mag: 9568256, sign: false });
+    data.append(FP16x16 { mag: 9633792, sign: false });
+    data.append(FP16x16 { mag: 9699328, sign: false });
+    data.append(FP16x16 { mag: 9764864, sign: false });
+    data.append(FP16x16 { mag: 9830400, sign: false });
+    data.append(FP16x16 { mag: 9895936, sign: false });
+    data.append(FP16x16 { mag: 9961472, sign: false });
+    data.append(FP16x16 { mag: 10027008, sign: false });
+    data.append(FP16x16 { mag: 10092544, sign: false });
+    data.append(FP16x16 { mag: 10158080, sign: false });
+    data.append(FP16x16 { mag: 10223616, sign: false });
+    data.append(FP16x16 { mag: 10289152, sign: false });
+    data.append(FP16x16 { mag: 10354688, sign: false });
+    data.append(FP16x16 { mag: 10420224, sign: false });
+    data.append(FP16x16 { mag: 10485760, sign: false });
+    data.append(FP16x16 { mag: 10551296, sign: false });
+    data.append(FP16x16 { mag: 10616832, sign: false });
+    data.append(FP16x16 { mag: 10682368, sign: false });
+    data.append(FP16x16 { mag: 10747904, sign: false });
+    data.append(FP16x16 { mag: 10813440, sign: false });
+    data.append(FP16x16 { mag: 10878976, sign: false });
+    data.append(FP16x16 { mag: 10944512, sign: false });
+    data.append(FP16x16 { mag: 11010048, sign: false });
+    data.append(FP16x16 { mag: 11075584, sign: false });
+    data.append(FP16x16 { mag: 11141120, sign: false });
+    data.append(FP16x16 { mag: 11206656, sign: false });
+    data.append(FP16x16 { mag: 11272192, sign: false });
+    data.append(FP16x16 { mag: 11337728, sign: false });
+    data.append(FP16x16 { mag: 11403264, sign: false });
+    data.append(FP16x16 { mag: 11468800, sign: false });
+    data.append(FP16x16 { mag: 11534336, sign: false });
+    data.append(FP16x16 { mag: 11599872, sign: false });
+    data.append(FP16x16 { mag: 11665408, sign: false });
+    data.append(FP16x16 { mag: 11730944, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp16x16_3d_axis1/input_1.cairo b/tests/nodes/compress_fp16x16_3d_axis1/input_1.cairo
new file mode 100644
index 000000000..b97e1003b
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_axis1/input_1.cairo
@@ -0,0 +1,15 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
+    data.append(1);
+    data.append(0);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp16x16_3d_axis1/output_0.cairo b/tests/nodes/compress_fp16x16_3d_axis1/output_0.cairo
new file mode 100644
index 000000000..1ff0323a5
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_axis1/output_0.cairo
@@ -0,0 +1,150 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 0, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1114112, sign: false });
+    data.append(FP16x16 { mag: 1179648, sign: false });
+    data.append(FP16x16 { mag: 1245184, sign: false });
+    data.append(FP16x16 { mag: 1310720, sign: false });
+    data.append(FP16x16 { mag: 1376256, sign: false });
+    data.append(FP16x16 { mag: 1441792, sign: false });
+    data.append(FP16x16 { mag: 1507328, sign: false });
+    data.append(FP16x16 { mag: 1572864, sign: false });
+    data.append(FP16x16 { mag: 1638400, sign: false });
+    data.append(FP16x16 { mag: 1703936, sign: false });
+    data.append(FP16x16 { mag: 1769472, sign: false });
+    data.append(FP16x16 { mag: 1835008, sign: false });
+    data.append(FP16x16 { mag: 1900544, sign: false });
+    data.append(FP16x16 { mag: 1966080, sign: false });
+    data.append(FP16x16 { mag: 2031616, sign: false });
+    data.append(FP16x16 { mag: 2097152, sign: false });
+    data.append(FP16x16 { mag: 2162688, sign: false });
+    data.append(FP16x16 { mag: 2228224, sign: false });
+    data.append(FP16x16 { mag: 2293760, sign: false });
+    data.append(FP16x16 { mag: 2359296, sign: false });
+    data.append(FP16x16 { mag: 2424832, sign: false });
+    data.append(FP16x16 { mag: 2490368, sign: false });
+    data.append(FP16x16 { mag: 2555904, sign: false });
+    data.append(FP16x16 { mag: 2621440, sign: false });
+    data.append(FP16x16 { mag: 2686976, sign: false });
+    data.append(FP16x16 { mag: 2752512, sign: false });
+    data.append(FP16x16 { mag: 2818048, sign: false });
+    data.append(FP16x16 { mag: 2883584, sign: false });
+    data.append(FP16x16 { mag: 3932160, sign: false });
+    data.append(FP16x16 { mag: 3997696, sign: false });
+    data.append(FP16x16 { mag: 4063232, sign: false });
+    data.append(FP16x16 { mag: 4128768, sign: false });
+    data.append(FP16x16 { mag: 4194304, sign: false });
+    data.append(FP16x16 { mag: 4259840, sign: false });
+    data.append(FP16x16 { mag: 4325376, sign: false });
+    data.append(FP16x16 { mag: 4390912, sign: false });
+    data.append(FP16x16 { mag: 4456448, sign: false });
+    data.append(FP16x16 { mag: 4521984, sign: false });
+    data.append(FP16x16 { mag: 4587520, sign: false });
+    data.append(FP16x16 { mag: 4653056, sign: false });
+    data.append(FP16x16 { mag: 4718592, sign: false });
+    data.append(FP16x16 { mag: 4784128, sign: false });
+    data.append(FP16x16 { mag: 4849664, sign: false });
+    data.append(FP16x16 { mag: 4915200, sign: false });
+    data.append(FP16x16 { mag: 4980736, sign: false });
+    data.append(FP16x16 { mag: 5046272, sign: false });
+    data.append(FP16x16 { mag: 5111808, sign: false });
+    data.append(FP16x16 { mag: 5177344, sign: false });
+    data.append(FP16x16 { mag: 5242880, sign: false });
+    data.append(FP16x16 { mag: 5308416, sign: false });
+    data.append(FP16x16 { mag: 5373952, sign: false });
+    data.append(FP16x16 { mag: 5439488, sign: false });
+    data.append(FP16x16 { mag: 5505024, sign: false });
+    data.append(FP16x16 { mag: 5570560, sign: false });
+    data.append(FP16x16 { mag: 5636096, sign: false });
+    data.append(FP16x16 { mag: 5701632, sign: false });
+    data.append(FP16x16 { mag: 5767168, sign: false });
+    data.append(FP16x16 { mag: 5832704, sign: false });
+    data.append(FP16x16 { mag: 5898240, sign: false });
+    data.append(FP16x16 { mag: 5963776, sign: false });
+    data.append(FP16x16 { mag: 6029312, sign: false });
+    data.append(FP16x16 { mag: 6094848, sign: false });
+    data.append(FP16x16 { mag: 6160384, sign: false });
+    data.append(FP16x16 { mag: 6225920, sign: false });
+    data.append(FP16x16 { mag: 6291456, sign: false });
+    data.append(FP16x16 { mag: 6356992, sign: false });
+    data.append(FP16x16 { mag: 6422528, sign: false });
+    data.append(FP16x16 { mag: 6488064, sign: false });
+    data.append(FP16x16 { mag: 6553600, sign: false });
+    data.append(FP16x16 { mag: 6619136, sign: false });
+    data.append(FP16x16 { mag: 6684672, sign: false });
+    data.append(FP16x16 { mag: 6750208, sign: false });
+    data.append(FP16x16 { mag: 6815744, sign: false });
+    data.append(FP16x16 { mag: 7864320, sign: false });
+    data.append(FP16x16 { mag: 7929856, sign: false });
+    data.append(FP16x16 { mag: 7995392, sign: false });
+    data.append(FP16x16 { mag: 8060928, sign: false });
+    data.append(FP16x16 { mag: 8126464, sign: false });
+    data.append(FP16x16 { mag: 8192000, sign: false });
+    data.append(FP16x16 { mag: 8257536, sign: false });
+    data.append(FP16x16 { mag: 8323072, sign: false });
+    data.append(FP16x16 { mag: 8388608, sign: false });
+    data.append(FP16x16 { mag: 8454144, sign: false });
+    data.append(FP16x16 { mag: 8519680, sign: false });
+    data.append(FP16x16 { mag: 8585216, sign: false });
+    data.append(FP16x16 { mag: 8650752, sign: false });
+    data.append(FP16x16 { mag: 8716288, sign: false });
+    data.append(FP16x16 { mag: 8781824, sign: false });
+    data.append(FP16x16 { mag: 8847360, sign: false });
+    data.append(FP16x16 { mag: 8912896, sign: false });
+    data.append(FP16x16 { mag: 8978432, sign: false });
+    data.append(FP16x16 { mag: 9043968, sign: false });
+    data.append(FP16x16 { mag: 9109504, sign: false });
+    data.append(FP16x16 { mag: 9175040, sign: false });
+    data.append(FP16x16 { mag: 9240576, sign: false });
+    data.append(FP16x16 { mag: 9306112, sign: false });
+    data.append(FP16x16 { mag: 9371648, sign: false });
+    data.append(FP16x16 { mag: 9437184, sign: false });
+    data.append(FP16x16 { mag: 9502720, sign: false });
+    data.append(FP16x16 { mag: 9568256, sign: false });
+    data.append(FP16x16 { mag: 9633792, sign: false });
+    data.append(FP16x16 { mag: 9699328, sign: false });
+    data.append(FP16x16 { mag: 9764864, sign: false });
+    data.append(FP16x16 { mag: 9830400, sign: false });
+    data.append(FP16x16 { mag: 9895936, sign: false });
+    data.append(FP16x16 { mag: 9961472, sign: false });
+    data.append(FP16x16 { mag: 10027008, sign: false });
+    data.append(FP16x16 { mag: 10092544, sign: false });
+    data.append(FP16x16 { mag: 10158080, sign: false });
+    data.append(FP16x16 { mag: 10223616, sign: false });
+    data.append(FP16x16 { mag: 10289152, sign: false });
+    data.append(FP16x16 { mag: 10354688, sign: false });
+    data.append(FP16x16 { mag: 10420224, sign: false });
+    data.append(FP16x16 { mag: 10485760, sign: false });
+    data.append(FP16x16 { mag: 10551296, sign: false });
+    data.append(FP16x16 { mag: 10616832, sign: false });
+    data.append(FP16x16 { mag: 10682368, sign: false });
+    data.append(FP16x16 { mag: 10747904, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp16x16_3d_axis2.cairo b/tests/nodes/compress_fp16x16_3d_axis2.cairo
new file mode 100644
index 000000000..1115fb557
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_axis2.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_fp16x16_3d_axis2() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(2));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_fp16x16_3d_axis2/input_0.cairo b/tests/nodes/compress_fp16x16_3d_axis2/input_0.cairo
new file mode 100644
index 000000000..2ddf33488
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_axis2/input_0.cairo
@@ -0,0 +1,62 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+    shape.append(3);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 0, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1114112, sign: false });
+    data.append(FP16x16 { mag: 1179648, sign: false });
+    data.append(FP16x16 { mag: 1245184, sign: false });
+    data.append(FP16x16 { mag: 1310720, sign: false });
+    data.append(FP16x16 { mag: 1376256, sign: false });
+    data.append(FP16x16 { mag: 1441792, sign: false });
+    data.append(FP16x16 { mag: 1507328, sign: false });
+    data.append(FP16x16 { mag: 1572864, sign: false });
+    data.append(FP16x16 { mag: 1638400, sign: false });
+    data.append(FP16x16 { mag: 1703936, sign: false });
+    data.append(FP16x16 { mag: 1769472, sign: false });
+    data.append(FP16x16 { mag: 1835008, sign: false });
+    data.append(FP16x16 { mag: 1900544, sign: false });
+    data.append(FP16x16 { mag: 1966080, sign: false });
+    data.append(FP16x16 { mag: 2031616, sign: false });
+    data.append(FP16x16 { mag: 2097152, sign: false });
+    data.append(FP16x16 { mag: 2162688, sign: false });
+    data.append(FP16x16 { mag: 2228224, sign: false });
+    data.append(FP16x16 { mag: 2293760, sign: false });
+    data.append(FP16x16 { mag: 2359296, sign: false });
+    data.append(FP16x16 { mag: 2424832, sign: false });
+    data.append(FP16x16 { mag: 2490368, sign: false });
+    data.append(FP16x16 { mag: 2555904, sign: false });
+    data.append(FP16x16 { mag: 2621440, sign: false });
+    data.append(FP16x16 { mag: 2686976, sign: false });
+    data.append(FP16x16 { mag: 2752512, sign: false });
+    data.append(FP16x16 { mag: 2818048, sign: false });
+    data.append(FP16x16 { mag: 2883584, sign: false });
+    data.append(FP16x16 { mag: 2949120, sign: false });
+    data.append(FP16x16 { mag: 3014656, sign: false });
+    data.append(FP16x16 { mag: 3080192, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp16x16_3d_axis2/input_1.cairo b/tests/nodes/compress_fp16x16_3d_axis2/input_1.cairo
new file mode 100644
index 000000000..f1549f634
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_axis2/input_1.cairo
@@ -0,0 +1,15 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp16x16_3d_axis2/output_0.cairo b/tests/nodes/compress_fp16x16_3d_axis2/output_0.cairo
new file mode 100644
index 000000000..4a9d7b81f
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_axis2/output_0.cairo
@@ -0,0 +1,50 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 0, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1179648, sign: false });
+    data.append(FP16x16 { mag: 1245184, sign: false });
+    data.append(FP16x16 { mag: 1310720, sign: false });
+    data.append(FP16x16 { mag: 1441792, sign: false });
+    data.append(FP16x16 { mag: 1507328, sign: false });
+    data.append(FP16x16 { mag: 1572864, sign: false });
+    data.append(FP16x16 { mag: 1703936, sign: false });
+    data.append(FP16x16 { mag: 1769472, sign: false });
+    data.append(FP16x16 { mag: 1835008, sign: false });
+    data.append(FP16x16 { mag: 1966080, sign: false });
+    data.append(FP16x16 { mag: 2031616, sign: false });
+    data.append(FP16x16 { mag: 2097152, sign: false });
+    data.append(FP16x16 { mag: 2228224, sign: false });
+    data.append(FP16x16 { mag: 2293760, sign: false });
+    data.append(FP16x16 { mag: 2359296, sign: false });
+    data.append(FP16x16 { mag: 2490368, sign: false });
+    data.append(FP16x16 { mag: 2555904, sign: false });
+    data.append(FP16x16 { mag: 2621440, sign: false });
+    data.append(FP16x16 { mag: 2752512, sign: false });
+    data.append(FP16x16 { mag: 2818048, sign: false });
+    data.append(FP16x16 { mag: 2883584, sign: false });
+    data.append(FP16x16 { mag: 3014656, sign: false });
+    data.append(FP16x16 { mag: 3080192, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp16x16_3d_axis3.cairo b/tests/nodes/compress_fp16x16_3d_axis3.cairo
new file mode 100644
index 000000000..76ef5f641
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_axis3.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_fp16x16_3d_axis3() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(3));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_fp16x16_3d_axis3/input_0.cairo b/tests/nodes/compress_fp16x16_3d_axis3/input_0.cairo
new file mode 100644
index 000000000..a189b31f1
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_axis3/input_0.cairo
@@ -0,0 +1,111 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+    shape.append(3);
+    shape.append(4);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 0, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1114112, sign: false });
+    data.append(FP16x16 { mag: 1179648, sign: false });
+    data.append(FP16x16 { mag: 1245184, sign: false });
+    data.append(FP16x16 { mag: 1310720, sign: false });
+    data.append(FP16x16 { mag: 1376256, sign: false });
+    data.append(FP16x16 { mag: 1441792, sign: false });
+    data.append(FP16x16 { mag: 1507328, sign: false });
+    data.append(FP16x16 { mag: 1572864, sign: false });
+    data.append(FP16x16 { mag: 1638400, sign: false });
+    data.append(FP16x16 { mag: 1703936, sign: false });
+    data.append(FP16x16 { mag: 1769472, sign: false });
+    data.append(FP16x16 { mag: 1835008, sign: false });
+    data.append(FP16x16 { mag: 1900544, sign: false });
+    data.append(FP16x16 { mag: 1966080, sign: false });
+    data.append(FP16x16 { mag: 2031616, sign: false });
+    data.append(FP16x16 { mag: 2097152, sign: false });
+    data.append(FP16x16 { mag: 2162688, sign: false });
+    data.append(FP16x16 { mag: 2228224, sign: false });
+    data.append(FP16x16 { mag: 2293760, sign: false });
+    data.append(FP16x16 { mag: 2359296, sign: false });
+    data.append(FP16x16 { mag: 2424832, sign: false });
+    data.append(FP16x16 { mag: 2490368, sign: false });
+    data.append(FP16x16 { mag: 2555904, sign: false });
+    data.append(FP16x16 { mag: 2621440, sign: false });
+    data.append(FP16x16 { mag: 2686976, sign: false });
+    data.append(FP16x16 { mag: 2752512, sign: false });
+    data.append(FP16x16 { mag: 2818048, sign: false });
+    data.append(FP16x16 { mag: 2883584, sign: false });
+    data.append(FP16x16 { mag: 2949120, sign: false });
+    data.append(FP16x16 { mag: 3014656, sign: false });
+    data.append(FP16x16 { mag: 3080192, sign: false });
+    data.append(FP16x16 { mag: 3145728, sign: false });
+    data.append(FP16x16 { mag: 3211264, sign: false });
+    data.append(FP16x16 { mag: 3276800, sign: false });
+    data.append(FP16x16 { mag: 3342336, sign: false });
+    data.append(FP16x16 { mag: 3407872, sign: false });
+    data.append(FP16x16 { mag: 3473408, sign: false });
+    data.append(FP16x16 { mag: 3538944, sign: false });
+    data.append(FP16x16 { mag: 3604480, sign: false });
+    data.append(FP16x16 { mag: 3670016, sign: false });
+    data.append(FP16x16 { mag: 3735552, sign: false });
+    data.append(FP16x16 { mag: 3801088, sign: false });
+    data.append(FP16x16 { mag: 3866624, sign: false });
+    data.append(FP16x16 { mag: 3932160, sign: false });
+    data.append(FP16x16 { mag: 3997696, sign: false });
+    data.append(FP16x16 { mag: 4063232, sign: false });
+    data.append(FP16x16 { mag: 4128768, sign: false });
+    data.append(FP16x16 { mag: 4194304, sign: false });
+    data.append(FP16x16 { mag: 4259840, sign: false });
+    data.append(FP16x16 { mag: 4325376, sign: false });
+    data.append(FP16x16 { mag: 4390912, sign: false });
+    data.append(FP16x16 { mag: 4456448, sign: false });
+    data.append(FP16x16 { mag: 4521984, sign: false });
+    data.append(FP16x16 { mag: 4587520, sign: false });
+    data.append(FP16x16 { mag: 4653056, sign: false });
+    data.append(FP16x16 { mag: 4718592, sign: false });
+    data.append(FP16x16 { mag: 4784128, sign: false });
+    data.append(FP16x16 { mag: 4849664, sign: false });
+    data.append(FP16x16 { mag: 4915200, sign: false });
+    data.append(FP16x16 { mag: 4980736, sign: false });
+    data.append(FP16x16 { mag: 5046272, sign: false });
+    data.append(FP16x16 { mag: 5111808, sign: false });
+    data.append(FP16x16 { mag: 5177344, sign: false });
+    data.append(FP16x16 { mag: 5242880, sign: false });
+    data.append(FP16x16 { mag: 5308416, sign: false });
+    data.append(FP16x16 { mag: 5373952, sign: false });
+    data.append(FP16x16 { mag: 5439488, sign: false });
+    data.append(FP16x16 { mag: 5505024, sign: false });
+    data.append(FP16x16 { mag: 5570560, sign: false });
+    data.append(FP16x16 { mag: 5636096, sign: false });
+    data.append(FP16x16 { mag: 5701632, sign: false });
+    data.append(FP16x16 { mag: 5767168, sign: false });
+    data.append(FP16x16 { mag: 5832704, sign: false });
+    data.append(FP16x16 { mag: 5898240, sign: false });
+    data.append(FP16x16 { mag: 5963776, sign: false });
+    data.append(FP16x16 { mag: 6029312, sign: false });
+    data.append(FP16x16 { mag: 6094848, sign: false });
+    data.append(FP16x16 { mag: 6160384, sign: false });
+    data.append(FP16x16 { mag: 6225920, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp16x16_3d_axis3/input_1.cairo b/tests/nodes/compress_fp16x16_3d_axis3/input_1.cairo
new file mode 100644
index 000000000..8d36b9136
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_axis3/input_1.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(0);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp16x16_3d_axis3/output_0.cairo b/tests/nodes/compress_fp16x16_3d_axis3/output_0.cairo
new file mode 100644
index 000000000..edfec2268
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_axis3/output_0.cairo
@@ -0,0 +1,63 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+    shape.append(3);
+    shape.append(4);
+    shape.append(1);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 0, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1179648, sign: false });
+    data.append(FP16x16 { mag: 1310720, sign: false });
+    data.append(FP16x16 { mag: 1441792, sign: false });
+    data.append(FP16x16 { mag: 1572864, sign: false });
+    data.append(FP16x16 { mag: 1703936, sign: false });
+    data.append(FP16x16 { mag: 1835008, sign: false });
+    data.append(FP16x16 { mag: 1966080, sign: false });
+    data.append(FP16x16 { mag: 2097152, sign: false });
+    data.append(FP16x16 { mag: 2228224, sign: false });
+    data.append(FP16x16 { mag: 2359296, sign: false });
+    data.append(FP16x16 { mag: 2490368, sign: false });
+    data.append(FP16x16 { mag: 2621440, sign: false });
+    data.append(FP16x16 { mag: 2752512, sign: false });
+    data.append(FP16x16 { mag: 2883584, sign: false });
+    data.append(FP16x16 { mag: 3014656, sign: false });
+    data.append(FP16x16 { mag: 3145728, sign: false });
+    data.append(FP16x16 { mag: 3276800, sign: false });
+    data.append(FP16x16 { mag: 3407872, sign: false });
+    data.append(FP16x16 { mag: 3538944, sign: false });
+    data.append(FP16x16 { mag: 3670016, sign: false });
+    data.append(FP16x16 { mag: 3801088, sign: false });
+    data.append(FP16x16 { mag: 3932160, sign: false });
+    data.append(FP16x16 { mag: 4063232, sign: false });
+    data.append(FP16x16 { mag: 4194304, sign: false });
+    data.append(FP16x16 { mag: 4325376, sign: false });
+    data.append(FP16x16 { mag: 4456448, sign: false });
+    data.append(FP16x16 { mag: 4587520, sign: false });
+    data.append(FP16x16 { mag: 4718592, sign: false });
+    data.append(FP16x16 { mag: 4849664, sign: false });
+    data.append(FP16x16 { mag: 4980736, sign: false });
+    data.append(FP16x16 { mag: 5111808, sign: false });
+    data.append(FP16x16 { mag: 5242880, sign: false });
+    data.append(FP16x16 { mag: 5373952, sign: false });
+    data.append(FP16x16 { mag: 5505024, sign: false });
+    data.append(FP16x16 { mag: 5636096, sign: false });
+    data.append(FP16x16 { mag: 5767168, sign: false });
+    data.append(FP16x16 { mag: 5898240, sign: false });
+    data.append(FP16x16 { mag: 6029312, sign: false });
+    data.append(FP16x16 { mag: 6160384, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp16x16_3d_default.cairo b/tests/nodes/compress_fp16x16_3d_default.cairo
new file mode 100644
index 000000000..aff1849e2
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_default.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_fp16x16_3d_default() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(0));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_fp16x16_3d_default/input_0.cairo b/tests/nodes/compress_fp16x16_3d_default/input_0.cairo
new file mode 100644
index 000000000..22bb148eb
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_default/input_0.cairo
@@ -0,0 +1,41 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 0, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1114112, sign: false });
+    data.append(FP16x16 { mag: 1179648, sign: false });
+    data.append(FP16x16 { mag: 1245184, sign: false });
+    data.append(FP16x16 { mag: 1310720, sign: false });
+    data.append(FP16x16 { mag: 1376256, sign: false });
+    data.append(FP16x16 { mag: 1441792, sign: false });
+    data.append(FP16x16 { mag: 1507328, sign: false });
+    data.append(FP16x16 { mag: 1572864, sign: false });
+    data.append(FP16x16 { mag: 1638400, sign: false });
+    data.append(FP16x16 { mag: 1703936, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp16x16_3d_default/input_1.cairo b/tests/nodes/compress_fp16x16_3d_default/input_1.cairo
new file mode 100644
index 000000000..5b1c8b963
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_default/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp16x16_3d_default/output_0.cairo b/tests/nodes/compress_fp16x16_3d_default/output_0.cairo
new file mode 100644
index 000000000..32e64e952
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_default/output_0.cairo
@@ -0,0 +1,32 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1114112, sign: false });
+    data.append(FP16x16 { mag: 1179648, sign: false });
+    data.append(FP16x16 { mag: 1245184, sign: false });
+    data.append(FP16x16 { mag: 1310720, sign: false });
+    data.append(FP16x16 { mag: 1376256, sign: false });
+    data.append(FP16x16 { mag: 1441792, sign: false });
+    data.append(FP16x16 { mag: 1507328, sign: false });
+    data.append(FP16x16 { mag: 1572864, sign: false });
+    data.append(FP16x16 { mag: 1638400, sign: false });
+    data.append(FP16x16 { mag: 1703936, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp16x16_3d_noaxis.cairo b/tests/nodes/compress_fp16x16_3d_noaxis.cairo
new file mode 100644
index 000000000..3c9645b1d
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_noaxis.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_fp16x16_3d_noaxis() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::None(()));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_fp16x16_3d_noaxis/input_0.cairo b/tests/nodes/compress_fp16x16_3d_noaxis/input_0.cairo
new file mode 100644
index 000000000..22bb148eb
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_noaxis/input_0.cairo
@@ -0,0 +1,41 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 0, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1114112, sign: false });
+    data.append(FP16x16 { mag: 1179648, sign: false });
+    data.append(FP16x16 { mag: 1245184, sign: false });
+    data.append(FP16x16 { mag: 1310720, sign: false });
+    data.append(FP16x16 { mag: 1376256, sign: false });
+    data.append(FP16x16 { mag: 1441792, sign: false });
+    data.append(FP16x16 { mag: 1507328, sign: false });
+    data.append(FP16x16 { mag: 1572864, sign: false });
+    data.append(FP16x16 { mag: 1638400, sign: false });
+    data.append(FP16x16 { mag: 1703936, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp16x16_3d_noaxis/input_1.cairo b/tests/nodes/compress_fp16x16_3d_noaxis/input_1.cairo
new file mode 100644
index 000000000..2f28d9179
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_noaxis/input_1.cairo
@@ -0,0 +1,20 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(9);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(0);
+    data.append(1);
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    data.append(1);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp16x16_3d_noaxis/output_0.cairo b/tests/nodes/compress_fp16x16_3d_noaxis/output_0.cairo
new file mode 100644
index 000000000..e443f84b4
--- /dev/null
+++ b/tests/nodes/compress_fp16x16_3d_noaxis/output_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(7);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 0, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp8x23_3d_axis1.cairo b/tests/nodes/compress_fp8x23_3d_axis1.cairo
new file mode 100644
index 000000000..f7edfd13a
--- /dev/null
+++ b/tests/nodes/compress_fp8x23_3d_axis1.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::operators::tensor::FP8x23TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_fp8x23_3d_axis1() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(1));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_fp8x23_3d_axis1/input_0.cairo b/tests/nodes/compress_fp8x23_3d_axis1/input_0.cairo
new file mode 100644
index 000000000..158782e65
--- /dev/null
+++ b/tests/nodes/compress_fp8x23_3d_axis1/input_0.cairo
@@ -0,0 +1,41 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 0, sign: false });
+    data.append(FP8x23 { mag: 8388608, sign: false });
+    data.append(FP8x23 { mag: 16777216, sign: false });
+    data.append(FP8x23 { mag: 25165824, sign: false });
+    data.append(FP8x23 { mag: 33554432, sign: false });
+    data.append(FP8x23 { mag: 41943040, sign: false });
+    data.append(FP8x23 { mag: 50331648, sign: false });
+    data.append(FP8x23 { mag: 58720256, sign: false });
+    data.append(FP8x23 { mag: 67108864, sign: false });
+    data.append(FP8x23 { mag: 75497472, sign: false });
+    data.append(FP8x23 { mag: 83886080, sign: false });
+    data.append(FP8x23 { mag: 92274688, sign: false });
+    data.append(FP8x23 { mag: 100663296, sign: false });
+    data.append(FP8x23 { mag: 109051904, sign: false });
+    data.append(FP8x23 { mag: 117440512, sign: false });
+    data.append(FP8x23 { mag: 125829120, sign: false });
+    data.append(FP8x23 { mag: 134217728, sign: false });
+    data.append(FP8x23 { mag: 142606336, sign: false });
+    data.append(FP8x23 { mag: 150994944, sign: false });
+    data.append(FP8x23 { mag: 159383552, sign: false });
+    data.append(FP8x23 { mag: 167772160, sign: false });
+    data.append(FP8x23 { mag: 176160768, sign: false });
+    data.append(FP8x23 { mag: 184549376, sign: false });
+    data.append(FP8x23 { mag: 192937984, sign: false });
+    data.append(FP8x23 { mag: 201326592, sign: false });
+    data.append(FP8x23 { mag: 209715200, sign: false });
+    data.append(FP8x23 { mag: 218103808, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp8x23_3d_axis1/input_1.cairo b/tests/nodes/compress_fp8x23_3d_axis1/input_1.cairo
new file mode 100644
index 000000000..5b1c8b963
--- /dev/null
+++ b/tests/nodes/compress_fp8x23_3d_axis1/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp8x23_3d_axis1/output_0.cairo b/tests/nodes/compress_fp8x23_3d_axis1/output_0.cairo
new file mode 100644
index 000000000..83aae904e
--- /dev/null
+++ b/tests/nodes/compress_fp8x23_3d_axis1/output_0.cairo
@@ -0,0 +1,32 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn output_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(2);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 25165824, sign: false });
+    data.append(FP8x23 { mag: 33554432, sign: false });
+    data.append(FP8x23 { mag: 41943040, sign: false });
+    data.append(FP8x23 { mag: 50331648, sign: false });
+    data.append(FP8x23 { mag: 58720256, sign: false });
+    data.append(FP8x23 { mag: 67108864, sign: false });
+    data.append(FP8x23 { mag: 100663296, sign: false });
+    data.append(FP8x23 { mag: 109051904, sign: false });
+    data.append(FP8x23 { mag: 117440512, sign: false });
+    data.append(FP8x23 { mag: 125829120, sign: false });
+    data.append(FP8x23 { mag: 134217728, sign: false });
+    data.append(FP8x23 { mag: 142606336, sign: false });
+    data.append(FP8x23 { mag: 176160768, sign: false });
+    data.append(FP8x23 { mag: 184549376, sign: false });
+    data.append(FP8x23 { mag: 192937984, sign: false });
+    data.append(FP8x23 { mag: 201326592, sign: false });
+    data.append(FP8x23 { mag: 209715200, sign: false });
+    data.append(FP8x23 { mag: 218103808, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp8x23_3d_axis2.cairo b/tests/nodes/compress_fp8x23_3d_axis2.cairo
new file mode 100644
index 000000000..369ffb8bf
--- /dev/null
+++ b/tests/nodes/compress_fp8x23_3d_axis2.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::operators::tensor::FP8x23TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_fp8x23_3d_axis2() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(2));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_fp8x23_3d_axis2/input_0.cairo b/tests/nodes/compress_fp8x23_3d_axis2/input_0.cairo
new file mode 100644
index 000000000..158782e65
--- /dev/null
+++ b/tests/nodes/compress_fp8x23_3d_axis2/input_0.cairo
@@ -0,0 +1,41 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 0, sign: false });
+    data.append(FP8x23 { mag: 8388608, sign: false });
+    data.append(FP8x23 { mag: 16777216, sign: false });
+    data.append(FP8x23 { mag: 25165824, sign: false });
+    data.append(FP8x23 { mag: 33554432, sign: false });
+    data.append(FP8x23 { mag: 41943040, sign: false });
+    data.append(FP8x23 { mag: 50331648, sign: false });
+    data.append(FP8x23 { mag: 58720256, sign: false });
+    data.append(FP8x23 { mag: 67108864, sign: false });
+    data.append(FP8x23 { mag: 75497472, sign: false });
+    data.append(FP8x23 { mag: 83886080, sign: false });
+    data.append(FP8x23 { mag: 92274688, sign: false });
+    data.append(FP8x23 { mag: 100663296, sign: false });
+    data.append(FP8x23 { mag: 109051904, sign: false });
+    data.append(FP8x23 { mag: 117440512, sign: false });
+    data.append(FP8x23 { mag: 125829120, sign: false });
+    data.append(FP8x23 { mag: 134217728, sign: false });
+    data.append(FP8x23 { mag: 142606336, sign: false });
+    data.append(FP8x23 { mag: 150994944, sign: false });
+    data.append(FP8x23 { mag: 159383552, sign: false });
+    data.append(FP8x23 { mag: 167772160, sign: false });
+    data.append(FP8x23 { mag: 176160768, sign: false });
+    data.append(FP8x23 { mag: 184549376, sign: false });
+    data.append(FP8x23 { mag: 192937984, sign: false });
+    data.append(FP8x23 { mag: 201326592, sign: false });
+    data.append(FP8x23 { mag: 209715200, sign: false });
+    data.append(FP8x23 { mag: 218103808, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp8x23_3d_axis2/input_1.cairo b/tests/nodes/compress_fp8x23_3d_axis2/input_1.cairo
new file mode 100644
index 000000000..5b1c8b963
--- /dev/null
+++ b/tests/nodes/compress_fp8x23_3d_axis2/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp8x23_3d_axis2/output_0.cairo b/tests/nodes/compress_fp8x23_3d_axis2/output_0.cairo
new file mode 100644
index 000000000..340000317
--- /dev/null
+++ b/tests/nodes/compress_fp8x23_3d_axis2/output_0.cairo
@@ -0,0 +1,32 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn output_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 8388608, sign: false });
+    data.append(FP8x23 { mag: 16777216, sign: false });
+    data.append(FP8x23 { mag: 33554432, sign: false });
+    data.append(FP8x23 { mag: 41943040, sign: false });
+    data.append(FP8x23 { mag: 58720256, sign: false });
+    data.append(FP8x23 { mag: 67108864, sign: false });
+    data.append(FP8x23 { mag: 83886080, sign: false });
+    data.append(FP8x23 { mag: 92274688, sign: false });
+    data.append(FP8x23 { mag: 109051904, sign: false });
+    data.append(FP8x23 { mag: 117440512, sign: false });
+    data.append(FP8x23 { mag: 134217728, sign: false });
+    data.append(FP8x23 { mag: 142606336, sign: false });
+    data.append(FP8x23 { mag: 159383552, sign: false });
+    data.append(FP8x23 { mag: 167772160, sign: false });
+    data.append(FP8x23 { mag: 184549376, sign: false });
+    data.append(FP8x23 { mag: 192937984, sign: false });
+    data.append(FP8x23 { mag: 209715200, sign: false });
+    data.append(FP8x23 { mag: 218103808, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp8x23_3d_default.cairo b/tests/nodes/compress_fp8x23_3d_default.cairo
new file mode 100644
index 000000000..eab9aa1ac
--- /dev/null
+++ b/tests/nodes/compress_fp8x23_3d_default.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::operators::tensor::FP8x23TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_fp8x23_3d_default() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(0));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_fp8x23_3d_default/input_0.cairo b/tests/nodes/compress_fp8x23_3d_default/input_0.cairo
new file mode 100644
index 000000000..158782e65
--- /dev/null
+++ b/tests/nodes/compress_fp8x23_3d_default/input_0.cairo
@@ -0,0 +1,41 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 0, sign: false });
+    data.append(FP8x23 { mag: 8388608, sign: false });
+    data.append(FP8x23 { mag: 16777216, sign: false });
+    data.append(FP8x23 { mag: 25165824, sign: false });
+    data.append(FP8x23 { mag: 33554432, sign: false });
+    data.append(FP8x23 { mag: 41943040, sign: false });
+    data.append(FP8x23 { mag: 50331648, sign: false });
+    data.append(FP8x23 { mag: 58720256, sign: false });
+    data.append(FP8x23 { mag: 67108864, sign: false });
+    data.append(FP8x23 { mag: 75497472, sign: false });
+    data.append(FP8x23 { mag: 83886080, sign: false });
+    data.append(FP8x23 { mag: 92274688, sign: false });
+    data.append(FP8x23 { mag: 100663296, sign: false });
+    data.append(FP8x23 { mag: 109051904, sign: false });
+    data.append(FP8x23 { mag: 117440512, sign: false });
+    data.append(FP8x23 { mag: 125829120, sign: false });
+    data.append(FP8x23 { mag: 134217728, sign: false });
+    data.append(FP8x23 { mag: 142606336, sign: false });
+    data.append(FP8x23 { mag: 150994944, sign: false });
+    data.append(FP8x23 { mag: 159383552, sign: false });
+    data.append(FP8x23 { mag: 167772160, sign: false });
+    data.append(FP8x23 { mag: 176160768, sign: false });
+    data.append(FP8x23 { mag: 184549376, sign: false });
+    data.append(FP8x23 { mag: 192937984, sign: false });
+    data.append(FP8x23 { mag: 201326592, sign: false });
+    data.append(FP8x23 { mag: 209715200, sign: false });
+    data.append(FP8x23 { mag: 218103808, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp8x23_3d_default/input_1.cairo b/tests/nodes/compress_fp8x23_3d_default/input_1.cairo
new file mode 100644
index 000000000..5b1c8b963
--- /dev/null
+++ b/tests/nodes/compress_fp8x23_3d_default/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_fp8x23_3d_default/output_0.cairo b/tests/nodes/compress_fp8x23_3d_default/output_0.cairo
new file mode 100644
index 000000000..baf4ef69f
--- /dev/null
+++ b/tests/nodes/compress_fp8x23_3d_default/output_0.cairo
@@ -0,0 +1,32 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn output_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 75497472, sign: false });
+    data.append(FP8x23 { mag: 83886080, sign: false });
+    data.append(FP8x23 { mag: 92274688, sign: false });
+    data.append(FP8x23 { mag: 100663296, sign: false });
+    data.append(FP8x23 { mag: 109051904, sign: false });
+    data.append(FP8x23 { mag: 117440512, sign: false });
+    data.append(FP8x23 { mag: 125829120, sign: false });
+    data.append(FP8x23 { mag: 134217728, sign: false });
+    data.append(FP8x23 { mag: 142606336, sign: false });
+    data.append(FP8x23 { mag: 150994944, sign: false });
+    data.append(FP8x23 { mag: 159383552, sign: false });
+    data.append(FP8x23 { mag: 167772160, sign: false });
+    data.append(FP8x23 { mag: 176160768, sign: false });
+    data.append(FP8x23 { mag: 184549376, sign: false });
+    data.append(FP8x23 { mag: 192937984, sign: false });
+    data.append(FP8x23 { mag: 201326592, sign: false });
+    data.append(FP8x23 { mag: 209715200, sign: false });
+    data.append(FP8x23 { mag: 218103808, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i32_3d_axis1.cairo b/tests/nodes/compress_i32_3d_axis1.cairo
new file mode 100644
index 000000000..571e5beb5
--- /dev/null
+++ b/tests/nodes/compress_i32_3d_axis1.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::I32TensorPartialEq;
+use orion::operators::tensor::I32Tensor;
+use core::array::{ArrayTrait, SpanTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_i32_3d_axis1() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(1));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_i32_3d_axis1/input_0.cairo b/tests/nodes/compress_i32_3d_axis1/input_0.cairo
new file mode 100644
index 000000000..9b4392c28
--- /dev/null
+++ b/tests/nodes/compress_i32_3d_axis1/input_0.cairo
@@ -0,0 +1,41 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::I32Tensor;
+use orion::numbers::{IntegerTrait, i32};
+
+fn input_0() -> Tensor<i32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(i32 { mag: 0, sign: false });
+    data.append(i32 { mag: 1, sign: false });
+    data.append(i32 { mag: 2, sign: false });
+    data.append(i32 { mag: 3, sign: false });
+    data.append(i32 { mag: 4, sign: false });
+    data.append(i32 { mag: 5, sign: false });
+    data.append(i32 { mag: 6, sign: false });
+    data.append(i32 { mag: 7, sign: false });
+    data.append(i32 { mag: 8, sign: false });
+    data.append(i32 { mag: 9, sign: false });
+    data.append(i32 { mag: 10, sign: false });
+    data.append(i32 { mag: 11, sign: false });
+    data.append(i32 { mag: 12, sign: false });
+    data.append(i32 { mag: 13, sign: false });
+    data.append(i32 { mag: 14, sign: false });
+    data.append(i32 { mag: 15, sign: false });
+    data.append(i32 { mag: 16, sign: false });
+    data.append(i32 { mag: 17, sign: false });
+    data.append(i32 { mag: 18, sign: false });
+    data.append(i32 { mag: 19, sign: false });
+    data.append(i32 { mag: 20, sign: false });
+    data.append(i32 { mag: 21, sign: false });
+    data.append(i32 { mag: 22, sign: false });
+    data.append(i32 { mag: 23, sign: false });
+    data.append(i32 { mag: 24, sign: false });
+    data.append(i32 { mag: 25, sign: false });
+    data.append(i32 { mag: 26, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i32_3d_axis1/input_1.cairo b/tests/nodes/compress_i32_3d_axis1/input_1.cairo
new file mode 100644
index 000000000..5b1c8b963
--- /dev/null
+++ b/tests/nodes/compress_i32_3d_axis1/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i32_3d_axis1/output_0.cairo b/tests/nodes/compress_i32_3d_axis1/output_0.cairo
new file mode 100644
index 000000000..4e04ef235
--- /dev/null
+++ b/tests/nodes/compress_i32_3d_axis1/output_0.cairo
@@ -0,0 +1,32 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::I32Tensor;
+use orion::numbers::{IntegerTrait, i32};
+
+fn output_0() -> Tensor<i32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(2);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(i32 { mag: 3, sign: false });
+    data.append(i32 { mag: 4, sign: false });
+    data.append(i32 { mag: 5, sign: false });
+    data.append(i32 { mag: 6, sign: false });
+    data.append(i32 { mag: 7, sign: false });
+    data.append(i32 { mag: 8, sign: false });
+    data.append(i32 { mag: 12, sign: false });
+    data.append(i32 { mag: 13, sign: false });
+    data.append(i32 { mag: 14, sign: false });
+    data.append(i32 { mag: 15, sign: false });
+    data.append(i32 { mag: 16, sign: false });
+    data.append(i32 { mag: 17, sign: false });
+    data.append(i32 { mag: 21, sign: false });
+    data.append(i32 { mag: 22, sign: false });
+    data.append(i32 { mag: 23, sign: false });
+    data.append(i32 { mag: 24, sign: false });
+    data.append(i32 { mag: 25, sign: false });
+    data.append(i32 { mag: 26, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i32_3d_axis2.cairo b/tests/nodes/compress_i32_3d_axis2.cairo
new file mode 100644
index 000000000..be674ffba
--- /dev/null
+++ b/tests/nodes/compress_i32_3d_axis2.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::I32TensorPartialEq;
+use orion::operators::tensor::I32Tensor;
+use core::array::{ArrayTrait, SpanTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_i32_3d_axis2() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(2));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_i32_3d_axis2/input_0.cairo b/tests/nodes/compress_i32_3d_axis2/input_0.cairo
new file mode 100644
index 000000000..9b4392c28
--- /dev/null
+++ b/tests/nodes/compress_i32_3d_axis2/input_0.cairo
@@ -0,0 +1,41 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::I32Tensor;
+use orion::numbers::{IntegerTrait, i32};
+
+fn input_0() -> Tensor<i32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(i32 { mag: 0, sign: false });
+    data.append(i32 { mag: 1, sign: false });
+    data.append(i32 { mag: 2, sign: false });
+    data.append(i32 { mag: 3, sign: false });
+    data.append(i32 { mag: 4, sign: false });
+    data.append(i32 { mag: 5, sign: false });
+    data.append(i32 { mag: 6, sign: false });
+    data.append(i32 { mag: 7, sign: false });
+    data.append(i32 { mag: 8, sign: false });
+    data.append(i32 { mag: 9, sign: false });
+    data.append(i32 { mag: 10, sign: false });
+    data.append(i32 { mag: 11, sign: false });
+    data.append(i32 { mag: 12, sign: false });
+    data.append(i32 { mag: 13, sign: false });
+    data.append(i32 { mag: 14, sign: false });
+    data.append(i32 { mag: 15, sign: false });
+    data.append(i32 { mag: 16, sign: false });
+    data.append(i32 { mag: 17, sign: false });
+    data.append(i32 { mag: 18, sign: false });
+    data.append(i32 { mag: 19, sign: false });
+    data.append(i32 { mag: 20, sign: false });
+    data.append(i32 { mag: 21, sign: false });
+    data.append(i32 { mag: 22, sign: false });
+    data.append(i32 { mag: 23, sign: false });
+    data.append(i32 { mag: 24, sign: false });
+    data.append(i32 { mag: 25, sign: false });
+    data.append(i32 { mag: 26, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i32_3d_axis2/input_1.cairo b/tests/nodes/compress_i32_3d_axis2/input_1.cairo
new file mode 100644
index 000000000..5b1c8b963
--- /dev/null
+++ b/tests/nodes/compress_i32_3d_axis2/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i32_3d_axis2/output_0.cairo b/tests/nodes/compress_i32_3d_axis2/output_0.cairo
new file mode 100644
index 000000000..697fe6ee3
--- /dev/null
+++ b/tests/nodes/compress_i32_3d_axis2/output_0.cairo
@@ -0,0 +1,32 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::I32Tensor;
+use orion::numbers::{IntegerTrait, i32};
+
+fn output_0() -> Tensor<i32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(i32 { mag: 1, sign: false });
+    data.append(i32 { mag: 2, sign: false });
+    data.append(i32 { mag: 4, sign: false });
+    data.append(i32 { mag: 5, sign: false });
+    data.append(i32 { mag: 7, sign: false });
+    data.append(i32 { mag: 8, sign: false });
+    data.append(i32 { mag: 10, sign: false });
+    data.append(i32 { mag: 11, sign: false });
+    data.append(i32 { mag: 13, sign: false });
+    data.append(i32 { mag: 14, sign: false });
+    data.append(i32 { mag: 16, sign: false });
+    data.append(i32 { mag: 17, sign: false });
+    data.append(i32 { mag: 19, sign: false });
+    data.append(i32 { mag: 20, sign: false });
+    data.append(i32 { mag: 22, sign: false });
+    data.append(i32 { mag: 23, sign: false });
+    data.append(i32 { mag: 25, sign: false });
+    data.append(i32 { mag: 26, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i32_3d_default.cairo b/tests/nodes/compress_i32_3d_default.cairo
new file mode 100644
index 000000000..4bd05fce1
--- /dev/null
+++ b/tests/nodes/compress_i32_3d_default.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::I32TensorPartialEq;
+use orion::operators::tensor::I32Tensor;
+use core::array::{ArrayTrait, SpanTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_i32_3d_default() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(0));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_i32_3d_default/input_0.cairo b/tests/nodes/compress_i32_3d_default/input_0.cairo
new file mode 100644
index 000000000..9b4392c28
--- /dev/null
+++ b/tests/nodes/compress_i32_3d_default/input_0.cairo
@@ -0,0 +1,41 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::I32Tensor;
+use orion::numbers::{IntegerTrait, i32};
+
+fn input_0() -> Tensor<i32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(i32 { mag: 0, sign: false });
+    data.append(i32 { mag: 1, sign: false });
+    data.append(i32 { mag: 2, sign: false });
+    data.append(i32 { mag: 3, sign: false });
+    data.append(i32 { mag: 4, sign: false });
+    data.append(i32 { mag: 5, sign: false });
+    data.append(i32 { mag: 6, sign: false });
+    data.append(i32 { mag: 7, sign: false });
+    data.append(i32 { mag: 8, sign: false });
+    data.append(i32 { mag: 9, sign: false });
+    data.append(i32 { mag: 10, sign: false });
+    data.append(i32 { mag: 11, sign: false });
+    data.append(i32 { mag: 12, sign: false });
+    data.append(i32 { mag: 13, sign: false });
+    data.append(i32 { mag: 14, sign: false });
+    data.append(i32 { mag: 15, sign: false });
+    data.append(i32 { mag: 16, sign: false });
+    data.append(i32 { mag: 17, sign: false });
+    data.append(i32 { mag: 18, sign: false });
+    data.append(i32 { mag: 19, sign: false });
+    data.append(i32 { mag: 20, sign: false });
+    data.append(i32 { mag: 21, sign: false });
+    data.append(i32 { mag: 22, sign: false });
+    data.append(i32 { mag: 23, sign: false });
+    data.append(i32 { mag: 24, sign: false });
+    data.append(i32 { mag: 25, sign: false });
+    data.append(i32 { mag: 26, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i32_3d_default/input_1.cairo b/tests/nodes/compress_i32_3d_default/input_1.cairo
new file mode 100644
index 000000000..5b1c8b963
--- /dev/null
+++ b/tests/nodes/compress_i32_3d_default/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i32_3d_default/output_0.cairo b/tests/nodes/compress_i32_3d_default/output_0.cairo
new file mode 100644
index 000000000..97e177389
--- /dev/null
+++ b/tests/nodes/compress_i32_3d_default/output_0.cairo
@@ -0,0 +1,32 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::I32Tensor;
+use orion::numbers::{IntegerTrait, i32};
+
+fn output_0() -> Tensor<i32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(i32 { mag: 9, sign: false });
+    data.append(i32 { mag: 10, sign: false });
+    data.append(i32 { mag: 11, sign: false });
+    data.append(i32 { mag: 12, sign: false });
+    data.append(i32 { mag: 13, sign: false });
+    data.append(i32 { mag: 14, sign: false });
+    data.append(i32 { mag: 15, sign: false });
+    data.append(i32 { mag: 16, sign: false });
+    data.append(i32 { mag: 17, sign: false });
+    data.append(i32 { mag: 18, sign: false });
+    data.append(i32 { mag: 19, sign: false });
+    data.append(i32 { mag: 20, sign: false });
+    data.append(i32 { mag: 21, sign: false });
+    data.append(i32 { mag: 22, sign: false });
+    data.append(i32 { mag: 23, sign: false });
+    data.append(i32 { mag: 24, sign: false });
+    data.append(i32 { mag: 25, sign: false });
+    data.append(i32 { mag: 26, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i8_3d_axis1.cairo b/tests/nodes/compress_i8_3d_axis1.cairo
new file mode 100644
index 000000000..fae6c2356
--- /dev/null
+++ b/tests/nodes/compress_i8_3d_axis1.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::I8Tensor;
+use orion::operators::tensor::I8TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_i8_3d_axis1() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(1));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_i8_3d_axis1/input_0.cairo b/tests/nodes/compress_i8_3d_axis1/input_0.cairo
new file mode 100644
index 000000000..c095ab988
--- /dev/null
+++ b/tests/nodes/compress_i8_3d_axis1/input_0.cairo
@@ -0,0 +1,41 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::I8Tensor;
+use orion::numbers::{IntegerTrait, i8};
+
+fn input_0() -> Tensor<i8> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(i8 { mag: 0, sign: false });
+    data.append(i8 { mag: 1, sign: false });
+    data.append(i8 { mag: 2, sign: false });
+    data.append(i8 { mag: 3, sign: false });
+    data.append(i8 { mag: 4, sign: false });
+    data.append(i8 { mag: 5, sign: false });
+    data.append(i8 { mag: 6, sign: false });
+    data.append(i8 { mag: 7, sign: false });
+    data.append(i8 { mag: 8, sign: false });
+    data.append(i8 { mag: 9, sign: false });
+    data.append(i8 { mag: 10, sign: false });
+    data.append(i8 { mag: 11, sign: false });
+    data.append(i8 { mag: 12, sign: false });
+    data.append(i8 { mag: 13, sign: false });
+    data.append(i8 { mag: 14, sign: false });
+    data.append(i8 { mag: 15, sign: false });
+    data.append(i8 { mag: 16, sign: false });
+    data.append(i8 { mag: 17, sign: false });
+    data.append(i8 { mag: 18, sign: false });
+    data.append(i8 { mag: 19, sign: false });
+    data.append(i8 { mag: 20, sign: false });
+    data.append(i8 { mag: 21, sign: false });
+    data.append(i8 { mag: 22, sign: false });
+    data.append(i8 { mag: 23, sign: false });
+    data.append(i8 { mag: 24, sign: false });
+    data.append(i8 { mag: 25, sign: false });
+    data.append(i8 { mag: 26, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i8_3d_axis1/input_1.cairo b/tests/nodes/compress_i8_3d_axis1/input_1.cairo
new file mode 100644
index 000000000..5b1c8b963
--- /dev/null
+++ b/tests/nodes/compress_i8_3d_axis1/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i8_3d_axis1/output_0.cairo b/tests/nodes/compress_i8_3d_axis1/output_0.cairo
new file mode 100644
index 000000000..0a6e60910
--- /dev/null
+++ b/tests/nodes/compress_i8_3d_axis1/output_0.cairo
@@ -0,0 +1,32 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::I8Tensor;
+use orion::numbers::{IntegerTrait, i8};
+
+fn output_0() -> Tensor<i8> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(2);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(i8 { mag: 3, sign: false });
+    data.append(i8 { mag: 4, sign: false });
+    data.append(i8 { mag: 5, sign: false });
+    data.append(i8 { mag: 6, sign: false });
+    data.append(i8 { mag: 7, sign: false });
+    data.append(i8 { mag: 8, sign: false });
+    data.append(i8 { mag: 12, sign: false });
+    data.append(i8 { mag: 13, sign: false });
+    data.append(i8 { mag: 14, sign: false });
+    data.append(i8 { mag: 15, sign: false });
+    data.append(i8 { mag: 16, sign: false });
+    data.append(i8 { mag: 17, sign: false });
+    data.append(i8 { mag: 21, sign: false });
+    data.append(i8 { mag: 22, sign: false });
+    data.append(i8 { mag: 23, sign: false });
+    data.append(i8 { mag: 24, sign: false });
+    data.append(i8 { mag: 25, sign: false });
+    data.append(i8 { mag: 26, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i8_3d_axis2.cairo b/tests/nodes/compress_i8_3d_axis2.cairo
new file mode 100644
index 000000000..f8e90c133
--- /dev/null
+++ b/tests/nodes/compress_i8_3d_axis2.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::I8Tensor;
+use orion::operators::tensor::I8TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_i8_3d_axis2() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(2));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_i8_3d_axis2/input_0.cairo b/tests/nodes/compress_i8_3d_axis2/input_0.cairo
new file mode 100644
index 000000000..c095ab988
--- /dev/null
+++ b/tests/nodes/compress_i8_3d_axis2/input_0.cairo
@@ -0,0 +1,41 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::I8Tensor;
+use orion::numbers::{IntegerTrait, i8};
+
+fn input_0() -> Tensor<i8> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(i8 { mag: 0, sign: false });
+    data.append(i8 { mag: 1, sign: false });
+    data.append(i8 { mag: 2, sign: false });
+    data.append(i8 { mag: 3, sign: false });
+    data.append(i8 { mag: 4, sign: false });
+    data.append(i8 { mag: 5, sign: false });
+    data.append(i8 { mag: 6, sign: false });
+    data.append(i8 { mag: 7, sign: false });
+    data.append(i8 { mag: 8, sign: false });
+    data.append(i8 { mag: 9, sign: false });
+    data.append(i8 { mag: 10, sign: false });
+    data.append(i8 { mag: 11, sign: false });
+    data.append(i8 { mag: 12, sign: false });
+    data.append(i8 { mag: 13, sign: false });
+    data.append(i8 { mag: 14, sign: false });
+    data.append(i8 { mag: 15, sign: false });
+    data.append(i8 { mag: 16, sign: false });
+    data.append(i8 { mag: 17, sign: false });
+    data.append(i8 { mag: 18, sign: false });
+    data.append(i8 { mag: 19, sign: false });
+    data.append(i8 { mag: 20, sign: false });
+    data.append(i8 { mag: 21, sign: false });
+    data.append(i8 { mag: 22, sign: false });
+    data.append(i8 { mag: 23, sign: false });
+    data.append(i8 { mag: 24, sign: false });
+    data.append(i8 { mag: 25, sign: false });
+    data.append(i8 { mag: 26, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i8_3d_axis2/input_1.cairo b/tests/nodes/compress_i8_3d_axis2/input_1.cairo
new file mode 100644
index 000000000..5b1c8b963
--- /dev/null
+++ b/tests/nodes/compress_i8_3d_axis2/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i8_3d_axis2/output_0.cairo b/tests/nodes/compress_i8_3d_axis2/output_0.cairo
new file mode 100644
index 000000000..f2d25b67d
--- /dev/null
+++ b/tests/nodes/compress_i8_3d_axis2/output_0.cairo
@@ -0,0 +1,32 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::I8Tensor;
+use orion::numbers::{IntegerTrait, i8};
+
+fn output_0() -> Tensor<i8> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(i8 { mag: 1, sign: false });
+    data.append(i8 { mag: 2, sign: false });
+    data.append(i8 { mag: 4, sign: false });
+    data.append(i8 { mag: 5, sign: false });
+    data.append(i8 { mag: 7, sign: false });
+    data.append(i8 { mag: 8, sign: false });
+    data.append(i8 { mag: 10, sign: false });
+    data.append(i8 { mag: 11, sign: false });
+    data.append(i8 { mag: 13, sign: false });
+    data.append(i8 { mag: 14, sign: false });
+    data.append(i8 { mag: 16, sign: false });
+    data.append(i8 { mag: 17, sign: false });
+    data.append(i8 { mag: 19, sign: false });
+    data.append(i8 { mag: 20, sign: false });
+    data.append(i8 { mag: 22, sign: false });
+    data.append(i8 { mag: 23, sign: false });
+    data.append(i8 { mag: 25, sign: false });
+    data.append(i8 { mag: 26, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i8_3d_default.cairo b/tests/nodes/compress_i8_3d_default.cairo
new file mode 100644
index 000000000..1b4052d0e
--- /dev/null
+++ b/tests/nodes/compress_i8_3d_default.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::I8Tensor;
+use orion::operators::tensor::I8TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_i8_3d_default() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(0));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_i8_3d_default/input_0.cairo b/tests/nodes/compress_i8_3d_default/input_0.cairo
new file mode 100644
index 000000000..c095ab988
--- /dev/null
+++ b/tests/nodes/compress_i8_3d_default/input_0.cairo
@@ -0,0 +1,41 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::I8Tensor;
+use orion::numbers::{IntegerTrait, i8};
+
+fn input_0() -> Tensor<i8> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(i8 { mag: 0, sign: false });
+    data.append(i8 { mag: 1, sign: false });
+    data.append(i8 { mag: 2, sign: false });
+    data.append(i8 { mag: 3, sign: false });
+    data.append(i8 { mag: 4, sign: false });
+    data.append(i8 { mag: 5, sign: false });
+    data.append(i8 { mag: 6, sign: false });
+    data.append(i8 { mag: 7, sign: false });
+    data.append(i8 { mag: 8, sign: false });
+    data.append(i8 { mag: 9, sign: false });
+    data.append(i8 { mag: 10, sign: false });
+    data.append(i8 { mag: 11, sign: false });
+    data.append(i8 { mag: 12, sign: false });
+    data.append(i8 { mag: 13, sign: false });
+    data.append(i8 { mag: 14, sign: false });
+    data.append(i8 { mag: 15, sign: false });
+    data.append(i8 { mag: 16, sign: false });
+    data.append(i8 { mag: 17, sign: false });
+    data.append(i8 { mag: 18, sign: false });
+    data.append(i8 { mag: 19, sign: false });
+    data.append(i8 { mag: 20, sign: false });
+    data.append(i8 { mag: 21, sign: false });
+    data.append(i8 { mag: 22, sign: false });
+    data.append(i8 { mag: 23, sign: false });
+    data.append(i8 { mag: 24, sign: false });
+    data.append(i8 { mag: 25, sign: false });
+    data.append(i8 { mag: 26, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i8_3d_default/input_1.cairo b/tests/nodes/compress_i8_3d_default/input_1.cairo
new file mode 100644
index 000000000..5b1c8b963
--- /dev/null
+++ b/tests/nodes/compress_i8_3d_default/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_i8_3d_default/output_0.cairo b/tests/nodes/compress_i8_3d_default/output_0.cairo
new file mode 100644
index 000000000..9472aea9f
--- /dev/null
+++ b/tests/nodes/compress_i8_3d_default/output_0.cairo
@@ -0,0 +1,32 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::I8Tensor;
+use orion::numbers::{IntegerTrait, i8};
+
+fn output_0() -> Tensor<i8> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(i8 { mag: 9, sign: false });
+    data.append(i8 { mag: 10, sign: false });
+    data.append(i8 { mag: 11, sign: false });
+    data.append(i8 { mag: 12, sign: false });
+    data.append(i8 { mag: 13, sign: false });
+    data.append(i8 { mag: 14, sign: false });
+    data.append(i8 { mag: 15, sign: false });
+    data.append(i8 { mag: 16, sign: false });
+    data.append(i8 { mag: 17, sign: false });
+    data.append(i8 { mag: 18, sign: false });
+    data.append(i8 { mag: 19, sign: false });
+    data.append(i8 { mag: 20, sign: false });
+    data.append(i8 { mag: 21, sign: false });
+    data.append(i8 { mag: 22, sign: false });
+    data.append(i8 { mag: 23, sign: false });
+    data.append(i8 { mag: 24, sign: false });
+    data.append(i8 { mag: 25, sign: false });
+    data.append(i8 { mag: 26, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_axis1.cairo b/tests/nodes/compress_u32_3d_axis1.cairo
new file mode 100644
index 000000000..7cfadc989
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis1.cairo
@@ -0,0 +1,22 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::{TensorTrait, Tensor};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::U32Tensor;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_u32_3d_axis1() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(1));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_u32_3d_axis1/input_0.cairo b/tests/nodes/compress_u32_3d_axis1/input_0.cairo
new file mode 100644
index 000000000..3b921bc94
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis1/input_0.cairo
@@ -0,0 +1,49 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(2);
+    data.append(3);
+    data.append(4);
+    data.append(5);
+    data.append(6);
+    data.append(7);
+    data.append(8);
+    data.append(9);
+    data.append(10);
+    data.append(11);
+    data.append(12);
+    data.append(13);
+    data.append(14);
+    data.append(15);
+    data.append(16);
+    data.append(17);
+    data.append(18);
+    data.append(19);
+    data.append(20);
+    data.append(21);
+    data.append(22);
+    data.append(23);
+    data.append(24);
+    data.append(25);
+    data.append(26);
+    data.append(27);
+    data.append(28);
+    data.append(29);
+    data.append(30);
+    data.append(31);
+    data.append(32);
+    data.append(33);
+    data.append(34);
+    data.append(35);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_axis1/input_1.cairo b/tests/nodes/compress_u32_3d_axis1/input_1.cairo
new file mode 100644
index 000000000..5b1c8b963
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis1/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_axis1/output_0.cairo b/tests/nodes/compress_u32_3d_axis1/output_0.cairo
new file mode 100644
index 000000000..41b41498a
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis1/output_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn output_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(2);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(3);
+    data.append(4);
+    data.append(5);
+    data.append(6);
+    data.append(7);
+    data.append(8);
+    data.append(15);
+    data.append(16);
+    data.append(17);
+    data.append(18);
+    data.append(19);
+    data.append(20);
+    data.append(27);
+    data.append(28);
+    data.append(29);
+    data.append(30);
+    data.append(31);
+    data.append(32);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_axis2.cairo b/tests/nodes/compress_u32_3d_axis2.cairo
new file mode 100644
index 000000000..9c70291c5
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis2.cairo
@@ -0,0 +1,22 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::{TensorTrait, Tensor};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::U32Tensor;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_u32_3d_axis2() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(2));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_u32_3d_axis2/input_0.cairo b/tests/nodes/compress_u32_3d_axis2/input_0.cairo
new file mode 100644
index 000000000..bb021b506
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis2/input_0.cairo
@@ -0,0 +1,61 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(2);
+    data.append(3);
+    data.append(4);
+    data.append(5);
+    data.append(6);
+    data.append(7);
+    data.append(8);
+    data.append(9);
+    data.append(10);
+    data.append(11);
+    data.append(12);
+    data.append(13);
+    data.append(14);
+    data.append(15);
+    data.append(16);
+    data.append(17);
+    data.append(18);
+    data.append(19);
+    data.append(20);
+    data.append(21);
+    data.append(22);
+    data.append(23);
+    data.append(24);
+    data.append(25);
+    data.append(26);
+    data.append(27);
+    data.append(28);
+    data.append(29);
+    data.append(30);
+    data.append(31);
+    data.append(32);
+    data.append(33);
+    data.append(34);
+    data.append(35);
+    data.append(36);
+    data.append(37);
+    data.append(38);
+    data.append(39);
+    data.append(40);
+    data.append(41);
+    data.append(42);
+    data.append(43);
+    data.append(44);
+    data.append(45);
+    data.append(46);
+    data.append(47);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_axis2/input_1.cairo b/tests/nodes/compress_u32_3d_axis2/input_1.cairo
new file mode 100644
index 000000000..5b1c8b963
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis2/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_axis2/output_0.cairo b/tests/nodes/compress_u32_3d_axis2/output_0.cairo
new file mode 100644
index 000000000..029f6af3a
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis2/output_0.cairo
@@ -0,0 +1,37 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn output_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(2);
+    data.append(5);
+    data.append(6);
+    data.append(9);
+    data.append(10);
+    data.append(13);
+    data.append(14);
+    data.append(17);
+    data.append(18);
+    data.append(21);
+    data.append(22);
+    data.append(25);
+    data.append(26);
+    data.append(29);
+    data.append(30);
+    data.append(33);
+    data.append(34);
+    data.append(37);
+    data.append(38);
+    data.append(41);
+    data.append(42);
+    data.append(45);
+    data.append(46);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_axis2_2.cairo b/tests/nodes/compress_u32_3d_axis2_2.cairo
new file mode 100644
index 000000000..850c10296
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis2_2.cairo
@@ -0,0 +1,22 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::{TensorTrait, Tensor};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::U32Tensor;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_u32_3d_axis2_2() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(2));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_u32_3d_axis2_2/input_0.cairo b/tests/nodes/compress_u32_3d_axis2_2/input_0.cairo
new file mode 100644
index 000000000..f92e7b071
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis2_2/input_0.cairo
@@ -0,0 +1,73 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(2);
+    data.append(3);
+    data.append(4);
+    data.append(5);
+    data.append(6);
+    data.append(7);
+    data.append(8);
+    data.append(9);
+    data.append(10);
+    data.append(11);
+    data.append(12);
+    data.append(13);
+    data.append(14);
+    data.append(15);
+    data.append(16);
+    data.append(17);
+    data.append(18);
+    data.append(19);
+    data.append(20);
+    data.append(21);
+    data.append(22);
+    data.append(23);
+    data.append(24);
+    data.append(25);
+    data.append(26);
+    data.append(27);
+    data.append(28);
+    data.append(29);
+    data.append(30);
+    data.append(31);
+    data.append(32);
+    data.append(33);
+    data.append(34);
+    data.append(35);
+    data.append(36);
+    data.append(37);
+    data.append(38);
+    data.append(39);
+    data.append(40);
+    data.append(41);
+    data.append(42);
+    data.append(43);
+    data.append(44);
+    data.append(45);
+    data.append(46);
+    data.append(47);
+    data.append(48);
+    data.append(49);
+    data.append(50);
+    data.append(51);
+    data.append(52);
+    data.append(53);
+    data.append(54);
+    data.append(55);
+    data.append(56);
+    data.append(57);
+    data.append(58);
+    data.append(59);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_axis2_2/input_1.cairo b/tests/nodes/compress_u32_3d_axis2_2/input_1.cairo
new file mode 100644
index 000000000..5b1c8b963
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis2_2/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_axis2_2/output_0.cairo b/tests/nodes/compress_u32_3d_axis2_2/output_0.cairo
new file mode 100644
index 000000000..e80139ba6
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis2_2/output_0.cairo
@@ -0,0 +1,37 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn output_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(2);
+    data.append(6);
+    data.append(7);
+    data.append(11);
+    data.append(12);
+    data.append(16);
+    data.append(17);
+    data.append(21);
+    data.append(22);
+    data.append(26);
+    data.append(27);
+    data.append(31);
+    data.append(32);
+    data.append(36);
+    data.append(37);
+    data.append(41);
+    data.append(42);
+    data.append(46);
+    data.append(47);
+    data.append(51);
+    data.append(52);
+    data.append(56);
+    data.append(57);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_axis3.cairo b/tests/nodes/compress_u32_3d_axis3.cairo
new file mode 100644
index 000000000..c53e3e1b1
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis3.cairo
@@ -0,0 +1,22 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::{TensorTrait, Tensor};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::U32Tensor;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_u32_3d_axis3() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(3));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_u32_3d_axis3/input_0.cairo b/tests/nodes/compress_u32_3d_axis3/input_0.cairo
new file mode 100644
index 000000000..05ec2b44a
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis3/input_0.cairo
@@ -0,0 +1,284 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(5);
+    shape.append(6);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(2);
+    data.append(3);
+    data.append(4);
+    data.append(5);
+    data.append(6);
+    data.append(7);
+    data.append(8);
+    data.append(9);
+    data.append(10);
+    data.append(11);
+    data.append(12);
+    data.append(13);
+    data.append(14);
+    data.append(15);
+    data.append(16);
+    data.append(17);
+    data.append(18);
+    data.append(19);
+    data.append(20);
+    data.append(21);
+    data.append(22);
+    data.append(23);
+    data.append(24);
+    data.append(25);
+    data.append(26);
+    data.append(27);
+    data.append(28);
+    data.append(29);
+    data.append(30);
+    data.append(31);
+    data.append(32);
+    data.append(33);
+    data.append(34);
+    data.append(35);
+    data.append(36);
+    data.append(37);
+    data.append(38);
+    data.append(39);
+    data.append(40);
+    data.append(41);
+    data.append(42);
+    data.append(43);
+    data.append(44);
+    data.append(45);
+    data.append(46);
+    data.append(47);
+    data.append(48);
+    data.append(49);
+    data.append(50);
+    data.append(51);
+    data.append(52);
+    data.append(53);
+    data.append(54);
+    data.append(55);
+    data.append(56);
+    data.append(57);
+    data.append(58);
+    data.append(59);
+    data.append(60);
+    data.append(61);
+    data.append(62);
+    data.append(63);
+    data.append(64);
+    data.append(65);
+    data.append(66);
+    data.append(67);
+    data.append(68);
+    data.append(69);
+    data.append(70);
+    data.append(71);
+    data.append(72);
+    data.append(73);
+    data.append(74);
+    data.append(75);
+    data.append(76);
+    data.append(77);
+    data.append(78);
+    data.append(79);
+    data.append(80);
+    data.append(81);
+    data.append(82);
+    data.append(83);
+    data.append(84);
+    data.append(85);
+    data.append(86);
+    data.append(87);
+    data.append(88);
+    data.append(89);
+    data.append(90);
+    data.append(91);
+    data.append(92);
+    data.append(93);
+    data.append(94);
+    data.append(95);
+    data.append(96);
+    data.append(97);
+    data.append(98);
+    data.append(99);
+    data.append(100);
+    data.append(101);
+    data.append(102);
+    data.append(103);
+    data.append(104);
+    data.append(105);
+    data.append(106);
+    data.append(107);
+    data.append(108);
+    data.append(109);
+    data.append(110);
+    data.append(111);
+    data.append(112);
+    data.append(113);
+    data.append(114);
+    data.append(115);
+    data.append(116);
+    data.append(117);
+    data.append(118);
+    data.append(119);
+    data.append(120);
+    data.append(121);
+    data.append(122);
+    data.append(123);
+    data.append(124);
+    data.append(125);
+    data.append(126);
+    data.append(127);
+    data.append(128);
+    data.append(129);
+    data.append(130);
+    data.append(131);
+    data.append(132);
+    data.append(133);
+    data.append(134);
+    data.append(135);
+    data.append(136);
+    data.append(137);
+    data.append(138);
+    data.append(139);
+    data.append(140);
+    data.append(141);
+    data.append(142);
+    data.append(143);
+    data.append(144);
+    data.append(145);
+    data.append(146);
+    data.append(147);
+    data.append(148);
+    data.append(149);
+    data.append(150);
+    data.append(151);
+    data.append(152);
+    data.append(153);
+    data.append(154);
+    data.append(155);
+    data.append(156);
+    data.append(157);
+    data.append(158);
+    data.append(159);
+    data.append(160);
+    data.append(161);
+    data.append(162);
+    data.append(163);
+    data.append(164);
+    data.append(165);
+    data.append(166);
+    data.append(167);
+    data.append(168);
+    data.append(169);
+    data.append(170);
+    data.append(171);
+    data.append(172);
+    data.append(173);
+    data.append(174);
+    data.append(175);
+    data.append(176);
+    data.append(177);
+    data.append(178);
+    data.append(179);
+    data.append(180);
+    data.append(181);
+    data.append(182);
+    data.append(183);
+    data.append(184);
+    data.append(185);
+    data.append(186);
+    data.append(187);
+    data.append(188);
+    data.append(189);
+    data.append(190);
+    data.append(191);
+    data.append(192);
+    data.append(193);
+    data.append(194);
+    data.append(195);
+    data.append(196);
+    data.append(197);
+    data.append(198);
+    data.append(199);
+    data.append(200);
+    data.append(201);
+    data.append(202);
+    data.append(203);
+    data.append(204);
+    data.append(205);
+    data.append(206);
+    data.append(207);
+    data.append(208);
+    data.append(209);
+    data.append(210);
+    data.append(211);
+    data.append(212);
+    data.append(213);
+    data.append(214);
+    data.append(215);
+    data.append(216);
+    data.append(217);
+    data.append(218);
+    data.append(219);
+    data.append(220);
+    data.append(221);
+    data.append(222);
+    data.append(223);
+    data.append(224);
+    data.append(225);
+    data.append(226);
+    data.append(227);
+    data.append(228);
+    data.append(229);
+    data.append(230);
+    data.append(231);
+    data.append(232);
+    data.append(233);
+    data.append(234);
+    data.append(235);
+    data.append(236);
+    data.append(237);
+    data.append(238);
+    data.append(239);
+    data.append(240);
+    data.append(241);
+    data.append(242);
+    data.append(243);
+    data.append(244);
+    data.append(245);
+    data.append(246);
+    data.append(247);
+    data.append(248);
+    data.append(249);
+    data.append(250);
+    data.append(251);
+    data.append(252);
+    data.append(253);
+    data.append(254);
+    data.append(255);
+    data.append(256);
+    data.append(257);
+    data.append(258);
+    data.append(259);
+    data.append(260);
+    data.append(261);
+    data.append(262);
+    data.append(263);
+    data.append(264);
+    data.append(265);
+    data.append(266);
+    data.append(267);
+    data.append(268);
+    data.append(269);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_axis3/input_1.cairo b/tests/nodes/compress_u32_3d_axis3/input_1.cairo
new file mode 100644
index 000000000..ae751534e
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis3/input_1.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(6);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(1);
+    data.append(1);
+    data.append(0);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_axis3/output_0.cairo b/tests/nodes/compress_u32_3d_axis3/output_0.cairo
new file mode 100644
index 000000000..069e45a81
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_axis3/output_0.cairo
@@ -0,0 +1,194 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn output_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(3);
+    shape.append(5);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(2);
+    data.append(3);
+    data.append(5);
+    data.append(7);
+    data.append(8);
+    data.append(9);
+    data.append(11);
+    data.append(13);
+    data.append(14);
+    data.append(15);
+    data.append(17);
+    data.append(19);
+    data.append(20);
+    data.append(21);
+    data.append(23);
+    data.append(25);
+    data.append(26);
+    data.append(27);
+    data.append(29);
+    data.append(31);
+    data.append(32);
+    data.append(33);
+    data.append(35);
+    data.append(37);
+    data.append(38);
+    data.append(39);
+    data.append(41);
+    data.append(43);
+    data.append(44);
+    data.append(45);
+    data.append(47);
+    data.append(49);
+    data.append(50);
+    data.append(51);
+    data.append(53);
+    data.append(55);
+    data.append(56);
+    data.append(57);
+    data.append(59);
+    data.append(61);
+    data.append(62);
+    data.append(63);
+    data.append(65);
+    data.append(67);
+    data.append(68);
+    data.append(69);
+    data.append(71);
+    data.append(73);
+    data.append(74);
+    data.append(75);
+    data.append(77);
+    data.append(79);
+    data.append(80);
+    data.append(81);
+    data.append(83);
+    data.append(85);
+    data.append(86);
+    data.append(87);
+    data.append(89);
+    data.append(91);
+    data.append(92);
+    data.append(93);
+    data.append(95);
+    data.append(97);
+    data.append(98);
+    data.append(99);
+    data.append(101);
+    data.append(103);
+    data.append(104);
+    data.append(105);
+    data.append(107);
+    data.append(109);
+    data.append(110);
+    data.append(111);
+    data.append(113);
+    data.append(115);
+    data.append(116);
+    data.append(117);
+    data.append(119);
+    data.append(121);
+    data.append(122);
+    data.append(123);
+    data.append(125);
+    data.append(127);
+    data.append(128);
+    data.append(129);
+    data.append(131);
+    data.append(133);
+    data.append(134);
+    data.append(135);
+    data.append(137);
+    data.append(139);
+    data.append(140);
+    data.append(141);
+    data.append(143);
+    data.append(145);
+    data.append(146);
+    data.append(147);
+    data.append(149);
+    data.append(151);
+    data.append(152);
+    data.append(153);
+    data.append(155);
+    data.append(157);
+    data.append(158);
+    data.append(159);
+    data.append(161);
+    data.append(163);
+    data.append(164);
+    data.append(165);
+    data.append(167);
+    data.append(169);
+    data.append(170);
+    data.append(171);
+    data.append(173);
+    data.append(175);
+    data.append(176);
+    data.append(177);
+    data.append(179);
+    data.append(181);
+    data.append(182);
+    data.append(183);
+    data.append(185);
+    data.append(187);
+    data.append(188);
+    data.append(189);
+    data.append(191);
+    data.append(193);
+    data.append(194);
+    data.append(195);
+    data.append(197);
+    data.append(199);
+    data.append(200);
+    data.append(201);
+    data.append(203);
+    data.append(205);
+    data.append(206);
+    data.append(207);
+    data.append(209);
+    data.append(211);
+    data.append(212);
+    data.append(213);
+    data.append(215);
+    data.append(217);
+    data.append(218);
+    data.append(219);
+    data.append(221);
+    data.append(223);
+    data.append(224);
+    data.append(225);
+    data.append(227);
+    data.append(229);
+    data.append(230);
+    data.append(231);
+    data.append(233);
+    data.append(235);
+    data.append(236);
+    data.append(237);
+    data.append(239);
+    data.append(241);
+    data.append(242);
+    data.append(243);
+    data.append(245);
+    data.append(247);
+    data.append(248);
+    data.append(249);
+    data.append(251);
+    data.append(253);
+    data.append(254);
+    data.append(255);
+    data.append(257);
+    data.append(259);
+    data.append(260);
+    data.append(261);
+    data.append(263);
+    data.append(265);
+    data.append(266);
+    data.append(267);
+    data.append(269);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_default.cairo b/tests/nodes/compress_u32_3d_default.cairo
new file mode 100644
index 000000000..a7d987eb6
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_default.cairo
@@ -0,0 +1,22 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::{TensorTrait, Tensor};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::U32Tensor;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_compress_u32_3d_default() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.compress(condition: input_1, axis: Option::Some(0));
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/compress_u32_3d_default/input_0.cairo b/tests/nodes/compress_u32_3d_default/input_0.cairo
new file mode 100644
index 000000000..f2805e81e
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_default/input_0.cairo
@@ -0,0 +1,61 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+    shape.append(4);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(2);
+    data.append(3);
+    data.append(4);
+    data.append(5);
+    data.append(6);
+    data.append(7);
+    data.append(8);
+    data.append(9);
+    data.append(10);
+    data.append(11);
+    data.append(12);
+    data.append(13);
+    data.append(14);
+    data.append(15);
+    data.append(16);
+    data.append(17);
+    data.append(18);
+    data.append(19);
+    data.append(20);
+    data.append(21);
+    data.append(22);
+    data.append(23);
+    data.append(24);
+    data.append(25);
+    data.append(26);
+    data.append(27);
+    data.append(28);
+    data.append(29);
+    data.append(30);
+    data.append(31);
+    data.append(32);
+    data.append(33);
+    data.append(34);
+    data.append(35);
+    data.append(36);
+    data.append(37);
+    data.append(38);
+    data.append(39);
+    data.append(40);
+    data.append(41);
+    data.append(42);
+    data.append(43);
+    data.append(44);
+    data.append(45);
+    data.append(46);
+    data.append(47);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_default/input_1.cairo b/tests/nodes/compress_u32_3d_default/input_1.cairo
new file mode 100644
index 000000000..43059b669
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_default/input_1.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/compress_u32_3d_default/output_0.cairo b/tests/nodes/compress_u32_3d_default/output_0.cairo
new file mode 100644
index 000000000..875197f4f
--- /dev/null
+++ b/tests/nodes/compress_u32_3d_default/output_0.cairo
@@ -0,0 +1,37 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn output_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(4);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(1);
+    data.append(2);
+    data.append(3);
+    data.append(4);
+    data.append(5);
+    data.append(6);
+    data.append(7);
+    data.append(8);
+    data.append(9);
+    data.append(10);
+    data.append(11);
+    data.append(12);
+    data.append(13);
+    data.append(14);
+    data.append(15);
+    data.append(16);
+    data.append(17);
+    data.append(18);
+    data.append(19);
+    data.append(20);
+    data.append(21);
+    data.append(22);
+    data.append(23);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/concat_from_sequence_fp16x16_new_axis_default.cairo b/tests/nodes/concat_from_sequence_fp16x16_new_axis_default.cairo
index ce6b56929..adaf44a2d 100644
--- a/tests/nodes/concat_from_sequence_fp16x16_new_axis_default.cairo
+++ b/tests/nodes/concat_from_sequence_fp16x16_new_axis_default.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::FP16x16Tensor;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::FP16x16Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_fp16x16_new_axis_default() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::None(())
     );
 
diff --git a/tests/nodes/concat_from_sequence_fp16x16_new_axis_one.cairo b/tests/nodes/concat_from_sequence_fp16x16_new_axis_one.cairo
index bbb5d9fc0..0bb989ed7 100644
--- a/tests/nodes/concat_from_sequence_fp16x16_new_axis_one.cairo
+++ b/tests/nodes/concat_from_sequence_fp16x16_new_axis_one.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::FP16x16Tensor;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::FP16x16Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_fp16x16_new_axis_one() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1)
     );
 
diff --git a/tests/nodes/concat_from_sequence_fp16x16_new_axis_zero.cairo b/tests/nodes/concat_from_sequence_fp16x16_new_axis_zero.cairo
index 7ae21d053..a2856a1cc 100644
--- a/tests/nodes/concat_from_sequence_fp16x16_new_axis_zero.cairo
+++ b/tests/nodes/concat_from_sequence_fp16x16_new_axis_zero.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::FP16x16Tensor;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::FP16x16Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_fp16x16_new_axis_zero() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0)
     );
 
diff --git a/tests/nodes/concat_from_sequence_fp8x23_new_axis_default.cairo b/tests/nodes/concat_from_sequence_fp8x23_new_axis_default.cairo
index 3383ce9f6..e569736f6 100644
--- a/tests/nodes/concat_from_sequence_fp8x23_new_axis_default.cairo
+++ b/tests/nodes/concat_from_sequence_fp8x23_new_axis_default.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::FP8x23Tensor;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::FP8x23Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_fp8x23_new_axis_default() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::None(())
     );
 
diff --git a/tests/nodes/concat_from_sequence_fp8x23_new_axis_one.cairo b/tests/nodes/concat_from_sequence_fp8x23_new_axis_one.cairo
index a032dd956..12ef40787 100644
--- a/tests/nodes/concat_from_sequence_fp8x23_new_axis_one.cairo
+++ b/tests/nodes/concat_from_sequence_fp8x23_new_axis_one.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::FP8x23Tensor;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::FP8x23Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_fp8x23_new_axis_one() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1)
     );
 
diff --git a/tests/nodes/concat_from_sequence_fp8x23_new_axis_zero.cairo b/tests/nodes/concat_from_sequence_fp8x23_new_axis_zero.cairo
index 3696d14af..a249cd309 100644
--- a/tests/nodes/concat_from_sequence_fp8x23_new_axis_zero.cairo
+++ b/tests/nodes/concat_from_sequence_fp8x23_new_axis_zero.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::FP8x23Tensor;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::FP8x23Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_fp8x23_new_axis_zero() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0)
     );
 
diff --git a/tests/nodes/concat_from_sequence_i32_new_axis_default.cairo b/tests/nodes/concat_from_sequence_i32_new_axis_default.cairo
index 8c53ebf67..e6fbf0409 100644
--- a/tests/nodes/concat_from_sequence_i32_new_axis_default.cairo
+++ b/tests/nodes/concat_from_sequence_i32_new_axis_default.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::I32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_i32_new_axis_default() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::None(())
     );
 
diff --git a/tests/nodes/concat_from_sequence_i32_new_axis_one.cairo b/tests/nodes/concat_from_sequence_i32_new_axis_one.cairo
index 855321bf4..703d0b928 100644
--- a/tests/nodes/concat_from_sequence_i32_new_axis_one.cairo
+++ b/tests/nodes/concat_from_sequence_i32_new_axis_one.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::I32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_i32_new_axis_one() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1)
     );
 
diff --git a/tests/nodes/concat_from_sequence_i32_new_axis_zero.cairo b/tests/nodes/concat_from_sequence_i32_new_axis_zero.cairo
index ca190e242..d1ffd1caf 100644
--- a/tests/nodes/concat_from_sequence_i32_new_axis_zero.cairo
+++ b/tests/nodes/concat_from_sequence_i32_new_axis_zero.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::I32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_i32_new_axis_zero() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0)
     );
 
diff --git a/tests/nodes/concat_from_sequence_i8_new_axis_default.cairo b/tests/nodes/concat_from_sequence_i8_new_axis_default.cairo
index 0cab162e7..e1bff9308 100644
--- a/tests/nodes/concat_from_sequence_i8_new_axis_default.cairo
+++ b/tests/nodes/concat_from_sequence_i8_new_axis_default.cairo
@@ -8,6 +8,8 @@ use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I8Tensor;
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::I8Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_i8_new_axis_default() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::None(())
     );
 
diff --git a/tests/nodes/concat_from_sequence_i8_new_axis_one.cairo b/tests/nodes/concat_from_sequence_i8_new_axis_one.cairo
index 59d295d3c..003b5446d 100644
--- a/tests/nodes/concat_from_sequence_i8_new_axis_one.cairo
+++ b/tests/nodes/concat_from_sequence_i8_new_axis_one.cairo
@@ -8,6 +8,8 @@ use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I8Tensor;
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::I8Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_i8_new_axis_one() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1)
     );
 
diff --git a/tests/nodes/concat_from_sequence_i8_new_axis_zero.cairo b/tests/nodes/concat_from_sequence_i8_new_axis_zero.cairo
index 0d4e4daea..1299495df 100644
--- a/tests/nodes/concat_from_sequence_i8_new_axis_zero.cairo
+++ b/tests/nodes/concat_from_sequence_i8_new_axis_zero.cairo
@@ -8,6 +8,8 @@ use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I8Tensor;
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::I8Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_i8_new_axis_zero() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0)
     );
 
diff --git a/tests/nodes/concat_from_sequence_u32_new_axis_default.cairo b/tests/nodes/concat_from_sequence_u32_new_axis_default.cairo
index 8a787ea18..474cda7db 100644
--- a/tests/nodes/concat_from_sequence_u32_new_axis_default.cairo
+++ b/tests/nodes/concat_from_sequence_u32_new_axis_default.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::U32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::U32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_u32_new_axis_default() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::None(())
     );
 
diff --git a/tests/nodes/concat_from_sequence_u32_new_axis_one.cairo b/tests/nodes/concat_from_sequence_u32_new_axis_one.cairo
index fa1fa4d9c..7d86fa0b3 100644
--- a/tests/nodes/concat_from_sequence_u32_new_axis_one.cairo
+++ b/tests/nodes/concat_from_sequence_u32_new_axis_one.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::U32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::U32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_u32_new_axis_one() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::Some(1)
     );
 
diff --git a/tests/nodes/concat_from_sequence_u32_new_axis_zero.cairo b/tests/nodes/concat_from_sequence_u32_new_axis_zero.cairo
index 5a2b1fa30..9bfbeacb0 100644
--- a/tests/nodes/concat_from_sequence_u32_new_axis_zero.cairo
+++ b/tests/nodes/concat_from_sequence_u32_new_axis_zero.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::U32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
+use orion::operators::sequence::U32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -15,7 +17,7 @@ fn test_concat_from_sequence_u32_new_axis_zero() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::concat_from_sequence(
+    let y = SequenceTrait::concat_from_sequence(
         input_0, IntegerTrait::<i32>::new(1, false), Option::Some(0)
     );
 
diff --git a/tests/nodes/gather_nd_fp16x16_3d_batch_dims1.cairo b/tests/nodes/gather_nd_fp16x16_3d_batch_dims1.cairo
index d2c0b80dd..025cc8261 100644
--- a/tests/nodes/gather_nd_fp16x16_3d_batch_dims1.cairo
+++ b/tests/nodes/gather_nd_fp16x16_3d_batch_dims1.cairo
@@ -18,7 +18,7 @@ fn test_gather_nd_fp16x16_3d_batch_dims1() {
     let input_1 = input_1::input_1();
     let z_0 = output_0::output_0();
 
-    let y_0 = input_0.gather_nd(indices:input_1, batch_dims:Option::Some(1));
+    let y_0 = input_0.gather_nd(indices: input_1, batch_dims: Option::Some(1));
 
     assert_eq(y_0, z_0);
 }
diff --git a/tests/nodes/gather_nd_fp16x16_3d_batch_dims2.cairo b/tests/nodes/gather_nd_fp16x16_3d_batch_dims2.cairo
index 507847851..677a40f6a 100644
--- a/tests/nodes/gather_nd_fp16x16_3d_batch_dims2.cairo
+++ b/tests/nodes/gather_nd_fp16x16_3d_batch_dims2.cairo
@@ -18,7 +18,7 @@ fn test_gather_nd_fp16x16_3d_batch_dims2() {
     let input_1 = input_1::input_1();
     let z_0 = output_0::output_0();
 
-    let y_0 = input_0.gather_nd(indices:input_1, batch_dims:Option::Some(2));
+    let y_0 = input_0.gather_nd(indices: input_1, batch_dims: Option::Some(2));
 
     assert_eq(y_0, z_0);
 }
diff --git a/tests/nodes/gather_nd_fp16x16_3d_default.cairo b/tests/nodes/gather_nd_fp16x16_3d_default.cairo
index ae4609a66..b8339a0d2 100644
--- a/tests/nodes/gather_nd_fp16x16_3d_default.cairo
+++ b/tests/nodes/gather_nd_fp16x16_3d_default.cairo
@@ -18,7 +18,7 @@ fn test_gather_nd_fp16x16_3d_default() {
     let input_1 = input_1::input_1();
     let z_0 = output_0::output_0();
 
-    let y_0 = input_0.gather_nd(indices:input_1, batch_dims:Option::Some(0));
+    let y_0 = input_0.gather_nd(indices: input_1, batch_dims: Option::Some(0));
 
     assert_eq(y_0, z_0);
 }
diff --git a/tests/nodes/gather_nd_fp8x23_3d_batch_dims1.cairo b/tests/nodes/gather_nd_fp8x23_3d_batch_dims1.cairo
index b9a083796..65980d91f 100644
--- a/tests/nodes/gather_nd_fp8x23_3d_batch_dims1.cairo
+++ b/tests/nodes/gather_nd_fp8x23_3d_batch_dims1.cairo
@@ -18,7 +18,7 @@ fn test_gather_nd_fp8x23_3d_batch_dims1() {
     let input_1 = input_1::input_1();
     let z_0 = output_0::output_0();
 
-    let y_0 = input_0.gather_nd(indices:input_1, batch_dims:Option::Some(1));
+    let y_0 = input_0.gather_nd(indices: input_1, batch_dims: Option::Some(1));
 
     assert_eq(y_0, z_0);
 }
diff --git a/tests/nodes/gather_nd_fp8x23_3d_batch_dims2.cairo b/tests/nodes/gather_nd_fp8x23_3d_batch_dims2.cairo
index 5e42ca893..48c812baf 100644
--- a/tests/nodes/gather_nd_fp8x23_3d_batch_dims2.cairo
+++ b/tests/nodes/gather_nd_fp8x23_3d_batch_dims2.cairo
@@ -18,7 +18,7 @@ fn test_gather_nd_fp8x23_3d_batch_dims2() {
     let input_1 = input_1::input_1();
     let z_0 = output_0::output_0();
 
-    let y_0 = input_0.gather_nd(indices:input_1, batch_dims:Option::Some(2));
+    let y_0 = input_0.gather_nd(indices: input_1, batch_dims: Option::Some(2));
 
     assert_eq(y_0, z_0);
 }
diff --git a/tests/nodes/gather_nd_fp8x23_3d_default.cairo b/tests/nodes/gather_nd_fp8x23_3d_default.cairo
index 12b6408e0..342cd2b72 100644
--- a/tests/nodes/gather_nd_fp8x23_3d_default.cairo
+++ b/tests/nodes/gather_nd_fp8x23_3d_default.cairo
@@ -18,7 +18,7 @@ fn test_gather_nd_fp8x23_3d_default() {
     let input_1 = input_1::input_1();
     let z_0 = output_0::output_0();
 
-    let y_0 = input_0.gather_nd(indices:input_1, batch_dims:Option::Some(0));
+    let y_0 = input_0.gather_nd(indices: input_1, batch_dims: Option::Some(0));
 
     assert_eq(y_0, z_0);
 }
diff --git a/tests/nodes/gather_nd_i32_3d_batch_dims1.cairo b/tests/nodes/gather_nd_i32_3d_batch_dims1.cairo
index 243b0ca16..318ccd62e 100644
--- a/tests/nodes/gather_nd_i32_3d_batch_dims1.cairo
+++ b/tests/nodes/gather_nd_i32_3d_batch_dims1.cairo
@@ -18,7 +18,7 @@ fn test_gather_nd_i32_3d_batch_dims1() {
     let input_1 = input_1::input_1();
     let z_0 = output_0::output_0();
 
-    let y_0 = input_0.gather_nd(indices:input_1, batch_dims:Option::Some(1));
+    let y_0 = input_0.gather_nd(indices: input_1, batch_dims: Option::Some(1));
 
     assert_eq(y_0, z_0);
 }
diff --git a/tests/nodes/gather_nd_i32_3d_batch_dims2.cairo b/tests/nodes/gather_nd_i32_3d_batch_dims2.cairo
index d11370b94..177c8e40f 100644
--- a/tests/nodes/gather_nd_i32_3d_batch_dims2.cairo
+++ b/tests/nodes/gather_nd_i32_3d_batch_dims2.cairo
@@ -18,7 +18,7 @@ fn test_gather_nd_i32_3d_batch_dims2() {
     let input_1 = input_1::input_1();
     let z_0 = output_0::output_0();
 
-    let y_0 = input_0.gather_nd(indices:input_1, batch_dims:Option::Some(2));
+    let y_0 = input_0.gather_nd(indices: input_1, batch_dims: Option::Some(2));
 
     assert_eq(y_0, z_0);
 }
diff --git a/tests/nodes/gather_nd_i32_3d_default.cairo b/tests/nodes/gather_nd_i32_3d_default.cairo
index 35c054093..97212f737 100644
--- a/tests/nodes/gather_nd_i32_3d_default.cairo
+++ b/tests/nodes/gather_nd_i32_3d_default.cairo
@@ -18,7 +18,7 @@ fn test_gather_nd_i32_3d_default() {
     let input_1 = input_1::input_1();
     let z_0 = output_0::output_0();
 
-    let y_0 = input_0.gather_nd(indices:input_1, batch_dims:Option::Some(0));
+    let y_0 = input_0.gather_nd(indices: input_1, batch_dims: Option::Some(0));
 
     assert_eq(y_0, z_0);
 }
diff --git a/tests/nodes/gather_nd_i8_3d_batch_dims1.cairo b/tests/nodes/gather_nd_i8_3d_batch_dims1.cairo
index ae83a8c7d..f849c8677 100644
--- a/tests/nodes/gather_nd_i8_3d_batch_dims1.cairo
+++ b/tests/nodes/gather_nd_i8_3d_batch_dims1.cairo
@@ -18,7 +18,7 @@ fn test_gather_nd_i8_3d_batch_dims1() {
     let input_1 = input_1::input_1();
     let z_0 = output_0::output_0();
 
-    let y_0 = input_0.gather_nd(indices:input_1, batch_dims:Option::Some(1));
+    let y_0 = input_0.gather_nd(indices: input_1, batch_dims: Option::Some(1));
 
     assert_eq(y_0, z_0);
 }
diff --git a/tests/nodes/gather_nd_i8_3d_default.cairo b/tests/nodes/gather_nd_i8_3d_default.cairo
index 73e1d91b2..ff7ad9252 100644
--- a/tests/nodes/gather_nd_i8_3d_default.cairo
+++ b/tests/nodes/gather_nd_i8_3d_default.cairo
@@ -18,7 +18,7 @@ fn test_gather_nd_i8_3d_default() {
     let input_1 = input_1::input_1();
     let z_0 = output_0::output_0();
 
-    let y_0 = input_0.gather_nd(indices:input_1, batch_dims:Option::Some(0));
+    let y_0 = input_0.gather_nd(indices: input_1, batch_dims: Option::Some(0));
 
     assert_eq(y_0, z_0);
 }
diff --git a/tests/nodes/gather_nd_u32_batch_dims1.cairo b/tests/nodes/gather_nd_u32_batch_dims1.cairo
index 0428ec1d5..860675f66 100644
--- a/tests/nodes/gather_nd_u32_batch_dims1.cairo
+++ b/tests/nodes/gather_nd_u32_batch_dims1.cairo
@@ -16,7 +16,7 @@ fn test_gather_nd_u32_batch_dims1() {
     let input_1 = input_1::input_1();
     let z_0 = output_0::output_0();
 
-    let y_0 = input_0.gather_nd(indices:input_1, batch_dims:Option::Some(1));
+    let y_0 = input_0.gather_nd(indices: input_1, batch_dims: Option::Some(1));
 
     assert_eq(y_0, z_0);
 }
diff --git a/tests/nodes/gather_nd_u32_batch_dims2.cairo b/tests/nodes/gather_nd_u32_batch_dims2.cairo
index 39857ef1d..f0662be99 100644
--- a/tests/nodes/gather_nd_u32_batch_dims2.cairo
+++ b/tests/nodes/gather_nd_u32_batch_dims2.cairo
@@ -16,7 +16,7 @@ fn test_gather_nd_u32_batch_dims2() {
     let input_1 = input_1::input_1();
     let z_0 = output_0::output_0();
 
-    let y_0 = input_0.gather_nd(indices:input_1, batch_dims:Option::Some(2));
+    let y_0 = input_0.gather_nd(indices: input_1, batch_dims: Option::Some(2));
 
     assert_eq(y_0, z_0);
 }
diff --git a/tests/nodes/gather_nd_u32_default.cairo b/tests/nodes/gather_nd_u32_default.cairo
index f55b49d5e..be6edd699 100644
--- a/tests/nodes/gather_nd_u32_default.cairo
+++ b/tests/nodes/gather_nd_u32_default.cairo
@@ -16,7 +16,7 @@ fn test_gather_nd_u32_default() {
     let input_1 = input_1::input_1();
     let z_0 = output_0::output_0();
 
-    let y_0 = input_0.gather_nd(indices:input_1, batch_dims:Option::Some(0));
+    let y_0 = input_0.gather_nd(indices: input_1, batch_dims: Option::Some(0));
 
     assert_eq(y_0, z_0);
 }
diff --git a/tests/nodes/layer_normalization_3d_axis0_epsilon.cairo b/tests/nodes/layer_normalization_3d_axis0_epsilon.cairo
new file mode 100644
index 000000000..9f0a27f1a
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis0_epsilon.cairo
@@ -0,0 +1,33 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_3d_axis0_epsilon() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1,
+            Option::Some(@input_2),
+            Option::Some(IntegerTrait::<i32>::new(0, false)),
+            Option::Some(FixedTrait::new(6554, false)),
+            Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_3d_axis0_epsilon/input_0.cairo b/tests/nodes/layer_normalization_3d_axis0_epsilon/input_0.cairo
new file mode 100644
index 000000000..2f4da77ea
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis0_epsilon/input_0.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 73220, sign: false });
+    data.append(FP16x16 { mag: 15621, sign: false });
+    data.append(FP16x16 { mag: 26862, sign: true });
+    data.append(FP16x16 { mag: 63971, sign: false });
+    data.append(FP16x16 { mag: 64826, sign: false });
+    data.append(FP16x16 { mag: 18837, sign: false });
+    data.append(FP16x16 { mag: 66021, sign: true });
+    data.append(FP16x16 { mag: 42181, sign: true });
+    data.append(FP16x16 { mag: 69342, sign: true });
+    data.append(FP16x16 { mag: 72001, sign: true });
+    data.append(FP16x16 { mag: 99818, sign: true });
+    data.append(FP16x16 { mag: 63088, sign: false });
+    data.append(FP16x16 { mag: 17845, sign: true });
+    data.append(FP16x16 { mag: 37020, sign: true });
+    data.append(FP16x16 { mag: 20567, sign: false });
+    data.append(FP16x16 { mag: 1924, sign: true });
+    data.append(FP16x16 { mag: 13154, sign: true });
+    data.append(FP16x16 { mag: 88735, sign: false });
+    data.append(FP16x16 { mag: 40464, sign: false });
+    data.append(FP16x16 { mag: 96907, sign: false });
+    data.append(FP16x16 { mag: 79699, sign: false });
+    data.append(FP16x16 { mag: 91862, sign: true });
+    data.append(FP16x16 { mag: 97396, sign: false });
+    data.append(FP16x16 { mag: 23929, sign: false });
+    data.append(FP16x16 { mag: 11785, sign: true });
+    data.append(FP16x16 { mag: 7747, sign: false });
+    data.append(FP16x16 { mag: 91889, sign: true });
+    data.append(FP16x16 { mag: 16735, sign: true });
+    data.append(FP16x16 { mag: 120303, sign: true });
+    data.append(FP16x16 { mag: 116144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis0_epsilon/input_1.cairo b/tests/nodes/layer_normalization_3d_axis0_epsilon/input_1.cairo
new file mode 100644
index 000000000..f169c9adb
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis0_epsilon/input_1.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 53884, sign: false });
+    data.append(FP16x16 { mag: 80807, sign: true });
+    data.append(FP16x16 { mag: 17881, sign: true });
+    data.append(FP16x16 { mag: 18476, sign: true });
+    data.append(FP16x16 { mag: 36283, sign: true });
+    data.append(FP16x16 { mag: 61330, sign: true });
+    data.append(FP16x16 { mag: 41039, sign: false });
+    data.append(FP16x16 { mag: 82065, sign: false });
+    data.append(FP16x16 { mag: 45401, sign: false });
+    data.append(FP16x16 { mag: 128118, sign: true });
+    data.append(FP16x16 { mag: 214898, sign: true });
+    data.append(FP16x16 { mag: 16418, sign: false });
+    data.append(FP16x16 { mag: 82143, sign: false });
+    data.append(FP16x16 { mag: 573, sign: true });
+    data.append(FP16x16 { mag: 48898, sign: false });
+    data.append(FP16x16 { mag: 14511, sign: false });
+    data.append(FP16x16 { mag: 11366, sign: false });
+    data.append(FP16x16 { mag: 53881, sign: false });
+    data.append(FP16x16 { mag: 27317, sign: true });
+    data.append(FP16x16 { mag: 88557, sign: false });
+    data.append(FP16x16 { mag: 14203, sign: false });
+    data.append(FP16x16 { mag: 1404, sign: true });
+    data.append(FP16x16 { mag: 30266, sign: false });
+    data.append(FP16x16 { mag: 83574, sign: true });
+    data.append(FP16x16 { mag: 82692, sign: false });
+    data.append(FP16x16 { mag: 86496, sign: false });
+    data.append(FP16x16 { mag: 101363, sign: true });
+    data.append(FP16x16 { mag: 30107, sign: true });
+    data.append(FP16x16 { mag: 40283, sign: true });
+    data.append(FP16x16 { mag: 54260, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis0_epsilon/input_2.cairo b/tests/nodes/layer_normalization_3d_axis0_epsilon/input_2.cairo
new file mode 100644
index 000000000..68ddc02f9
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis0_epsilon/input_2.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 12782, sign: true });
+    data.append(FP16x16 { mag: 34829, sign: true });
+    data.append(FP16x16 { mag: 85769, sign: true });
+    data.append(FP16x16 { mag: 76891, sign: true });
+    data.append(FP16x16 { mag: 52049, sign: true });
+    data.append(FP16x16 { mag: 129289, sign: true });
+    data.append(FP16x16 { mag: 92309, sign: true });
+    data.append(FP16x16 { mag: 48090, sign: true });
+    data.append(FP16x16 { mag: 1390, sign: false });
+    data.append(FP16x16 { mag: 10093, sign: true });
+    data.append(FP16x16 { mag: 6373, sign: true });
+    data.append(FP16x16 { mag: 91002, sign: true });
+    data.append(FP16x16 { mag: 9698, sign: false });
+    data.append(FP16x16 { mag: 103992, sign: true });
+    data.append(FP16x16 { mag: 26897, sign: true });
+    data.append(FP16x16 { mag: 67478, sign: true });
+    data.append(FP16x16 { mag: 5546, sign: false });
+    data.append(FP16x16 { mag: 55870, sign: true });
+    data.append(FP16x16 { mag: 35113, sign: true });
+    data.append(FP16x16 { mag: 267167, sign: true });
+    data.append(FP16x16 { mag: 51438, sign: true });
+    data.append(FP16x16 { mag: 13667, sign: false });
+    data.append(FP16x16 { mag: 17845, sign: false });
+    data.append(FP16x16 { mag: 92263, sign: false });
+    data.append(FP16x16 { mag: 114550, sign: true });
+    data.append(FP16x16 { mag: 31510, sign: false });
+    data.append(FP16x16 { mag: 24263, sign: true });
+    data.append(FP16x16 { mag: 68737, sign: true });
+    data.append(FP16x16 { mag: 61297, sign: true });
+    data.append(FP16x16 { mag: 33386, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis0_epsilon/output_0.cairo b/tests/nodes/layer_normalization_3d_axis0_epsilon/output_0.cairo
new file mode 100644
index 000000000..8ff9e98aa
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis0_epsilon/output_0.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 42429, sign: false });
+    data.append(FP16x16 { mag: 49634, sign: true });
+    data.append(FP16x16 { mag: 77948, sign: true });
+    data.append(FP16x16 { mag: 93325, sign: true });
+    data.append(FP16x16 { mag: 84777, sign: true });
+    data.append(FP16x16 { mag: 143407, sign: true });
+    data.append(FP16x16 { mag: 133737, sign: true });
+    data.append(FP16x16 { mag: 102353, sign: true });
+    data.append(FP16x16 { mag: 46642, sign: true });
+    data.append(FP16x16 { mag: 130431, sign: false });
+    data.append(FP16x16 { mag: 316659, sign: false });
+    data.append(FP16x16 { mag: 76609, sign: true });
+    data.append(FP16x16 { mag: 15411, sign: true });
+    data.append(FP16x16 { mag: 103656, sign: true });
+    data.append(FP16x16 { mag: 14405, sign: true });
+    data.append(FP16x16 { mag: 68539, sign: true });
+    data.append(FP16x16 { mag: 2850, sign: false });
+    data.append(FP16x16 { mag: 11550, sign: false });
+    data.append(FP16x16 { mag: 50032, sign: true });
+    data.append(FP16x16 { mag: 145784, sign: true });
+    data.append(FP16x16 { mag: 35540, sign: true });
+    data.append(FP16x16 { mag: 15615, sign: false });
+    data.append(FP16x16 { mag: 59548, sign: false });
+    data.append(FP16x16 { mag: 66808, sign: false });
+    data.append(FP16x16 { mag: 132508, sign: true });
+    data.append(FP16x16 { mag: 37408, sign: false });
+    data.append(FP16x16 { mag: 116363, sign: false });
+    data.append(FP16x16 { mag: 60021, sign: true });
+    data.append(FP16x16 { mag: 11310, sign: false });
+    data.append(FP16x16 { mag: 56233, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis1_epsilon.cairo b/tests/nodes/layer_normalization_3d_axis1_epsilon.cairo
new file mode 100644
index 000000000..d2c6400b6
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis1_epsilon.cairo
@@ -0,0 +1,32 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_3d_axis1_epsilon() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1,
+            Option::Some(@input_2),
+            Option::Some(IntegerTrait::<i32>::new(1, false)),
+            Option::Some(FixedTrait::new(6554, false)),
+            Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_3d_axis1_epsilon/input_0.cairo b/tests/nodes/layer_normalization_3d_axis1_epsilon/input_0.cairo
new file mode 100644
index 000000000..2f4da77ea
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis1_epsilon/input_0.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 73220, sign: false });
+    data.append(FP16x16 { mag: 15621, sign: false });
+    data.append(FP16x16 { mag: 26862, sign: true });
+    data.append(FP16x16 { mag: 63971, sign: false });
+    data.append(FP16x16 { mag: 64826, sign: false });
+    data.append(FP16x16 { mag: 18837, sign: false });
+    data.append(FP16x16 { mag: 66021, sign: true });
+    data.append(FP16x16 { mag: 42181, sign: true });
+    data.append(FP16x16 { mag: 69342, sign: true });
+    data.append(FP16x16 { mag: 72001, sign: true });
+    data.append(FP16x16 { mag: 99818, sign: true });
+    data.append(FP16x16 { mag: 63088, sign: false });
+    data.append(FP16x16 { mag: 17845, sign: true });
+    data.append(FP16x16 { mag: 37020, sign: true });
+    data.append(FP16x16 { mag: 20567, sign: false });
+    data.append(FP16x16 { mag: 1924, sign: true });
+    data.append(FP16x16 { mag: 13154, sign: true });
+    data.append(FP16x16 { mag: 88735, sign: false });
+    data.append(FP16x16 { mag: 40464, sign: false });
+    data.append(FP16x16 { mag: 96907, sign: false });
+    data.append(FP16x16 { mag: 79699, sign: false });
+    data.append(FP16x16 { mag: 91862, sign: true });
+    data.append(FP16x16 { mag: 97396, sign: false });
+    data.append(FP16x16 { mag: 23929, sign: false });
+    data.append(FP16x16 { mag: 11785, sign: true });
+    data.append(FP16x16 { mag: 7747, sign: false });
+    data.append(FP16x16 { mag: 91889, sign: true });
+    data.append(FP16x16 { mag: 16735, sign: true });
+    data.append(FP16x16 { mag: 120303, sign: true });
+    data.append(FP16x16 { mag: 116144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis1_epsilon/input_1.cairo b/tests/nodes/layer_normalization_3d_axis1_epsilon/input_1.cairo
new file mode 100644
index 000000000..6db5b252a
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis1_epsilon/input_1.cairo
@@ -0,0 +1,28 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 71268, sign: false });
+    data.append(FP16x16 { mag: 7868, sign: false });
+    data.append(FP16x16 { mag: 95401, sign: true });
+    data.append(FP16x16 { mag: 1087, sign: true });
+    data.append(FP16x16 { mag: 1166, sign: false });
+    data.append(FP16x16 { mag: 10185, sign: false });
+    data.append(FP16x16 { mag: 52837, sign: true });
+    data.append(FP16x16 { mag: 5760, sign: true });
+    data.append(FP16x16 { mag: 21502, sign: true });
+    data.append(FP16x16 { mag: 44185, sign: true });
+    data.append(FP16x16 { mag: 39539, sign: false });
+    data.append(FP16x16 { mag: 113293, sign: false });
+    data.append(FP16x16 { mag: 24873, sign: false });
+    data.append(FP16x16 { mag: 124246, sign: false });
+    data.append(FP16x16 { mag: 20310, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis1_epsilon/input_2.cairo b/tests/nodes/layer_normalization_3d_axis1_epsilon/input_2.cairo
new file mode 100644
index 000000000..86501b55a
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis1_epsilon/input_2.cairo
@@ -0,0 +1,28 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 37055, sign: true });
+    data.append(FP16x16 { mag: 26922, sign: false });
+    data.append(FP16x16 { mag: 155904, sign: false });
+    data.append(FP16x16 { mag: 33841, sign: true });
+    data.append(FP16x16 { mag: 53256, sign: false });
+    data.append(FP16x16 { mag: 22490, sign: false });
+    data.append(FP16x16 { mag: 110070, sign: false });
+    data.append(FP16x16 { mag: 90061, sign: true });
+    data.append(FP16x16 { mag: 44130, sign: true });
+    data.append(FP16x16 { mag: 8720, sign: true });
+    data.append(FP16x16 { mag: 61513, sign: true });
+    data.append(FP16x16 { mag: 42238, sign: true });
+    data.append(FP16x16 { mag: 18154, sign: false });
+    data.append(FP16x16 { mag: 88282, sign: false });
+    data.append(FP16x16 { mag: 29231, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis1_epsilon/output_0.cairo b/tests/nodes/layer_normalization_3d_axis1_epsilon/output_0.cairo
new file mode 100644
index 000000000..ac2abb924
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis1_epsilon/output_0.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 59799, sign: false });
+    data.append(FP16x16 { mag: 29975, sign: false });
+    data.append(FP16x16 { mag: 187208, sign: false });
+    data.append(FP16x16 { mag: 35149, sign: true });
+    data.append(FP16x16 { mag: 54676, sign: false });
+    data.append(FP16x16 { mag: 26994, sign: false });
+    data.append(FP16x16 { mag: 162286, sign: false });
+    data.append(FP16x16 { mag: 86683, sign: true });
+    data.append(FP16x16 { mag: 21676, sign: true });
+    data.append(FP16x16 { mag: 39399, sign: false });
+    data.append(FP16x16 { mag: 123115, sign: true });
+    data.append(FP16x16 { mag: 92377, sign: false });
+    data.append(FP16x16 { mag: 13773, sign: false });
+    data.append(FP16x16 { mag: 26238, sign: false });
+    data.append(FP16x16 { mag: 19656, sign: true });
+    data.append(FP16x16 { mag: 51756, sign: true });
+    data.append(FP16x16 { mag: 24121, sign: false });
+    data.append(FP16x16 { mag: 60347, sign: false });
+    data.append(FP16x16 { mag: 34230, sign: true });
+    data.append(FP16x16 { mag: 54552, sign: false });
+    data.append(FP16x16 { mag: 31465, sign: false });
+    data.append(FP16x16 { mag: 184284, sign: false });
+    data.append(FP16x16 { mag: 96496, sign: true });
+    data.append(FP16x16 { mag: 47101, sign: true });
+    data.append(FP16x16 { mag: 6198, sign: false });
+    data.append(FP16x16 { mag: 64574, sign: true });
+    data.append(FP16x16 { mag: 201408, sign: true });
+    data.append(FP16x16 { mag: 8115, sign: false });
+    data.append(FP16x16 { mag: 133313, sign: true });
+    data.append(FP16x16 { mag: 1471, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis2_epsilon.cairo b/tests/nodes/layer_normalization_3d_axis2_epsilon.cairo
new file mode 100644
index 000000000..2d3fe7a76
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis2_epsilon.cairo
@@ -0,0 +1,32 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_3d_axis2_epsilon() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1,
+            Option::Some(@input_2),
+            Option::Some(IntegerTrait::<i32>::new(2, false)),
+            Option::Some(FixedTrait::new(6554, false)),
+            Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_3d_axis2_epsilon/input_0.cairo b/tests/nodes/layer_normalization_3d_axis2_epsilon/input_0.cairo
new file mode 100644
index 000000000..2f4da77ea
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis2_epsilon/input_0.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 73220, sign: false });
+    data.append(FP16x16 { mag: 15621, sign: false });
+    data.append(FP16x16 { mag: 26862, sign: true });
+    data.append(FP16x16 { mag: 63971, sign: false });
+    data.append(FP16x16 { mag: 64826, sign: false });
+    data.append(FP16x16 { mag: 18837, sign: false });
+    data.append(FP16x16 { mag: 66021, sign: true });
+    data.append(FP16x16 { mag: 42181, sign: true });
+    data.append(FP16x16 { mag: 69342, sign: true });
+    data.append(FP16x16 { mag: 72001, sign: true });
+    data.append(FP16x16 { mag: 99818, sign: true });
+    data.append(FP16x16 { mag: 63088, sign: false });
+    data.append(FP16x16 { mag: 17845, sign: true });
+    data.append(FP16x16 { mag: 37020, sign: true });
+    data.append(FP16x16 { mag: 20567, sign: false });
+    data.append(FP16x16 { mag: 1924, sign: true });
+    data.append(FP16x16 { mag: 13154, sign: true });
+    data.append(FP16x16 { mag: 88735, sign: false });
+    data.append(FP16x16 { mag: 40464, sign: false });
+    data.append(FP16x16 { mag: 96907, sign: false });
+    data.append(FP16x16 { mag: 79699, sign: false });
+    data.append(FP16x16 { mag: 91862, sign: true });
+    data.append(FP16x16 { mag: 97396, sign: false });
+    data.append(FP16x16 { mag: 23929, sign: false });
+    data.append(FP16x16 { mag: 11785, sign: true });
+    data.append(FP16x16 { mag: 7747, sign: false });
+    data.append(FP16x16 { mag: 91889, sign: true });
+    data.append(FP16x16 { mag: 16735, sign: true });
+    data.append(FP16x16 { mag: 120303, sign: true });
+    data.append(FP16x16 { mag: 116144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis2_epsilon/input_1.cairo b/tests/nodes/layer_normalization_3d_axis2_epsilon/input_1.cairo
new file mode 100644
index 000000000..4bb7a1456
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis2_epsilon/input_1.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 49614, sign: false });
+    data.append(FP16x16 { mag: 39079, sign: true });
+    data.append(FP16x16 { mag: 59684, sign: true });
+    data.append(FP16x16 { mag: 44628, sign: false });
+    data.append(FP16x16 { mag: 45415, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis2_epsilon/input_2.cairo b/tests/nodes/layer_normalization_3d_axis2_epsilon/input_2.cairo
new file mode 100644
index 000000000..797fd7460
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis2_epsilon/input_2.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 93216, sign: true });
+    data.append(FP16x16 { mag: 37735, sign: false });
+    data.append(FP16x16 { mag: 93039, sign: false });
+    data.append(FP16x16 { mag: 65618, sign: false });
+    data.append(FP16x16 { mag: 52063, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis2_epsilon/output_0.cairo b/tests/nodes/layer_normalization_3d_axis2_epsilon/output_0.cairo
new file mode 100644
index 000000000..aa884adae
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis2_epsilon/output_0.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 53283, sign: true });
+    data.append(FP16x16 { mag: 57948, sign: false });
+    data.append(FP16x16 { mag: 182112, sign: false });
+    data.append(FP16x16 { mag: 92064, sign: false });
+    data.append(FP16x16 { mag: 79866, sign: false });
+    data.append(FP16x16 { mag: 12556, sign: true });
+    data.append(FP16x16 { mag: 57172, sign: false });
+    data.append(FP16x16 { mag: 87126, sign: false });
+    data.append(FP16x16 { mag: 39713, sign: false });
+    data.append(FP16x16 { mag: 22679, sign: false });
+    data.append(FP16x16 { mag: 165649, sign: true });
+    data.append(FP16x16 { mag: 13773, sign: true });
+    data.append(FP16x16 { mag: 96744, sign: false });
+    data.append(FP16x16 { mag: 48255, sign: false });
+    data.append(FP16x16 { mag: 78994, sign: false });
+    data.append(FP16x16 { mag: 137324, sign: true });
+    data.append(FP16x16 { mag: 81318, sign: false });
+    data.append(FP16x16 { mag: 37094, sign: false });
+    data.append(FP16x16 { mag: 64052, sign: false });
+    data.append(FP16x16 { mag: 102111, sign: false });
+    data.append(FP16x16 { mag: 51160, sign: true });
+    data.append(FP16x16 { mag: 98975, sign: false });
+    data.append(FP16x16 { mag: 27582, sign: false });
+    data.append(FP16x16 { mag: 68415, sign: false });
+    data.append(FP16x16 { mag: 32080, sign: false });
+    data.append(FP16x16 { mag: 76560, sign: true });
+    data.append(FP16x16 { mag: 70073, sign: false });
+    data.append(FP16x16 { mag: 90063, sign: false });
+    data.append(FP16x16 { mag: 13883, sign: false });
+    data.append(FP16x16 { mag: 124780, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon.cairo b/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon.cairo
new file mode 100644
index 000000000..f197b1609
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon.cairo
@@ -0,0 +1,32 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_3d_axis_negative_1_epsilon() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1,
+            Option::Some(@input_2),
+            Option::Some(IntegerTrait::<i32>::new(1, true)),
+            Option::Some(FixedTrait::new(6554, false)),
+            Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon/input_0.cairo b/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon/input_0.cairo
new file mode 100644
index 000000000..2f4da77ea
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon/input_0.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 73220, sign: false });
+    data.append(FP16x16 { mag: 15621, sign: false });
+    data.append(FP16x16 { mag: 26862, sign: true });
+    data.append(FP16x16 { mag: 63971, sign: false });
+    data.append(FP16x16 { mag: 64826, sign: false });
+    data.append(FP16x16 { mag: 18837, sign: false });
+    data.append(FP16x16 { mag: 66021, sign: true });
+    data.append(FP16x16 { mag: 42181, sign: true });
+    data.append(FP16x16 { mag: 69342, sign: true });
+    data.append(FP16x16 { mag: 72001, sign: true });
+    data.append(FP16x16 { mag: 99818, sign: true });
+    data.append(FP16x16 { mag: 63088, sign: false });
+    data.append(FP16x16 { mag: 17845, sign: true });
+    data.append(FP16x16 { mag: 37020, sign: true });
+    data.append(FP16x16 { mag: 20567, sign: false });
+    data.append(FP16x16 { mag: 1924, sign: true });
+    data.append(FP16x16 { mag: 13154, sign: true });
+    data.append(FP16x16 { mag: 88735, sign: false });
+    data.append(FP16x16 { mag: 40464, sign: false });
+    data.append(FP16x16 { mag: 96907, sign: false });
+    data.append(FP16x16 { mag: 79699, sign: false });
+    data.append(FP16x16 { mag: 91862, sign: true });
+    data.append(FP16x16 { mag: 97396, sign: false });
+    data.append(FP16x16 { mag: 23929, sign: false });
+    data.append(FP16x16 { mag: 11785, sign: true });
+    data.append(FP16x16 { mag: 7747, sign: false });
+    data.append(FP16x16 { mag: 91889, sign: true });
+    data.append(FP16x16 { mag: 16735, sign: true });
+    data.append(FP16x16 { mag: 120303, sign: true });
+    data.append(FP16x16 { mag: 116144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon/input_1.cairo b/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon/input_1.cairo
new file mode 100644
index 000000000..908e17fa1
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon/input_1.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 9463, sign: false });
+    data.append(FP16x16 { mag: 34110, sign: true });
+    data.append(FP16x16 { mag: 50067, sign: false });
+    data.append(FP16x16 { mag: 4048, sign: false });
+    data.append(FP16x16 { mag: 19840, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon/input_2.cairo b/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon/input_2.cairo
new file mode 100644
index 000000000..cf65126ba
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon/input_2.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 41329, sign: true });
+    data.append(FP16x16 { mag: 41872, sign: true });
+    data.append(FP16x16 { mag: 18851, sign: true });
+    data.append(FP16x16 { mag: 38714, sign: true });
+    data.append(FP16x16 { mag: 67617, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon/output_0.cairo b/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon/output_0.cairo
new file mode 100644
index 000000000..ed4491dc1
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_1_epsilon/output_0.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 33712, sign: true });
+    data.append(FP16x16 { mag: 24229, sign: true });
+    data.append(FP16x16 { mag: 93572, sign: true });
+    data.append(FP16x16 { mag: 36316, sign: true });
+    data.append(FP16x16 { mag: 55471, sign: true });
+    data.append(FP16x16 { mag: 25943, sign: true });
+    data.append(FP16x16 { mag: 24906, sign: true });
+    data.append(FP16x16 { mag: 13890, sign: true });
+    data.append(FP16x16 { mag: 41064, sign: true });
+    data.append(FP16x16 { mag: 80454, sign: true });
+    data.append(FP16x16 { mag: 55145, sign: true });
+    data.append(FP16x16 { mag: 86832, sign: true });
+    data.append(FP16x16 { mag: 21959, sign: true });
+    data.append(FP16x16 { mag: 40289, sign: true });
+    data.append(FP16x16 { mag: 55852, sign: true });
+    data.append(FP16x16 { mag: 49742, sign: true });
+    data.append(FP16x16 { mag: 3830, sign: true });
+    data.append(FP16x16 { mag: 28080, sign: false });
+    data.append(FP16x16 { mag: 38856, sign: true });
+    data.append(FP16x16 { mag: 45753, sign: true });
+    data.append(FP16x16 { mag: 33307, sign: true });
+    data.append(FP16x16 { mag: 11580, sign: false });
+    data.append(FP16x16 { mag: 36059, sign: false });
+    data.append(FP16x16 { mag: 38461, sign: true });
+    data.append(FP16x16 { mag: 76347, sign: true });
+    data.append(FP16x16 { mag: 38152, sign: true });
+    data.append(FP16x16 { mag: 13646, sign: true });
+    data.append(FP16x16 { mag: 16354, sign: true });
+    data.append(FP16x16 { mag: 43407, sign: true });
+    data.append(FP16x16 { mag: 35849, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon.cairo b/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon.cairo
new file mode 100644
index 000000000..0abf1233d
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon.cairo
@@ -0,0 +1,33 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_3d_axis_negative_2_epsilon() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1,
+            Option::Some(@input_2),
+            Option::Some(IntegerTrait::<i32>::new(2, true)),
+            Option::Some(FixedTrait::new(6554, false)),
+            Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon/input_0.cairo b/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon/input_0.cairo
new file mode 100644
index 000000000..2f4da77ea
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon/input_0.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 73220, sign: false });
+    data.append(FP16x16 { mag: 15621, sign: false });
+    data.append(FP16x16 { mag: 26862, sign: true });
+    data.append(FP16x16 { mag: 63971, sign: false });
+    data.append(FP16x16 { mag: 64826, sign: false });
+    data.append(FP16x16 { mag: 18837, sign: false });
+    data.append(FP16x16 { mag: 66021, sign: true });
+    data.append(FP16x16 { mag: 42181, sign: true });
+    data.append(FP16x16 { mag: 69342, sign: true });
+    data.append(FP16x16 { mag: 72001, sign: true });
+    data.append(FP16x16 { mag: 99818, sign: true });
+    data.append(FP16x16 { mag: 63088, sign: false });
+    data.append(FP16x16 { mag: 17845, sign: true });
+    data.append(FP16x16 { mag: 37020, sign: true });
+    data.append(FP16x16 { mag: 20567, sign: false });
+    data.append(FP16x16 { mag: 1924, sign: true });
+    data.append(FP16x16 { mag: 13154, sign: true });
+    data.append(FP16x16 { mag: 88735, sign: false });
+    data.append(FP16x16 { mag: 40464, sign: false });
+    data.append(FP16x16 { mag: 96907, sign: false });
+    data.append(FP16x16 { mag: 79699, sign: false });
+    data.append(FP16x16 { mag: 91862, sign: true });
+    data.append(FP16x16 { mag: 97396, sign: false });
+    data.append(FP16x16 { mag: 23929, sign: false });
+    data.append(FP16x16 { mag: 11785, sign: true });
+    data.append(FP16x16 { mag: 7747, sign: false });
+    data.append(FP16x16 { mag: 91889, sign: true });
+    data.append(FP16x16 { mag: 16735, sign: true });
+    data.append(FP16x16 { mag: 120303, sign: true });
+    data.append(FP16x16 { mag: 116144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon/input_1.cairo b/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon/input_1.cairo
new file mode 100644
index 000000000..ed3eccc4c
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon/input_1.cairo
@@ -0,0 +1,28 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 76875, sign: true });
+    data.append(FP16x16 { mag: 70595, sign: true });
+    data.append(FP16x16 { mag: 48362, sign: true });
+    data.append(FP16x16 { mag: 114023, sign: false });
+    data.append(FP16x16 { mag: 68398, sign: true });
+    data.append(FP16x16 { mag: 90609, sign: false });
+    data.append(FP16x16 { mag: 11920, sign: false });
+    data.append(FP16x16 { mag: 83372, sign: true });
+    data.append(FP16x16 { mag: 131126, sign: false });
+    data.append(FP16x16 { mag: 36226, sign: false });
+    data.append(FP16x16 { mag: 15255, sign: true });
+    data.append(FP16x16 { mag: 97966, sign: false });
+    data.append(FP16x16 { mag: 156224, sign: false });
+    data.append(FP16x16 { mag: 92550, sign: true });
+    data.append(FP16x16 { mag: 120464, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon/input_2.cairo b/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon/input_2.cairo
new file mode 100644
index 000000000..27b6c770b
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon/input_2.cairo
@@ -0,0 +1,28 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 93757, sign: false });
+    data.append(FP16x16 { mag: 4959, sign: true });
+    data.append(FP16x16 { mag: 26505, sign: false });
+    data.append(FP16x16 { mag: 1530, sign: false });
+    data.append(FP16x16 { mag: 75165, sign: true });
+    data.append(FP16x16 { mag: 97790, sign: true });
+    data.append(FP16x16 { mag: 16969, sign: true });
+    data.append(FP16x16 { mag: 61662, sign: false });
+    data.append(FP16x16 { mag: 110907, sign: true });
+    data.append(FP16x16 { mag: 17227, sign: true });
+    data.append(FP16x16 { mag: 14091, sign: false });
+    data.append(FP16x16 { mag: 52957, sign: true });
+    data.append(FP16x16 { mag: 41342, sign: false });
+    data.append(FP16x16 { mag: 34186, sign: false });
+    data.append(FP16x16 { mag: 17811, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon/output_0.cairo b/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon/output_0.cairo
new file mode 100644
index 000000000..5da7eb6ac
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_2_epsilon/output_0.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 10716, sign: true });
+    data.append(FP16x16 { mag: 32353, sign: true });
+    data.append(FP16x16 { mag: 42375, sign: false });
+    data.append(FP16x16 { mag: 138711, sign: false });
+    data.append(FP16x16 { mag: 158441, sign: true });
+    data.append(FP16x16 { mag: 57718, sign: true });
+    data.append(FP16x16 { mag: 28750, sign: true });
+    data.append(FP16x16 { mag: 110550, sign: false });
+    data.append(FP16x16 { mag: 247834, sign: true });
+    data.append(FP16x16 { mag: 56680, sign: true });
+    data.append(FP16x16 { mag: 37859, sign: false });
+    data.append(FP16x16 { mag: 63446, sign: false });
+    data.append(FP16x16 { mag: 13827, sign: false });
+    data.append(FP16x16 { mag: 80402, sign: false });
+    data.append(FP16x16 { mag: 74600, sign: false });
+    data.append(FP16x16 { mag: 109615, sign: false });
+    data.append(FP16x16 { mag: 20165, sign: false });
+    data.append(FP16x16 { mag: 21935, sign: true });
+    data.append(FP16x16 { mag: 42407, sign: false });
+    data.append(FP16x16 { mag: 151122, sign: true });
+    data.append(FP16x16 { mag: 17941, sign: true });
+    data.append(FP16x16 { mag: 33712, sign: true });
+    data.append(FP16x16 { mag: 31466, sign: true });
+    data.append(FP16x16 { mag: 92787, sign: true });
+    data.append(FP16x16 { mag: 29459, sign: true });
+    data.append(FP16x16 { mag: 15272, sign: false });
+    data.append(FP16x16 { mag: 190594, sign: true });
+    data.append(FP16x16 { mag: 21712, sign: true });
+    data.append(FP16x16 { mag: 199251, sign: false });
+    data.append(FP16x16 { mag: 182464, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon.cairo b/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon.cairo
new file mode 100644
index 000000000..2c56a29de
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon.cairo
@@ -0,0 +1,33 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_3d_axis_negative_3_epsilon() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1,
+            Option::Some(@input_2),
+            Option::Some(IntegerTrait::<i32>::new(3, true)),
+            Option::Some(FixedTrait::new(6554, false)),
+            Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon/input_0.cairo b/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon/input_0.cairo
new file mode 100644
index 000000000..2f4da77ea
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon/input_0.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 73220, sign: false });
+    data.append(FP16x16 { mag: 15621, sign: false });
+    data.append(FP16x16 { mag: 26862, sign: true });
+    data.append(FP16x16 { mag: 63971, sign: false });
+    data.append(FP16x16 { mag: 64826, sign: false });
+    data.append(FP16x16 { mag: 18837, sign: false });
+    data.append(FP16x16 { mag: 66021, sign: true });
+    data.append(FP16x16 { mag: 42181, sign: true });
+    data.append(FP16x16 { mag: 69342, sign: true });
+    data.append(FP16x16 { mag: 72001, sign: true });
+    data.append(FP16x16 { mag: 99818, sign: true });
+    data.append(FP16x16 { mag: 63088, sign: false });
+    data.append(FP16x16 { mag: 17845, sign: true });
+    data.append(FP16x16 { mag: 37020, sign: true });
+    data.append(FP16x16 { mag: 20567, sign: false });
+    data.append(FP16x16 { mag: 1924, sign: true });
+    data.append(FP16x16 { mag: 13154, sign: true });
+    data.append(FP16x16 { mag: 88735, sign: false });
+    data.append(FP16x16 { mag: 40464, sign: false });
+    data.append(FP16x16 { mag: 96907, sign: false });
+    data.append(FP16x16 { mag: 79699, sign: false });
+    data.append(FP16x16 { mag: 91862, sign: true });
+    data.append(FP16x16 { mag: 97396, sign: false });
+    data.append(FP16x16 { mag: 23929, sign: false });
+    data.append(FP16x16 { mag: 11785, sign: true });
+    data.append(FP16x16 { mag: 7747, sign: false });
+    data.append(FP16x16 { mag: 91889, sign: true });
+    data.append(FP16x16 { mag: 16735, sign: true });
+    data.append(FP16x16 { mag: 120303, sign: true });
+    data.append(FP16x16 { mag: 116144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon/input_1.cairo b/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon/input_1.cairo
new file mode 100644
index 000000000..07b6687de
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon/input_1.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 51329, sign: true });
+    data.append(FP16x16 { mag: 47264, sign: true });
+    data.append(FP16x16 { mag: 78049, sign: false });
+    data.append(FP16x16 { mag: 31332, sign: true });
+    data.append(FP16x16 { mag: 64228, sign: false });
+    data.append(FP16x16 { mag: 50183, sign: false });
+    data.append(FP16x16 { mag: 111933, sign: true });
+    data.append(FP16x16 { mag: 37549, sign: true });
+    data.append(FP16x16 { mag: 48542, sign: true });
+    data.append(FP16x16 { mag: 13252, sign: true });
+    data.append(FP16x16 { mag: 63185, sign: true });
+    data.append(FP16x16 { mag: 2871, sign: false });
+    data.append(FP16x16 { mag: 57251, sign: false });
+    data.append(FP16x16 { mag: 15125, sign: true });
+    data.append(FP16x16 { mag: 75974, sign: false });
+    data.append(FP16x16 { mag: 29448, sign: true });
+    data.append(FP16x16 { mag: 118787, sign: false });
+    data.append(FP16x16 { mag: 85238, sign: true });
+    data.append(FP16x16 { mag: 6392, sign: true });
+    data.append(FP16x16 { mag: 32667, sign: true });
+    data.append(FP16x16 { mag: 306, sign: false });
+    data.append(FP16x16 { mag: 53902, sign: true });
+    data.append(FP16x16 { mag: 25940, sign: true });
+    data.append(FP16x16 { mag: 38753, sign: true });
+    data.append(FP16x16 { mag: 73289, sign: true });
+    data.append(FP16x16 { mag: 47552, sign: false });
+    data.append(FP16x16 { mag: 27826, sign: false });
+    data.append(FP16x16 { mag: 47550, sign: false });
+    data.append(FP16x16 { mag: 36199, sign: true });
+    data.append(FP16x16 { mag: 43172, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon/input_2.cairo b/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon/input_2.cairo
new file mode 100644
index 000000000..85700c760
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon/input_2.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 6143, sign: false });
+    data.append(FP16x16 { mag: 4674, sign: false });
+    data.append(FP16x16 { mag: 48051, sign: true });
+    data.append(FP16x16 { mag: 18813, sign: false });
+    data.append(FP16x16 { mag: 46995, sign: false });
+    data.append(FP16x16 { mag: 20870, sign: true });
+    data.append(FP16x16 { mag: 56843, sign: false });
+    data.append(FP16x16 { mag: 81615, sign: false });
+    data.append(FP16x16 { mag: 92340, sign: false });
+    data.append(FP16x16 { mag: 84516, sign: false });
+    data.append(FP16x16 { mag: 82019, sign: false });
+    data.append(FP16x16 { mag: 51674, sign: false });
+    data.append(FP16x16 { mag: 52303, sign: false });
+    data.append(FP16x16 { mag: 7441, sign: true });
+    data.append(FP16x16 { mag: 35138, sign: false });
+    data.append(FP16x16 { mag: 78581, sign: false });
+    data.append(FP16x16 { mag: 6660, sign: false });
+    data.append(FP16x16 { mag: 137669, sign: true });
+    data.append(FP16x16 { mag: 12790, sign: true });
+    data.append(FP16x16 { mag: 144767, sign: false });
+    data.append(FP16x16 { mag: 10893, sign: true });
+    data.append(FP16x16 { mag: 26226, sign: true });
+    data.append(FP16x16 { mag: 64470, sign: false });
+    data.append(FP16x16 { mag: 22466, sign: false });
+    data.append(FP16x16 { mag: 101996, sign: true });
+    data.append(FP16x16 { mag: 46134, sign: true });
+    data.append(FP16x16 { mag: 81851, sign: true });
+    data.append(FP16x16 { mag: 176946, sign: false });
+    data.append(FP16x16 { mag: 6446, sign: true });
+    data.append(FP16x16 { mag: 77193, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon/output_0.cairo b/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon/output_0.cairo
new file mode 100644
index 000000000..7d9e964c0
--- /dev/null
+++ b/tests/nodes/layer_normalization_3d_axis_negative_3_epsilon/output_0.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 46451, sign: true });
+    data.append(FP16x16 { mag: 3984, sign: true });
+    data.append(FP16x16 { mag: 82191, sign: true });
+    data.append(FP16x16 { mag: 9056, sign: true });
+    data.append(FP16x16 { mag: 104929, sign: false });
+    data.append(FP16x16 { mag: 9318, sign: true });
+    data.append(FP16x16 { mag: 169836, sign: false });
+    data.append(FP16x16 { mag: 106443, sign: false });
+    data.append(FP16x16 { mag: 143696, sign: false });
+    data.append(FP16x16 { mag: 99052, sign: false });
+    data.append(FP16x16 { mag: 176999, sign: false });
+    data.append(FP16x16 { mag: 54192, sign: false });
+    data.append(FP16x16 { mag: 34801, sign: false });
+    data.append(FP16x16 { mag: 1418, sign: false });
+    data.append(FP16x16 { mag: 54547, sign: false });
+    data.append(FP16x16 { mag: 80734, sign: false });
+    data.append(FP16x16 { mag: 21511, sign: true });
+    data.append(FP16x16 { mag: 244326, sign: true });
+    data.append(FP16x16 { mag: 16281, sign: true });
+    data.append(FP16x16 { mag: 99990, sign: false });
+    data.append(FP16x16 { mag: 10550, sign: true });
+    data.append(FP16x16 { mag: 48534, sign: false });
+    data.append(FP16x16 { mag: 28730, sign: false });
+    data.append(FP16x16 { mag: 10662, sign: false });
+    data.append(FP16x16 { mag: 86081, sign: true });
+    data.append(FP16x16 { mag: 42891, sign: true });
+    data.append(FP16x16 { mag: 120456, sign: true });
+    data.append(FP16x16 { mag: 163181, sign: false });
+    data.append(FP16x16 { mag: 58800, sign: false });
+    data.append(FP16x16 { mag: 5886, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-1/input_0.cairo b/tests/nodes/layer_normalization_4d_axis-1/input_0.cairo
new file mode 100644
index 000000000..796ab8161
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-1/input_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 50439, sign: true });
+    data.append(FP16x16 { mag: 80584, sign: false });
+    data.append(FP16x16 { mag: 108804, sign: false });
+    data.append(FP16x16 { mag: 64736, sign: true });
+    data.append(FP16x16 { mag: 35989, sign: false });
+    data.append(FP16x16 { mag: 83918, sign: false });
+    data.append(FP16x16 { mag: 80462, sign: true });
+    data.append(FP16x16 { mag: 43318, sign: false });
+    data.append(FP16x16 { mag: 65651, sign: true });
+    data.append(FP16x16 { mag: 11331, sign: true });
+    data.append(FP16x16 { mag: 25254, sign: true });
+    data.append(FP16x16 { mag: 62677, sign: false });
+    data.append(FP16x16 { mag: 8412, sign: false });
+    data.append(FP16x16 { mag: 64067, sign: true });
+    data.append(FP16x16 { mag: 11592, sign: true });
+    data.append(FP16x16 { mag: 10557, sign: false });
+    data.append(FP16x16 { mag: 33540, sign: true });
+    data.append(FP16x16 { mag: 50007, sign: true });
+    data.append(FP16x16 { mag: 66942, sign: true });
+    data.append(FP16x16 { mag: 24781, sign: true });
+    data.append(FP16x16 { mag: 70618, sign: true });
+    data.append(FP16x16 { mag: 91944, sign: false });
+    data.append(FP16x16 { mag: 25130, sign: false });
+    data.append(FP16x16 { mag: 70069, sign: true });
+    data.append(FP16x16 { mag: 19429, sign: false });
+    data.append(FP16x16 { mag: 17607, sign: true });
+    data.append(FP16x16 { mag: 20059, sign: true });
+    data.append(FP16x16 { mag: 30637, sign: false });
+    data.append(FP16x16 { mag: 110305, sign: true });
+    data.append(FP16x16 { mag: 47227, sign: true });
+    data.append(FP16x16 { mag: 52701, sign: false });
+    data.append(FP16x16 { mag: 113586, sign: true });
+    data.append(FP16x16 { mag: 28893, sign: false });
+    data.append(FP16x16 { mag: 19309, sign: true });
+    data.append(FP16x16 { mag: 9704, sign: true });
+    data.append(FP16x16 { mag: 56793, sign: false });
+    data.append(FP16x16 { mag: 44072, sign: false });
+    data.append(FP16x16 { mag: 2660, sign: true });
+    data.append(FP16x16 { mag: 29401, sign: true });
+    data.append(FP16x16 { mag: 36736, sign: true });
+    data.append(FP16x16 { mag: 86250, sign: false });
+    data.append(FP16x16 { mag: 38971, sign: true });
+    data.append(FP16x16 { mag: 138249, sign: true });
+    data.append(FP16x16 { mag: 36048, sign: false });
+    data.append(FP16x16 { mag: 37452, sign: false });
+    data.append(FP16x16 { mag: 23181, sign: true });
+    data.append(FP16x16 { mag: 35955, sign: true });
+    data.append(FP16x16 { mag: 51748, sign: true });
+    data.append(FP16x16 { mag: 57097, sign: true });
+    data.append(FP16x16 { mag: 91645, sign: true });
+    data.append(FP16x16 { mag: 727, sign: true });
+    data.append(FP16x16 { mag: 26384, sign: true });
+    data.append(FP16x16 { mag: 1331, sign: false });
+    data.append(FP16x16 { mag: 98672, sign: false });
+    data.append(FP16x16 { mag: 82596, sign: false });
+    data.append(FP16x16 { mag: 100984, sign: true });
+    data.append(FP16x16 { mag: 88934, sign: true });
+    data.append(FP16x16 { mag: 110736, sign: false });
+    data.append(FP16x16 { mag: 106050, sign: true });
+    data.append(FP16x16 { mag: 43286, sign: true });
+    data.append(FP16x16 { mag: 114427, sign: false });
+    data.append(FP16x16 { mag: 26160, sign: false });
+    data.append(FP16x16 { mag: 19914, sign: true });
+    data.append(FP16x16 { mag: 168031, sign: false });
+    data.append(FP16x16 { mag: 70834, sign: true });
+    data.append(FP16x16 { mag: 62785, sign: false });
+    data.append(FP16x16 { mag: 20711, sign: true });
+    data.append(FP16x16 { mag: 150814, sign: true });
+    data.append(FP16x16 { mag: 19909, sign: true });
+    data.append(FP16x16 { mag: 4360, sign: true });
+    data.append(FP16x16 { mag: 38550, sign: false });
+    data.append(FP16x16 { mag: 155210, sign: false });
+    data.append(FP16x16 { mag: 49387, sign: true });
+    data.append(FP16x16 { mag: 48606, sign: false });
+    data.append(FP16x16 { mag: 26297, sign: false });
+    data.append(FP16x16 { mag: 5832, sign: true });
+    data.append(FP16x16 { mag: 67019, sign: false });
+    data.append(FP16x16 { mag: 108552, sign: false });
+    data.append(FP16x16 { mag: 38302, sign: true });
+    data.append(FP16x16 { mag: 67467, sign: false });
+    data.append(FP16x16 { mag: 123524, sign: false });
+    data.append(FP16x16 { mag: 38110, sign: true });
+    data.append(FP16x16 { mag: 49459, sign: true });
+    data.append(FP16x16 { mag: 89977, sign: false });
+    data.append(FP16x16 { mag: 3116, sign: false });
+    data.append(FP16x16 { mag: 139868, sign: true });
+    data.append(FP16x16 { mag: 3777, sign: false });
+    data.append(FP16x16 { mag: 93508, sign: false });
+    data.append(FP16x16 { mag: 18473, sign: true });
+    data.append(FP16x16 { mag: 9749, sign: false });
+    data.append(FP16x16 { mag: 69280, sign: false });
+    data.append(FP16x16 { mag: 31261, sign: true });
+    data.append(FP16x16 { mag: 70810, sign: true });
+    data.append(FP16x16 { mag: 53719, sign: true });
+    data.append(FP16x16 { mag: 25490, sign: false });
+    data.append(FP16x16 { mag: 76561, sign: true });
+    data.append(FP16x16 { mag: 87810, sign: true });
+    data.append(FP16x16 { mag: 54546, sign: true });
+    data.append(FP16x16 { mag: 11956, sign: false });
+    data.append(FP16x16 { mag: 53981, sign: true });
+    data.append(FP16x16 { mag: 48734, sign: false });
+    data.append(FP16x16 { mag: 81861, sign: true });
+    data.append(FP16x16 { mag: 91276, sign: false });
+    data.append(FP16x16 { mag: 104233, sign: false });
+    data.append(FP16x16 { mag: 52396, sign: false });
+    data.append(FP16x16 { mag: 37016, sign: false });
+    data.append(FP16x16 { mag: 39784, sign: false });
+    data.append(FP16x16 { mag: 16087, sign: true });
+    data.append(FP16x16 { mag: 22464, sign: true });
+    data.append(FP16x16 { mag: 98432, sign: true });
+    data.append(FP16x16 { mag: 120832, sign: true });
+    data.append(FP16x16 { mag: 25665, sign: true });
+    data.append(FP16x16 { mag: 23338, sign: true });
+    data.append(FP16x16 { mag: 18801, sign: false });
+    data.append(FP16x16 { mag: 22675, sign: false });
+    data.append(FP16x16 { mag: 79634, sign: true });
+    data.append(FP16x16 { mag: 95608, sign: true });
+    data.append(FP16x16 { mag: 73767, sign: false });
+    data.append(FP16x16 { mag: 20235, sign: false });
+    data.append(FP16x16 { mag: 19535, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-1/input_1.cairo b/tests/nodes/layer_normalization_4d_axis-1/input_1.cairo
new file mode 100644
index 000000000..5796909a7
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-1/input_1.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 4256, sign: true });
+    data.append(FP16x16 { mag: 12347, sign: true });
+    data.append(FP16x16 { mag: 12223, sign: true });
+    data.append(FP16x16 { mag: 35664, sign: false });
+    data.append(FP16x16 { mag: 47729, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-1/input_2.cairo b/tests/nodes/layer_normalization_4d_axis-1/input_2.cairo
new file mode 100644
index 000000000..5b36613cf
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-1/input_2.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 31681, sign: true });
+    data.append(FP16x16 { mag: 39712, sign: false });
+    data.append(FP16x16 { mag: 111813, sign: true });
+    data.append(FP16x16 { mag: 73292, sign: false });
+    data.append(FP16x16 { mag: 69974, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-1/output_0.cairo b/tests/nodes/layer_normalization_4d_axis-1/output_0.cairo
new file mode 100644
index 000000000..b16b3e34c
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-1/output_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 27222, sign: true });
+    data.append(FP16x16 { mag: 29264, sign: false });
+    data.append(FP16x16 { mag: 127142, sign: true });
+    data.append(FP16x16 { mag: 28560, sign: false });
+    data.append(FP16x16 { mag: 60351, sign: true });
+    data.append(FP16x16 { mag: 37788, sign: true });
+    data.append(FP16x16 { mag: 54370, sign: false });
+    data.append(FP16x16 { mag: 121438, sign: true });
+    data.append(FP16x16 { mag: 39380, sign: false });
+    data.append(FP16x16 { mag: 74002, sign: true });
+    data.append(FP16x16 { mag: 29712, sign: true });
+    data.append(FP16x16 { mag: 19392, sign: false });
+    data.append(FP16x16 { mag: 116026, sign: true });
+    data.append(FP16x16 { mag: 23611, sign: false });
+    data.append(FP16x16 { mag: 76413, sign: true });
+    data.append(FP16x16 { mag: 38774, sign: true });
+    data.append(FP16x16 { mag: 39995, sign: false });
+    data.append(FP16x16 { mag: 103821, sign: true });
+    data.append(FP16x16 { mag: 26838, sign: false });
+    data.append(FP16x16 { mag: 55050, sign: true });
+    data.append(FP16x16 { mag: 26907, sign: true });
+    data.append(FP16x16 { mag: 21300, sign: false });
+    data.append(FP16x16 { mag: 116914, sign: true });
+    data.append(FP16x16 { mag: 33608, sign: false });
+    data.append(FP16x16 { mag: 54427, sign: true });
+    data.append(FP16x16 { mag: 33093, sign: true });
+    data.append(FP16x16 { mag: 36271, sign: false });
+    data.append(FP16x16 { mag: 128658, sign: true });
+    data.append(FP16x16 { mag: 13438, sign: false });
+    data.append(FP16x16 { mag: 84790, sign: true });
+    data.append(FP16x16 { mag: 36528, sign: true });
+    data.append(FP16x16 { mag: 61679, sign: false });
+    data.append(FP16x16 { mag: 120628, sign: true });
+    data.append(FP16x16 { mag: 68843, sign: false });
+    data.append(FP16x16 { mag: 67883, sign: true });
+    data.append(FP16x16 { mag: 37338, sign: true });
+    data.append(FP16x16 { mag: 27445, sign: false });
+    data.append(FP16x16 { mag: 108887, sign: true });
+    data.append(FP16x16 { mag: 39593, sign: false });
+    data.append(FP16x16 { mag: 124310, sign: true });
+    data.append(FP16x16 { mag: 36554, sign: true });
+    data.append(FP16x16 { mag: 45301, sign: false });
+    data.append(FP16x16 { mag: 90795, sign: true });
+    data.append(FP16x16 { mag: 91286, sign: false });
+    data.append(FP16x16 { mag: 45037, sign: true });
+    data.append(FP16x16 { mag: 36959, sign: true });
+    data.append(FP16x16 { mag: 31204, sign: false });
+    data.append(FP16x16 { mag: 111906, sign: true });
+    data.append(FP16x16 { mag: 65334, sign: false });
+    data.append(FP16x16 { mag: 151771, sign: true });
+    data.append(FP16x16 { mag: 28963, sign: true });
+    data.append(FP16x16 { mag: 53952, sign: false });
+    data.append(FP16x16 { mag: 104513, sign: true });
+    data.append(FP16x16 { mag: 121643, sign: false });
+    data.append(FP16x16 { mag: 20658, sign: true });
+    data.append(FP16x16 { mag: 28787, sign: true });
+    data.append(FP16x16 { mag: 46278, sign: false });
+    data.append(FP16x16 { mag: 135335, sign: true });
+    data.append(FP16x16 { mag: 46819, sign: false });
+    data.append(FP16x16 { mag: 68555, sign: true });
+    data.append(FP16x16 { mag: 35143, sign: true });
+    data.append(FP16x16 { mag: 42180, sign: false });
+    data.append(FP16x16 { mag: 102903, sign: true });
+    data.append(FP16x16 { mag: 124252, sign: false });
+    data.append(FP16x16 { mag: 132667, sign: true });
+    data.append(FP16x16 { mag: 37173, sign: true });
+    data.append(FP16x16 { mag: 38662, sign: false });
+    data.append(FP16x16 { mag: 89894, sign: true });
+    data.append(FP16x16 { mag: 76738, sign: false });
+    data.append(FP16x16 { mag: 54649, sign: true });
+    data.append(FP16x16 { mag: 31336, sign: true });
+    data.append(FP16x16 { mag: 18722, sign: false });
+    data.append(FP16x16 { mag: 94412, sign: true });
+    data.append(FP16x16 { mag: 75879, sign: false });
+    data.append(FP16x16 { mag: 82768, sign: true });
+    data.append(FP16x16 { mag: 28065, sign: true });
+    data.append(FP16x16 { mag: 33448, sign: false });
+    data.append(FP16x16 { mag: 127469, sign: true });
+    data.append(FP16x16 { mag: 21429, sign: false });
+    data.append(FP16x16 { mag: 45363, sign: true });
+    data.append(FP16x16 { mag: 37692, sign: true });
+    data.append(FP16x16 { mag: 51120, sign: false });
+    data.append(FP16x16 { mag: 98514, sign: true });
+    data.append(FP16x16 { mag: 106370, sign: false });
+    data.append(FP16x16 { mag: 85630, sign: true });
+    data.append(FP16x16 { mag: 24340, sign: true });
+    data.append(FP16x16 { mag: 37405, sign: false });
+    data.append(FP16x16 { mag: 128694, sign: true });
+    data.append(FP16x16 { mag: 69394, sign: false });
+    data.append(FP16x16 { mag: 57263, sign: true });
+    data.append(FP16x16 { mag: 38335, sign: true });
+    data.append(FP16x16 { mag: 44227, sign: false });
+    data.append(FP16x16 { mag: 98068, sign: true });
+    data.append(FP16x16 { mag: 44884, sign: false });
+    data.append(FP16x16 { mag: 35454, sign: true });
+    data.append(FP16x16 { mag: 28683, sign: true });
+    data.append(FP16x16 { mag: 52423, sign: false });
+    data.append(FP16x16 { mag: 110980, sign: true });
+    data.append(FP16x16 { mag: 139406, sign: false });
+    data.append(FP16x16 { mag: 72447, sign: true });
+    data.append(FP16x16 { mag: 32053, sign: true });
+    data.append(FP16x16 { mag: 63056, sign: false });
+    data.append(FP16x16 { mag: 120760, sign: true });
+    data.append(FP16x16 { mag: 106395, sign: false });
+    data.append(FP16x16 { mag: 63149, sign: true });
+    data.append(FP16x16 { mag: 35826, sign: true });
+    data.append(FP16x16 { mag: 27007, sign: false });
+    data.append(FP16x16 { mag: 110829, sign: true });
+    data.append(FP16x16 { mag: 65907, sign: false });
+    data.append(FP16x16 { mag: 151855, sign: true });
+    data.append(FP16x16 { mag: 23850, sign: true });
+    data.append(FP16x16 { mag: 39710, sign: false });
+    data.append(FP16x16 { mag: 112364, sign: true });
+    data.append(FP16x16 { mag: 103957, sign: false });
+    data.append(FP16x16 { mag: 25360, sign: true });
+    data.append(FP16x16 { mag: 27646, sign: true });
+    data.append(FP16x16 { mag: 54560, sign: false });
+    data.append(FP16x16 { mag: 130110, sign: true });
+    data.append(FP16x16 { mag: 96250, sign: false });
+    data.append(FP16x16 { mag: 69503, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-2/input_0.cairo b/tests/nodes/layer_normalization_4d_axis-2/input_0.cairo
new file mode 100644
index 000000000..796ab8161
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-2/input_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 50439, sign: true });
+    data.append(FP16x16 { mag: 80584, sign: false });
+    data.append(FP16x16 { mag: 108804, sign: false });
+    data.append(FP16x16 { mag: 64736, sign: true });
+    data.append(FP16x16 { mag: 35989, sign: false });
+    data.append(FP16x16 { mag: 83918, sign: false });
+    data.append(FP16x16 { mag: 80462, sign: true });
+    data.append(FP16x16 { mag: 43318, sign: false });
+    data.append(FP16x16 { mag: 65651, sign: true });
+    data.append(FP16x16 { mag: 11331, sign: true });
+    data.append(FP16x16 { mag: 25254, sign: true });
+    data.append(FP16x16 { mag: 62677, sign: false });
+    data.append(FP16x16 { mag: 8412, sign: false });
+    data.append(FP16x16 { mag: 64067, sign: true });
+    data.append(FP16x16 { mag: 11592, sign: true });
+    data.append(FP16x16 { mag: 10557, sign: false });
+    data.append(FP16x16 { mag: 33540, sign: true });
+    data.append(FP16x16 { mag: 50007, sign: true });
+    data.append(FP16x16 { mag: 66942, sign: true });
+    data.append(FP16x16 { mag: 24781, sign: true });
+    data.append(FP16x16 { mag: 70618, sign: true });
+    data.append(FP16x16 { mag: 91944, sign: false });
+    data.append(FP16x16 { mag: 25130, sign: false });
+    data.append(FP16x16 { mag: 70069, sign: true });
+    data.append(FP16x16 { mag: 19429, sign: false });
+    data.append(FP16x16 { mag: 17607, sign: true });
+    data.append(FP16x16 { mag: 20059, sign: true });
+    data.append(FP16x16 { mag: 30637, sign: false });
+    data.append(FP16x16 { mag: 110305, sign: true });
+    data.append(FP16x16 { mag: 47227, sign: true });
+    data.append(FP16x16 { mag: 52701, sign: false });
+    data.append(FP16x16 { mag: 113586, sign: true });
+    data.append(FP16x16 { mag: 28893, sign: false });
+    data.append(FP16x16 { mag: 19309, sign: true });
+    data.append(FP16x16 { mag: 9704, sign: true });
+    data.append(FP16x16 { mag: 56793, sign: false });
+    data.append(FP16x16 { mag: 44072, sign: false });
+    data.append(FP16x16 { mag: 2660, sign: true });
+    data.append(FP16x16 { mag: 29401, sign: true });
+    data.append(FP16x16 { mag: 36736, sign: true });
+    data.append(FP16x16 { mag: 86250, sign: false });
+    data.append(FP16x16 { mag: 38971, sign: true });
+    data.append(FP16x16 { mag: 138249, sign: true });
+    data.append(FP16x16 { mag: 36048, sign: false });
+    data.append(FP16x16 { mag: 37452, sign: false });
+    data.append(FP16x16 { mag: 23181, sign: true });
+    data.append(FP16x16 { mag: 35955, sign: true });
+    data.append(FP16x16 { mag: 51748, sign: true });
+    data.append(FP16x16 { mag: 57097, sign: true });
+    data.append(FP16x16 { mag: 91645, sign: true });
+    data.append(FP16x16 { mag: 727, sign: true });
+    data.append(FP16x16 { mag: 26384, sign: true });
+    data.append(FP16x16 { mag: 1331, sign: false });
+    data.append(FP16x16 { mag: 98672, sign: false });
+    data.append(FP16x16 { mag: 82596, sign: false });
+    data.append(FP16x16 { mag: 100984, sign: true });
+    data.append(FP16x16 { mag: 88934, sign: true });
+    data.append(FP16x16 { mag: 110736, sign: false });
+    data.append(FP16x16 { mag: 106050, sign: true });
+    data.append(FP16x16 { mag: 43286, sign: true });
+    data.append(FP16x16 { mag: 114427, sign: false });
+    data.append(FP16x16 { mag: 26160, sign: false });
+    data.append(FP16x16 { mag: 19914, sign: true });
+    data.append(FP16x16 { mag: 168031, sign: false });
+    data.append(FP16x16 { mag: 70834, sign: true });
+    data.append(FP16x16 { mag: 62785, sign: false });
+    data.append(FP16x16 { mag: 20711, sign: true });
+    data.append(FP16x16 { mag: 150814, sign: true });
+    data.append(FP16x16 { mag: 19909, sign: true });
+    data.append(FP16x16 { mag: 4360, sign: true });
+    data.append(FP16x16 { mag: 38550, sign: false });
+    data.append(FP16x16 { mag: 155210, sign: false });
+    data.append(FP16x16 { mag: 49387, sign: true });
+    data.append(FP16x16 { mag: 48606, sign: false });
+    data.append(FP16x16 { mag: 26297, sign: false });
+    data.append(FP16x16 { mag: 5832, sign: true });
+    data.append(FP16x16 { mag: 67019, sign: false });
+    data.append(FP16x16 { mag: 108552, sign: false });
+    data.append(FP16x16 { mag: 38302, sign: true });
+    data.append(FP16x16 { mag: 67467, sign: false });
+    data.append(FP16x16 { mag: 123524, sign: false });
+    data.append(FP16x16 { mag: 38110, sign: true });
+    data.append(FP16x16 { mag: 49459, sign: true });
+    data.append(FP16x16 { mag: 89977, sign: false });
+    data.append(FP16x16 { mag: 3116, sign: false });
+    data.append(FP16x16 { mag: 139868, sign: true });
+    data.append(FP16x16 { mag: 3777, sign: false });
+    data.append(FP16x16 { mag: 93508, sign: false });
+    data.append(FP16x16 { mag: 18473, sign: true });
+    data.append(FP16x16 { mag: 9749, sign: false });
+    data.append(FP16x16 { mag: 69280, sign: false });
+    data.append(FP16x16 { mag: 31261, sign: true });
+    data.append(FP16x16 { mag: 70810, sign: true });
+    data.append(FP16x16 { mag: 53719, sign: true });
+    data.append(FP16x16 { mag: 25490, sign: false });
+    data.append(FP16x16 { mag: 76561, sign: true });
+    data.append(FP16x16 { mag: 87810, sign: true });
+    data.append(FP16x16 { mag: 54546, sign: true });
+    data.append(FP16x16 { mag: 11956, sign: false });
+    data.append(FP16x16 { mag: 53981, sign: true });
+    data.append(FP16x16 { mag: 48734, sign: false });
+    data.append(FP16x16 { mag: 81861, sign: true });
+    data.append(FP16x16 { mag: 91276, sign: false });
+    data.append(FP16x16 { mag: 104233, sign: false });
+    data.append(FP16x16 { mag: 52396, sign: false });
+    data.append(FP16x16 { mag: 37016, sign: false });
+    data.append(FP16x16 { mag: 39784, sign: false });
+    data.append(FP16x16 { mag: 16087, sign: true });
+    data.append(FP16x16 { mag: 22464, sign: true });
+    data.append(FP16x16 { mag: 98432, sign: true });
+    data.append(FP16x16 { mag: 120832, sign: true });
+    data.append(FP16x16 { mag: 25665, sign: true });
+    data.append(FP16x16 { mag: 23338, sign: true });
+    data.append(FP16x16 { mag: 18801, sign: false });
+    data.append(FP16x16 { mag: 22675, sign: false });
+    data.append(FP16x16 { mag: 79634, sign: true });
+    data.append(FP16x16 { mag: 95608, sign: true });
+    data.append(FP16x16 { mag: 73767, sign: false });
+    data.append(FP16x16 { mag: 20235, sign: false });
+    data.append(FP16x16 { mag: 19535, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-2/input_1.cairo b/tests/nodes/layer_normalization_4d_axis-2/input_1.cairo
new file mode 100644
index 000000000..88404328d
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-2/input_1.cairo
@@ -0,0 +1,33 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 12195, sign: false });
+    data.append(FP16x16 { mag: 25593, sign: false });
+    data.append(FP16x16 { mag: 14094, sign: true });
+    data.append(FP16x16 { mag: 99626, sign: true });
+    data.append(FP16x16 { mag: 48676, sign: true });
+    data.append(FP16x16 { mag: 58459, sign: false });
+    data.append(FP16x16 { mag: 96699, sign: true });
+    data.append(FP16x16 { mag: 14935, sign: true });
+    data.append(FP16x16 { mag: 2362, sign: true });
+    data.append(FP16x16 { mag: 150235, sign: false });
+    data.append(FP16x16 { mag: 65730, sign: true });
+    data.append(FP16x16 { mag: 56267, sign: false });
+    data.append(FP16x16 { mag: 83617, sign: true });
+    data.append(FP16x16 { mag: 34940, sign: false });
+    data.append(FP16x16 { mag: 14826, sign: false });
+    data.append(FP16x16 { mag: 67759, sign: true });
+    data.append(FP16x16 { mag: 88099, sign: true });
+    data.append(FP16x16 { mag: 103290, sign: true });
+    data.append(FP16x16 { mag: 50684, sign: true });
+    data.append(FP16x16 { mag: 29161, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-2/input_2.cairo b/tests/nodes/layer_normalization_4d_axis-2/input_2.cairo
new file mode 100644
index 000000000..d742874f7
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-2/input_2.cairo
@@ -0,0 +1,33 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 44676, sign: false });
+    data.append(FP16x16 { mag: 53361, sign: true });
+    data.append(FP16x16 { mag: 33378, sign: true });
+    data.append(FP16x16 { mag: 43061, sign: false });
+    data.append(FP16x16 { mag: 24801, sign: false });
+    data.append(FP16x16 { mag: 33406, sign: false });
+    data.append(FP16x16 { mag: 54529, sign: true });
+    data.append(FP16x16 { mag: 133687, sign: false });
+    data.append(FP16x16 { mag: 44032, sign: true });
+    data.append(FP16x16 { mag: 38747, sign: true });
+    data.append(FP16x16 { mag: 34054, sign: true });
+    data.append(FP16x16 { mag: 45436, sign: false });
+    data.append(FP16x16 { mag: 80815, sign: false });
+    data.append(FP16x16 { mag: 79372, sign: true });
+    data.append(FP16x16 { mag: 17958, sign: true });
+    data.append(FP16x16 { mag: 49483, sign: false });
+    data.append(FP16x16 { mag: 46695, sign: true });
+    data.append(FP16x16 { mag: 1816, sign: true });
+    data.append(FP16x16 { mag: 43264, sign: true });
+    data.append(FP16x16 { mag: 59187, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-2/output_0.cairo b/tests/nodes/layer_normalization_4d_axis-2/output_0.cairo
new file mode 100644
index 000000000..330008c16
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-2/output_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 34980, sign: false });
+    data.append(FP16x16 { mag: 14079, sign: true });
+    data.append(FP16x16 { mag: 62083, sign: true });
+    data.append(FP16x16 { mag: 147603, sign: false });
+    data.append(FP16x16 { mag: 11308, sign: true });
+    data.append(FP16x16 { mag: 126598, sign: false });
+    data.append(FP16x16 { mag: 73983, sign: false });
+    data.append(FP16x16 { mag: 120660, sign: false });
+    data.append(FP16x16 { mag: 41515, sign: true });
+    data.append(FP16x16 { mag: 53720, sign: true });
+    data.append(FP16x16 { mag: 11229, sign: true });
+    data.append(FP16x16 { mag: 113880, sign: false });
+    data.append(FP16x16 { mag: 59791, sign: false });
+    data.append(FP16x16 { mag: 115620, sign: true });
+    data.append(FP16x16 { mag: 19505, sign: true });
+    data.append(FP16x16 { mag: 29861, sign: false });
+    data.append(FP16x16 { mag: 3122, sign: true });
+    data.append(FP16x16 { mag: 79516, sign: false });
+    data.append(FP16x16 { mag: 11908, sign: false });
+    data.append(FP16x16 { mag: 49306, sign: true });
+    data.append(FP16x16 { mag: 30901, sign: false });
+    data.append(FP16x16 { mag: 4892, sign: true });
+    data.append(FP16x16 { mag: 42556, sign: true });
+    data.append(FP16x16 { mag: 154572, sign: false });
+    data.append(FP16x16 { mag: 1734, sign: true });
+    data.append(FP16x16 { mag: 25009, sign: false });
+    data.append(FP16x16 { mag: 36229, sign: true });
+    data.append(FP16x16 { mag: 122431, sign: false });
+    data.append(FP16x16 { mag: 39621, sign: true });
+    data.append(FP16x16 { mag: 143085, sign: true });
+    data.append(FP16x16 { mag: 110561, sign: true });
+    data.append(FP16x16 { mag: 63080, sign: true });
+    data.append(FP16x16 { mag: 20513, sign: false });
+    data.append(FP16x16 { mag: 85496, sign: true });
+    data.append(FP16x16 { mag: 17909, sign: true });
+    data.append(FP16x16 { mag: 34541, sign: true });
+    data.append(FP16x16 { mag: 135099, sign: true });
+    data.append(FP16x16 { mag: 15693, sign: true });
+    data.append(FP16x16 { mag: 24867, sign: true });
+    data.append(FP16x16 { mag: 44624, sign: true });
+    data.append(FP16x16 { mag: 62432, sign: false });
+    data.append(FP16x16 { mag: 61070, sign: true });
+    data.append(FP16x16 { mag: 9497, sign: true });
+    data.append(FP16x16 { mag: 31812, sign: true });
+    data.append(FP16x16 { mag: 12740, sign: true });
+    data.append(FP16x16 { mag: 28750, sign: false });
+    data.append(FP16x16 { mag: 29494, sign: true });
+    data.append(FP16x16 { mag: 140863, sign: false });
+    data.append(FP16x16 { mag: 42720, sign: true });
+    data.append(FP16x16 { mag: 195054, sign: true });
+    data.append(FP16x16 { mag: 49531, sign: true });
+    data.append(FP16x16 { mag: 38426, sign: false });
+    data.append(FP16x16 { mag: 58711, sign: false });
+    data.append(FP16x16 { mag: 22407, sign: true });
+    data.append(FP16x16 { mag: 2868, sign: false });
+    data.append(FP16x16 { mag: 128861, sign: false });
+    data.append(FP16x16 { mag: 41612, sign: false });
+    data.append(FP16x16 { mag: 187704, sign: true });
+    data.append(FP16x16 { mag: 19714, sign: false });
+    data.append(FP16x16 { mag: 48637, sign: true });
+    data.append(FP16x16 { mag: 58990, sign: false });
+    data.append(FP16x16 { mag: 53022, sign: true });
+    data.append(FP16x16 { mag: 25027, sign: true });
+    data.append(FP16x16 { mag: 144085, sign: true });
+    data.append(FP16x16 { mag: 86230, sign: false });
+    data.append(FP16x16 { mag: 62331, sign: false });
+    data.append(FP16x16 { mag: 3779, sign: false });
+    data.append(FP16x16 { mag: 168240, sign: false });
+    data.append(FP16x16 { mag: 42633, sign: true });
+    data.append(FP16x16 { mag: 97041, sign: true });
+    data.append(FP16x16 { mag: 45633, sign: true });
+    data.append(FP16x16 { mag: 141649, sign: false });
+    data.append(FP16x16 { mag: 162760, sign: false });
+    data.append(FP16x16 { mag: 68597, sign: true });
+    data.append(FP16x16 { mag: 17735, sign: true });
+    data.append(FP16x16 { mag: 77086, sign: false });
+    data.append(FP16x16 { mag: 95189, sign: true });
+    data.append(FP16x16 { mag: 115074, sign: true });
+    data.append(FP16x16 { mag: 980, sign: true });
+    data.append(FP16x16 { mag: 75411, sign: true });
+    data.append(FP16x16 { mag: 69778, sign: false });
+    data.append(FP16x16 { mag: 63413, sign: true });
+    data.append(FP16x16 { mag: 25417, sign: true });
+    data.append(FP16x16 { mag: 111323, sign: true });
+    data.append(FP16x16 { mag: 13487, sign: false });
+    data.append(FP16x16 { mag: 79762, sign: true });
+    data.append(FP16x16 { mag: 77975, sign: true });
+    data.append(FP16x16 { mag: 109742, sign: false });
+    data.append(FP16x16 { mag: 43808, sign: true });
+    data.append(FP16x16 { mag: 11284, sign: false });
+    data.append(FP16x16 { mag: 115283, sign: true });
+    data.append(FP16x16 { mag: 29181, sign: false });
+    data.append(FP16x16 { mag: 155120, sign: false });
+    data.append(FP16x16 { mag: 101365, sign: true });
+    data.append(FP16x16 { mag: 9482, sign: true });
+    data.append(FP16x16 { mag: 115606, sign: false });
+    data.append(FP16x16 { mag: 54304, sign: false });
+    data.append(FP16x16 { mag: 64495, sign: false });
+    data.append(FP16x16 { mag: 61839, sign: true });
+    data.append(FP16x16 { mag: 40715, sign: true });
+    data.append(FP16x16 { mag: 54635, sign: false });
+    data.append(FP16x16 { mag: 84491, sign: true });
+    data.append(FP16x16 { mag: 54222, sign: true });
+    data.append(FP16x16 { mag: 124370, sign: true });
+    data.append(FP16x16 { mag: 17725, sign: true });
+    data.append(FP16x16 { mag: 70484, sign: false });
+    data.append(FP16x16 { mag: 120027, sign: true });
+    data.append(FP16x16 { mag: 136560, sign: false });
+    data.append(FP16x16 { mag: 43343, sign: true });
+    data.append(FP16x16 { mag: 260233, sign: true });
+    data.append(FP16x16 { mag: 85770, sign: false });
+    data.append(FP16x16 { mag: 26220, sign: false });
+    data.append(FP16x16 { mag: 106343, sign: false });
+    data.append(FP16x16 { mag: 67118, sign: true });
+    data.append(FP16x16 { mag: 11865, sign: true });
+    data.append(FP16x16 { mag: 129550, sign: false });
+    data.append(FP16x16 { mag: 79314, sign: false });
+    data.append(FP16x16 { mag: 126419, sign: true });
+    data.append(FP16x16 { mag: 62170, sign: true });
+    data.append(FP16x16 { mag: 52010, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-3/input_0.cairo b/tests/nodes/layer_normalization_4d_axis-3/input_0.cairo
new file mode 100644
index 000000000..796ab8161
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-3/input_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 50439, sign: true });
+    data.append(FP16x16 { mag: 80584, sign: false });
+    data.append(FP16x16 { mag: 108804, sign: false });
+    data.append(FP16x16 { mag: 64736, sign: true });
+    data.append(FP16x16 { mag: 35989, sign: false });
+    data.append(FP16x16 { mag: 83918, sign: false });
+    data.append(FP16x16 { mag: 80462, sign: true });
+    data.append(FP16x16 { mag: 43318, sign: false });
+    data.append(FP16x16 { mag: 65651, sign: true });
+    data.append(FP16x16 { mag: 11331, sign: true });
+    data.append(FP16x16 { mag: 25254, sign: true });
+    data.append(FP16x16 { mag: 62677, sign: false });
+    data.append(FP16x16 { mag: 8412, sign: false });
+    data.append(FP16x16 { mag: 64067, sign: true });
+    data.append(FP16x16 { mag: 11592, sign: true });
+    data.append(FP16x16 { mag: 10557, sign: false });
+    data.append(FP16x16 { mag: 33540, sign: true });
+    data.append(FP16x16 { mag: 50007, sign: true });
+    data.append(FP16x16 { mag: 66942, sign: true });
+    data.append(FP16x16 { mag: 24781, sign: true });
+    data.append(FP16x16 { mag: 70618, sign: true });
+    data.append(FP16x16 { mag: 91944, sign: false });
+    data.append(FP16x16 { mag: 25130, sign: false });
+    data.append(FP16x16 { mag: 70069, sign: true });
+    data.append(FP16x16 { mag: 19429, sign: false });
+    data.append(FP16x16 { mag: 17607, sign: true });
+    data.append(FP16x16 { mag: 20059, sign: true });
+    data.append(FP16x16 { mag: 30637, sign: false });
+    data.append(FP16x16 { mag: 110305, sign: true });
+    data.append(FP16x16 { mag: 47227, sign: true });
+    data.append(FP16x16 { mag: 52701, sign: false });
+    data.append(FP16x16 { mag: 113586, sign: true });
+    data.append(FP16x16 { mag: 28893, sign: false });
+    data.append(FP16x16 { mag: 19309, sign: true });
+    data.append(FP16x16 { mag: 9704, sign: true });
+    data.append(FP16x16 { mag: 56793, sign: false });
+    data.append(FP16x16 { mag: 44072, sign: false });
+    data.append(FP16x16 { mag: 2660, sign: true });
+    data.append(FP16x16 { mag: 29401, sign: true });
+    data.append(FP16x16 { mag: 36736, sign: true });
+    data.append(FP16x16 { mag: 86250, sign: false });
+    data.append(FP16x16 { mag: 38971, sign: true });
+    data.append(FP16x16 { mag: 138249, sign: true });
+    data.append(FP16x16 { mag: 36048, sign: false });
+    data.append(FP16x16 { mag: 37452, sign: false });
+    data.append(FP16x16 { mag: 23181, sign: true });
+    data.append(FP16x16 { mag: 35955, sign: true });
+    data.append(FP16x16 { mag: 51748, sign: true });
+    data.append(FP16x16 { mag: 57097, sign: true });
+    data.append(FP16x16 { mag: 91645, sign: true });
+    data.append(FP16x16 { mag: 727, sign: true });
+    data.append(FP16x16 { mag: 26384, sign: true });
+    data.append(FP16x16 { mag: 1331, sign: false });
+    data.append(FP16x16 { mag: 98672, sign: false });
+    data.append(FP16x16 { mag: 82596, sign: false });
+    data.append(FP16x16 { mag: 100984, sign: true });
+    data.append(FP16x16 { mag: 88934, sign: true });
+    data.append(FP16x16 { mag: 110736, sign: false });
+    data.append(FP16x16 { mag: 106050, sign: true });
+    data.append(FP16x16 { mag: 43286, sign: true });
+    data.append(FP16x16 { mag: 114427, sign: false });
+    data.append(FP16x16 { mag: 26160, sign: false });
+    data.append(FP16x16 { mag: 19914, sign: true });
+    data.append(FP16x16 { mag: 168031, sign: false });
+    data.append(FP16x16 { mag: 70834, sign: true });
+    data.append(FP16x16 { mag: 62785, sign: false });
+    data.append(FP16x16 { mag: 20711, sign: true });
+    data.append(FP16x16 { mag: 150814, sign: true });
+    data.append(FP16x16 { mag: 19909, sign: true });
+    data.append(FP16x16 { mag: 4360, sign: true });
+    data.append(FP16x16 { mag: 38550, sign: false });
+    data.append(FP16x16 { mag: 155210, sign: false });
+    data.append(FP16x16 { mag: 49387, sign: true });
+    data.append(FP16x16 { mag: 48606, sign: false });
+    data.append(FP16x16 { mag: 26297, sign: false });
+    data.append(FP16x16 { mag: 5832, sign: true });
+    data.append(FP16x16 { mag: 67019, sign: false });
+    data.append(FP16x16 { mag: 108552, sign: false });
+    data.append(FP16x16 { mag: 38302, sign: true });
+    data.append(FP16x16 { mag: 67467, sign: false });
+    data.append(FP16x16 { mag: 123524, sign: false });
+    data.append(FP16x16 { mag: 38110, sign: true });
+    data.append(FP16x16 { mag: 49459, sign: true });
+    data.append(FP16x16 { mag: 89977, sign: false });
+    data.append(FP16x16 { mag: 3116, sign: false });
+    data.append(FP16x16 { mag: 139868, sign: true });
+    data.append(FP16x16 { mag: 3777, sign: false });
+    data.append(FP16x16 { mag: 93508, sign: false });
+    data.append(FP16x16 { mag: 18473, sign: true });
+    data.append(FP16x16 { mag: 9749, sign: false });
+    data.append(FP16x16 { mag: 69280, sign: false });
+    data.append(FP16x16 { mag: 31261, sign: true });
+    data.append(FP16x16 { mag: 70810, sign: true });
+    data.append(FP16x16 { mag: 53719, sign: true });
+    data.append(FP16x16 { mag: 25490, sign: false });
+    data.append(FP16x16 { mag: 76561, sign: true });
+    data.append(FP16x16 { mag: 87810, sign: true });
+    data.append(FP16x16 { mag: 54546, sign: true });
+    data.append(FP16x16 { mag: 11956, sign: false });
+    data.append(FP16x16 { mag: 53981, sign: true });
+    data.append(FP16x16 { mag: 48734, sign: false });
+    data.append(FP16x16 { mag: 81861, sign: true });
+    data.append(FP16x16 { mag: 91276, sign: false });
+    data.append(FP16x16 { mag: 104233, sign: false });
+    data.append(FP16x16 { mag: 52396, sign: false });
+    data.append(FP16x16 { mag: 37016, sign: false });
+    data.append(FP16x16 { mag: 39784, sign: false });
+    data.append(FP16x16 { mag: 16087, sign: true });
+    data.append(FP16x16 { mag: 22464, sign: true });
+    data.append(FP16x16 { mag: 98432, sign: true });
+    data.append(FP16x16 { mag: 120832, sign: true });
+    data.append(FP16x16 { mag: 25665, sign: true });
+    data.append(FP16x16 { mag: 23338, sign: true });
+    data.append(FP16x16 { mag: 18801, sign: false });
+    data.append(FP16x16 { mag: 22675, sign: false });
+    data.append(FP16x16 { mag: 79634, sign: true });
+    data.append(FP16x16 { mag: 95608, sign: true });
+    data.append(FP16x16 { mag: 73767, sign: false });
+    data.append(FP16x16 { mag: 20235, sign: false });
+    data.append(FP16x16 { mag: 19535, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-3/input_1.cairo b/tests/nodes/layer_normalization_4d_axis-3/input_1.cairo
new file mode 100644
index 000000000..f0db6e787
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-3/input_1.cairo
@@ -0,0 +1,74 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 72546, sign: true });
+    data.append(FP16x16 { mag: 1934, sign: true });
+    data.append(FP16x16 { mag: 26602, sign: false });
+    data.append(FP16x16 { mag: 64666, sign: false });
+    data.append(FP16x16 { mag: 113155, sign: false });
+    data.append(FP16x16 { mag: 140657, sign: true });
+    data.append(FP16x16 { mag: 8029, sign: false });
+    data.append(FP16x16 { mag: 1972, sign: true });
+    data.append(FP16x16 { mag: 13859, sign: true });
+    data.append(FP16x16 { mag: 30242, sign: true });
+    data.append(FP16x16 { mag: 5299, sign: false });
+    data.append(FP16x16 { mag: 164784, sign: false });
+    data.append(FP16x16 { mag: 46610, sign: false });
+    data.append(FP16x16 { mag: 8040, sign: true });
+    data.append(FP16x16 { mag: 2005, sign: false });
+    data.append(FP16x16 { mag: 43621, sign: true });
+    data.append(FP16x16 { mag: 23042, sign: false });
+    data.append(FP16x16 { mag: 12823, sign: true });
+    data.append(FP16x16 { mag: 93996, sign: true });
+    data.append(FP16x16 { mag: 32415, sign: true });
+    data.append(FP16x16 { mag: 78956, sign: true });
+    data.append(FP16x16 { mag: 74322, sign: true });
+    data.append(FP16x16 { mag: 3724, sign: true });
+    data.append(FP16x16 { mag: 5105, sign: true });
+    data.append(FP16x16 { mag: 54473, sign: false });
+    data.append(FP16x16 { mag: 38101, sign: false });
+    data.append(FP16x16 { mag: 12378, sign: false });
+    data.append(FP16x16 { mag: 36960, sign: true });
+    data.append(FP16x16 { mag: 87265, sign: true });
+    data.append(FP16x16 { mag: 34339, sign: true });
+    data.append(FP16x16 { mag: 109979, sign: false });
+    data.append(FP16x16 { mag: 138917, sign: true });
+    data.append(FP16x16 { mag: 93741, sign: false });
+    data.append(FP16x16 { mag: 139243, sign: false });
+    data.append(FP16x16 { mag: 42853, sign: true });
+    data.append(FP16x16 { mag: 33084, sign: false });
+    data.append(FP16x16 { mag: 23897, sign: true });
+    data.append(FP16x16 { mag: 52766, sign: true });
+    data.append(FP16x16 { mag: 38574, sign: true });
+    data.append(FP16x16 { mag: 10485, sign: true });
+    data.append(FP16x16 { mag: 60984, sign: true });
+    data.append(FP16x16 { mag: 20096, sign: false });
+    data.append(FP16x16 { mag: 24418, sign: false });
+    data.append(FP16x16 { mag: 29015, sign: false });
+    data.append(FP16x16 { mag: 49773, sign: false });
+    data.append(FP16x16 { mag: 104302, sign: false });
+    data.append(FP16x16 { mag: 39141, sign: false });
+    data.append(FP16x16 { mag: 23473, sign: true });
+    data.append(FP16x16 { mag: 33895, sign: false });
+    data.append(FP16x16 { mag: 12032, sign: true });
+    data.append(FP16x16 { mag: 60207, sign: false });
+    data.append(FP16x16 { mag: 64204, sign: false });
+    data.append(FP16x16 { mag: 34732, sign: true });
+    data.append(FP16x16 { mag: 31629, sign: false });
+    data.append(FP16x16 { mag: 3538, sign: true });
+    data.append(FP16x16 { mag: 13382, sign: false });
+    data.append(FP16x16 { mag: 29195, sign: true });
+    data.append(FP16x16 { mag: 5944, sign: true });
+    data.append(FP16x16 { mag: 103840, sign: true });
+    data.append(FP16x16 { mag: 38426, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-3/input_2.cairo b/tests/nodes/layer_normalization_4d_axis-3/input_2.cairo
new file mode 100644
index 000000000..bc434b717
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-3/input_2.cairo
@@ -0,0 +1,74 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 140567, sign: false });
+    data.append(FP16x16 { mag: 40459, sign: true });
+    data.append(FP16x16 { mag: 24644, sign: false });
+    data.append(FP16x16 { mag: 111555, sign: false });
+    data.append(FP16x16 { mag: 8570, sign: false });
+    data.append(FP16x16 { mag: 10992, sign: true });
+    data.append(FP16x16 { mag: 212035, sign: false });
+    data.append(FP16x16 { mag: 11465, sign: false });
+    data.append(FP16x16 { mag: 73223, sign: false });
+    data.append(FP16x16 { mag: 105723, sign: true });
+    data.append(FP16x16 { mag: 75766, sign: true });
+    data.append(FP16x16 { mag: 102598, sign: true });
+    data.append(FP16x16 { mag: 94019, sign: true });
+    data.append(FP16x16 { mag: 35770, sign: false });
+    data.append(FP16x16 { mag: 78502, sign: false });
+    data.append(FP16x16 { mag: 41664, sign: true });
+    data.append(FP16x16 { mag: 29280, sign: true });
+    data.append(FP16x16 { mag: 124649, sign: false });
+    data.append(FP16x16 { mag: 99019, sign: true });
+    data.append(FP16x16 { mag: 66970, sign: true });
+    data.append(FP16x16 { mag: 8747, sign: true });
+    data.append(FP16x16 { mag: 79443, sign: true });
+    data.append(FP16x16 { mag: 68471, sign: false });
+    data.append(FP16x16 { mag: 11126, sign: true });
+    data.append(FP16x16 { mag: 24010, sign: true });
+    data.append(FP16x16 { mag: 4088, sign: false });
+    data.append(FP16x16 { mag: 49743, sign: false });
+    data.append(FP16x16 { mag: 8246, sign: false });
+    data.append(FP16x16 { mag: 84164, sign: false });
+    data.append(FP16x16 { mag: 86259, sign: false });
+    data.append(FP16x16 { mag: 42086, sign: false });
+    data.append(FP16x16 { mag: 5197, sign: false });
+    data.append(FP16x16 { mag: 1592, sign: false });
+    data.append(FP16x16 { mag: 41261, sign: true });
+    data.append(FP16x16 { mag: 52531, sign: false });
+    data.append(FP16x16 { mag: 9495, sign: true });
+    data.append(FP16x16 { mag: 18817, sign: true });
+    data.append(FP16x16 { mag: 2626, sign: false });
+    data.append(FP16x16 { mag: 7622, sign: true });
+    data.append(FP16x16 { mag: 34140, sign: true });
+    data.append(FP16x16 { mag: 29535, sign: true });
+    data.append(FP16x16 { mag: 43504, sign: true });
+    data.append(FP16x16 { mag: 71292, sign: true });
+    data.append(FP16x16 { mag: 30928, sign: false });
+    data.append(FP16x16 { mag: 113967, sign: false });
+    data.append(FP16x16 { mag: 38791, sign: false });
+    data.append(FP16x16 { mag: 89439, sign: true });
+    data.append(FP16x16 { mag: 73665, sign: true });
+    data.append(FP16x16 { mag: 103501, sign: false });
+    data.append(FP16x16 { mag: 27579, sign: false });
+    data.append(FP16x16 { mag: 81207, sign: false });
+    data.append(FP16x16 { mag: 8128, sign: true });
+    data.append(FP16x16 { mag: 7160, sign: true });
+    data.append(FP16x16 { mag: 159276, sign: true });
+    data.append(FP16x16 { mag: 93395, sign: false });
+    data.append(FP16x16 { mag: 6090, sign: false });
+    data.append(FP16x16 { mag: 29949, sign: false });
+    data.append(FP16x16 { mag: 24359, sign: true });
+    data.append(FP16x16 { mag: 44435, sign: false });
+    data.append(FP16x16 { mag: 57014, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-3/output_0.cairo b/tests/nodes/layer_normalization_4d_axis-3/output_0.cairo
new file mode 100644
index 000000000..896cfb65f
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-3/output_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 187342, sign: false });
+    data.append(FP16x16 { mag: 43360, sign: true });
+    data.append(FP16x16 { mag: 76815, sign: false });
+    data.append(FP16x16 { mag: 54732, sign: false });
+    data.append(FP16x16 { mag: 95653, sign: false });
+    data.append(FP16x16 { mag: 229562, sign: true });
+    data.append(FP16x16 { mag: 202912, sign: false });
+    data.append(FP16x16 { mag: 9710, sign: false });
+    data.append(FP16x16 { mag: 85609, sign: false });
+    data.append(FP16x16 { mag: 105578, sign: true });
+    data.append(FP16x16 { mag: 76999, sign: true });
+    data.append(FP16x16 { mag: 96182, sign: false });
+    data.append(FP16x16 { mag: 79183, sign: true });
+    data.append(FP16x16 { mag: 42747, sign: false });
+    data.append(FP16x16 { mag: 78484, sign: false });
+    data.append(FP16x16 { mag: 57080, sign: true });
+    data.append(FP16x16 { mag: 37764, sign: true });
+    data.append(FP16x16 { mag: 132826, sign: false });
+    data.append(FP16x16 { mag: 13030, sign: true });
+    data.append(FP16x16 { mag: 59680, sign: true });
+    data.append(FP16x16 { mag: 68231, sign: false });
+    data.append(FP16x16 { mag: 204694, sign: true });
+    data.append(FP16x16 { mag: 66266, sign: false });
+    data.append(FP16x16 { mag: 6194, sign: true });
+    data.append(FP16x16 { mag: 3149, sign: false });
+    data.append(FP16x16 { mag: 6, sign: true });
+    data.append(FP16x16 { mag: 47916, sign: false });
+    data.append(FP16x16 { mag: 16960, sign: true });
+    data.append(FP16x16 { mag: 225919, sign: false });
+    data.append(FP16x16 { mag: 106594, sign: false });
+    data.append(FP16x16 { mag: 156803, sign: false });
+    data.append(FP16x16 { mag: 238316, sign: false });
+    data.append(FP16x16 { mag: 62849, sign: false });
+    data.append(FP16x16 { mag: 60107, sign: true });
+    data.append(FP16x16 { mag: 51595, sign: false });
+    data.append(FP16x16 { mag: 27228, sign: false });
+    data.append(FP16x16 { mag: 40369, sign: true });
+    data.append(FP16x16 { mag: 4608, sign: true });
+    data.append(FP16x16 { mag: 3968, sign: false });
+    data.append(FP16x16 { mag: 29731, sign: true });
+    data.append(FP16x16 { mag: 126627, sign: true });
+    data.append(FP16x16 { mag: 52689, sign: true });
+    data.append(FP16x16 { mag: 122123, sign: true });
+    data.append(FP16x16 { mag: 53286, sign: false });
+    data.append(FP16x16 { mag: 153464, sign: false });
+    data.append(FP16x16 { mag: 18067, sign: false });
+    data.append(FP16x16 { mag: 105399, sign: true });
+    data.append(FP16x16 { mag: 58027, sign: true });
+    data.append(FP16x16 { mag: 77954, sign: false });
+    data.append(FP16x16 { mag: 43451, sign: false });
+    data.append(FP16x16 { mag: 91367, sign: false });
+    data.append(FP16x16 { mag: 24251, sign: true });
+    data.append(FP16x16 { mag: 14192, sign: true });
+    data.append(FP16x16 { mag: 102491, sign: true });
+    data.append(FP16x16 { mag: 87973, sign: false });
+    data.append(FP16x16 { mag: 13606, sign: true });
+    data.append(FP16x16 { mag: 67163, sign: false });
+    data.append(FP16x16 { mag: 36206, sign: true });
+    data.append(FP16x16 { mag: 205884, sign: false });
+    data.append(FP16x16 { mag: 36735, sign: true });
+    data.append(FP16x16 { mag: 26425, sign: false });
+    data.append(FP16x16 { mag: 41090, sign: true });
+    data.append(FP16x16 { mag: 16004, sign: false });
+    data.append(FP16x16 { mag: 262275, sign: false });
+    data.append(FP16x16 { mag: 109588, sign: true });
+    data.append(FP16x16 { mag: 129666, sign: true });
+    data.append(FP16x16 { mag: 209336, sign: false });
+    data.append(FP16x16 { mag: 15754, sign: false });
+    data.append(FP16x16 { mag: 77723, sign: false });
+    data.append(FP16x16 { mag: 102547, sign: true });
+    data.append(FP16x16 { mag: 73109, sign: true });
+    data.append(FP16x16 { mag: 251621, sign: false });
+    data.append(FP16x16 { mag: 128567, sign: true });
+    data.append(FP16x16 { mag: 30597, sign: false });
+    data.append(FP16x16 { mag: 79160, sign: false });
+    data.append(FP16x16 { mag: 36176, sign: true });
+    data.append(FP16x16 { mag: 8461, sign: true });
+    data.append(FP16x16 { mag: 105536, sign: false });
+    data.append(FP16x16 { mag: 44072, sign: true });
+    data.append(FP16x16 { mag: 96463, sign: true });
+    data.append(FP16x16 { mag: 143124, sign: true });
+    data.append(FP16x16 { mag: 36197, sign: true });
+    data.append(FP16x16 { mag: 71235, sign: false });
+    data.append(FP16x16 { mag: 17395, sign: true });
+    data.append(FP16x16 { mag: 23976, sign: true });
+    data.append(FP16x16 { mag: 72861, sign: true });
+    data.append(FP16x16 { mag: 49866, sign: false });
+    data.append(FP16x16 { mag: 38981, sign: true });
+    data.append(FP16x16 { mag: 110729, sign: false });
+    data.append(FP16x16 { mag: 83018, sign: false });
+    data.append(FP16x16 { mag: 144969, sign: false });
+    data.append(FP16x16 { mag: 72585, sign: false });
+    data.append(FP16x16 { mag: 96262, sign: true });
+    data.append(FP16x16 { mag: 152991, sign: true });
+    data.append(FP16x16 { mag: 38957, sign: false });
+    data.append(FP16x16 { mag: 46719, sign: true });
+    data.append(FP16x16 { mag: 11869, sign: false });
+    data.append(FP16x16 { mag: 45582, sign: false });
+    data.append(FP16x16 { mag: 12464, sign: true });
+    data.append(FP16x16 { mag: 25688, sign: true });
+    data.append(FP16x16 { mag: 68881, sign: true });
+    data.append(FP16x16 { mag: 67620, sign: true });
+    data.append(FP16x16 { mag: 40860, sign: true });
+    data.append(FP16x16 { mag: 72401, sign: false });
+    data.append(FP16x16 { mag: 148655, sign: false });
+    data.append(FP16x16 { mag: 88817, sign: false });
+    data.append(FP16x16 { mag: 69136, sign: true });
+    data.append(FP16x16 { mag: 67311, sign: true });
+    data.append(FP16x16 { mag: 91271, sign: false });
+    data.append(FP16x16 { mag: 44836, sign: false });
+    data.append(FP16x16 { mag: 24194, sign: true });
+    data.append(FP16x16 { mag: 34197, sign: true });
+    data.append(FP16x16 { mag: 5799, sign: false });
+    data.append(FP16x16 { mag: 152246, sign: true });
+    data.append(FP16x16 { mag: 92415, sign: false });
+    data.append(FP16x16 { mag: 9547, sign: true });
+    data.append(FP16x16 { mag: 70654, sign: false });
+    data.append(FP16x16 { mag: 30297, sign: true });
+    data.append(FP16x16 { mag: 19252, sign: false });
+    data.append(FP16x16 { mag: 44739, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-4/input_0.cairo b/tests/nodes/layer_normalization_4d_axis-4/input_0.cairo
new file mode 100644
index 000000000..796ab8161
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-4/input_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 50439, sign: true });
+    data.append(FP16x16 { mag: 80584, sign: false });
+    data.append(FP16x16 { mag: 108804, sign: false });
+    data.append(FP16x16 { mag: 64736, sign: true });
+    data.append(FP16x16 { mag: 35989, sign: false });
+    data.append(FP16x16 { mag: 83918, sign: false });
+    data.append(FP16x16 { mag: 80462, sign: true });
+    data.append(FP16x16 { mag: 43318, sign: false });
+    data.append(FP16x16 { mag: 65651, sign: true });
+    data.append(FP16x16 { mag: 11331, sign: true });
+    data.append(FP16x16 { mag: 25254, sign: true });
+    data.append(FP16x16 { mag: 62677, sign: false });
+    data.append(FP16x16 { mag: 8412, sign: false });
+    data.append(FP16x16 { mag: 64067, sign: true });
+    data.append(FP16x16 { mag: 11592, sign: true });
+    data.append(FP16x16 { mag: 10557, sign: false });
+    data.append(FP16x16 { mag: 33540, sign: true });
+    data.append(FP16x16 { mag: 50007, sign: true });
+    data.append(FP16x16 { mag: 66942, sign: true });
+    data.append(FP16x16 { mag: 24781, sign: true });
+    data.append(FP16x16 { mag: 70618, sign: true });
+    data.append(FP16x16 { mag: 91944, sign: false });
+    data.append(FP16x16 { mag: 25130, sign: false });
+    data.append(FP16x16 { mag: 70069, sign: true });
+    data.append(FP16x16 { mag: 19429, sign: false });
+    data.append(FP16x16 { mag: 17607, sign: true });
+    data.append(FP16x16 { mag: 20059, sign: true });
+    data.append(FP16x16 { mag: 30637, sign: false });
+    data.append(FP16x16 { mag: 110305, sign: true });
+    data.append(FP16x16 { mag: 47227, sign: true });
+    data.append(FP16x16 { mag: 52701, sign: false });
+    data.append(FP16x16 { mag: 113586, sign: true });
+    data.append(FP16x16 { mag: 28893, sign: false });
+    data.append(FP16x16 { mag: 19309, sign: true });
+    data.append(FP16x16 { mag: 9704, sign: true });
+    data.append(FP16x16 { mag: 56793, sign: false });
+    data.append(FP16x16 { mag: 44072, sign: false });
+    data.append(FP16x16 { mag: 2660, sign: true });
+    data.append(FP16x16 { mag: 29401, sign: true });
+    data.append(FP16x16 { mag: 36736, sign: true });
+    data.append(FP16x16 { mag: 86250, sign: false });
+    data.append(FP16x16 { mag: 38971, sign: true });
+    data.append(FP16x16 { mag: 138249, sign: true });
+    data.append(FP16x16 { mag: 36048, sign: false });
+    data.append(FP16x16 { mag: 37452, sign: false });
+    data.append(FP16x16 { mag: 23181, sign: true });
+    data.append(FP16x16 { mag: 35955, sign: true });
+    data.append(FP16x16 { mag: 51748, sign: true });
+    data.append(FP16x16 { mag: 57097, sign: true });
+    data.append(FP16x16 { mag: 91645, sign: true });
+    data.append(FP16x16 { mag: 727, sign: true });
+    data.append(FP16x16 { mag: 26384, sign: true });
+    data.append(FP16x16 { mag: 1331, sign: false });
+    data.append(FP16x16 { mag: 98672, sign: false });
+    data.append(FP16x16 { mag: 82596, sign: false });
+    data.append(FP16x16 { mag: 100984, sign: true });
+    data.append(FP16x16 { mag: 88934, sign: true });
+    data.append(FP16x16 { mag: 110736, sign: false });
+    data.append(FP16x16 { mag: 106050, sign: true });
+    data.append(FP16x16 { mag: 43286, sign: true });
+    data.append(FP16x16 { mag: 114427, sign: false });
+    data.append(FP16x16 { mag: 26160, sign: false });
+    data.append(FP16x16 { mag: 19914, sign: true });
+    data.append(FP16x16 { mag: 168031, sign: false });
+    data.append(FP16x16 { mag: 70834, sign: true });
+    data.append(FP16x16 { mag: 62785, sign: false });
+    data.append(FP16x16 { mag: 20711, sign: true });
+    data.append(FP16x16 { mag: 150814, sign: true });
+    data.append(FP16x16 { mag: 19909, sign: true });
+    data.append(FP16x16 { mag: 4360, sign: true });
+    data.append(FP16x16 { mag: 38550, sign: false });
+    data.append(FP16x16 { mag: 155210, sign: false });
+    data.append(FP16x16 { mag: 49387, sign: true });
+    data.append(FP16x16 { mag: 48606, sign: false });
+    data.append(FP16x16 { mag: 26297, sign: false });
+    data.append(FP16x16 { mag: 5832, sign: true });
+    data.append(FP16x16 { mag: 67019, sign: false });
+    data.append(FP16x16 { mag: 108552, sign: false });
+    data.append(FP16x16 { mag: 38302, sign: true });
+    data.append(FP16x16 { mag: 67467, sign: false });
+    data.append(FP16x16 { mag: 123524, sign: false });
+    data.append(FP16x16 { mag: 38110, sign: true });
+    data.append(FP16x16 { mag: 49459, sign: true });
+    data.append(FP16x16 { mag: 89977, sign: false });
+    data.append(FP16x16 { mag: 3116, sign: false });
+    data.append(FP16x16 { mag: 139868, sign: true });
+    data.append(FP16x16 { mag: 3777, sign: false });
+    data.append(FP16x16 { mag: 93508, sign: false });
+    data.append(FP16x16 { mag: 18473, sign: true });
+    data.append(FP16x16 { mag: 9749, sign: false });
+    data.append(FP16x16 { mag: 69280, sign: false });
+    data.append(FP16x16 { mag: 31261, sign: true });
+    data.append(FP16x16 { mag: 70810, sign: true });
+    data.append(FP16x16 { mag: 53719, sign: true });
+    data.append(FP16x16 { mag: 25490, sign: false });
+    data.append(FP16x16 { mag: 76561, sign: true });
+    data.append(FP16x16 { mag: 87810, sign: true });
+    data.append(FP16x16 { mag: 54546, sign: true });
+    data.append(FP16x16 { mag: 11956, sign: false });
+    data.append(FP16x16 { mag: 53981, sign: true });
+    data.append(FP16x16 { mag: 48734, sign: false });
+    data.append(FP16x16 { mag: 81861, sign: true });
+    data.append(FP16x16 { mag: 91276, sign: false });
+    data.append(FP16x16 { mag: 104233, sign: false });
+    data.append(FP16x16 { mag: 52396, sign: false });
+    data.append(FP16x16 { mag: 37016, sign: false });
+    data.append(FP16x16 { mag: 39784, sign: false });
+    data.append(FP16x16 { mag: 16087, sign: true });
+    data.append(FP16x16 { mag: 22464, sign: true });
+    data.append(FP16x16 { mag: 98432, sign: true });
+    data.append(FP16x16 { mag: 120832, sign: true });
+    data.append(FP16x16 { mag: 25665, sign: true });
+    data.append(FP16x16 { mag: 23338, sign: true });
+    data.append(FP16x16 { mag: 18801, sign: false });
+    data.append(FP16x16 { mag: 22675, sign: false });
+    data.append(FP16x16 { mag: 79634, sign: true });
+    data.append(FP16x16 { mag: 95608, sign: true });
+    data.append(FP16x16 { mag: 73767, sign: false });
+    data.append(FP16x16 { mag: 20235, sign: false });
+    data.append(FP16x16 { mag: 19535, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-4/input_1.cairo b/tests/nodes/layer_normalization_4d_axis-4/input_1.cairo
new file mode 100644
index 000000000..7505f0217
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-4/input_1.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 9950, sign: true });
+    data.append(FP16x16 { mag: 28981, sign: true });
+    data.append(FP16x16 { mag: 12325, sign: false });
+    data.append(FP16x16 { mag: 111567, sign: true });
+    data.append(FP16x16 { mag: 87492, sign: true });
+    data.append(FP16x16 { mag: 2266, sign: false });
+    data.append(FP16x16 { mag: 91808, sign: false });
+    data.append(FP16x16 { mag: 99181, sign: false });
+    data.append(FP16x16 { mag: 54619, sign: false });
+    data.append(FP16x16 { mag: 56530, sign: true });
+    data.append(FP16x16 { mag: 58746, sign: false });
+    data.append(FP16x16 { mag: 11025, sign: true });
+    data.append(FP16x16 { mag: 63919, sign: false });
+    data.append(FP16x16 { mag: 16987, sign: true });
+    data.append(FP16x16 { mag: 14843, sign: true });
+    data.append(FP16x16 { mag: 9629, sign: false });
+    data.append(FP16x16 { mag: 27461, sign: false });
+    data.append(FP16x16 { mag: 34324, sign: false });
+    data.append(FP16x16 { mag: 102809, sign: true });
+    data.append(FP16x16 { mag: 75888, sign: false });
+    data.append(FP16x16 { mag: 65510, sign: true });
+    data.append(FP16x16 { mag: 54723, sign: true });
+    data.append(FP16x16 { mag: 52244, sign: true });
+    data.append(FP16x16 { mag: 54119, sign: false });
+    data.append(FP16x16 { mag: 28309, sign: false });
+    data.append(FP16x16 { mag: 30690, sign: false });
+    data.append(FP16x16 { mag: 5155, sign: false });
+    data.append(FP16x16 { mag: 1463, sign: false });
+    data.append(FP16x16 { mag: 72974, sign: true });
+    data.append(FP16x16 { mag: 62563, sign: true });
+    data.append(FP16x16 { mag: 115530, sign: true });
+    data.append(FP16x16 { mag: 3885, sign: true });
+    data.append(FP16x16 { mag: 1274, sign: false });
+    data.append(FP16x16 { mag: 20319, sign: true });
+    data.append(FP16x16 { mag: 13396, sign: true });
+    data.append(FP16x16 { mag: 12923, sign: false });
+    data.append(FP16x16 { mag: 13623, sign: false });
+    data.append(FP16x16 { mag: 95497, sign: true });
+    data.append(FP16x16 { mag: 78541, sign: false });
+    data.append(FP16x16 { mag: 61124, sign: false });
+    data.append(FP16x16 { mag: 12527, sign: true });
+    data.append(FP16x16 { mag: 128370, sign: true });
+    data.append(FP16x16 { mag: 81782, sign: false });
+    data.append(FP16x16 { mag: 47494, sign: true });
+    data.append(FP16x16 { mag: 15027, sign: false });
+    data.append(FP16x16 { mag: 76936, sign: false });
+    data.append(FP16x16 { mag: 95112, sign: true });
+    data.append(FP16x16 { mag: 37987, sign: false });
+    data.append(FP16x16 { mag: 11759, sign: false });
+    data.append(FP16x16 { mag: 128485, sign: true });
+    data.append(FP16x16 { mag: 12506, sign: false });
+    data.append(FP16x16 { mag: 16778, sign: false });
+    data.append(FP16x16 { mag: 49483, sign: false });
+    data.append(FP16x16 { mag: 11346, sign: false });
+    data.append(FP16x16 { mag: 58647, sign: true });
+    data.append(FP16x16 { mag: 71599, sign: false });
+    data.append(FP16x16 { mag: 22777, sign: false });
+    data.append(FP16x16 { mag: 17125, sign: true });
+    data.append(FP16x16 { mag: 122340, sign: true });
+    data.append(FP16x16 { mag: 14001, sign: true });
+    data.append(FP16x16 { mag: 43279, sign: false });
+    data.append(FP16x16 { mag: 74703, sign: false });
+    data.append(FP16x16 { mag: 95648, sign: false });
+    data.append(FP16x16 { mag: 9808, sign: true });
+    data.append(FP16x16 { mag: 41586, sign: false });
+    data.append(FP16x16 { mag: 55055, sign: false });
+    data.append(FP16x16 { mag: 29114, sign: false });
+    data.append(FP16x16 { mag: 80337, sign: true });
+    data.append(FP16x16 { mag: 45090, sign: false });
+    data.append(FP16x16 { mag: 21497, sign: true });
+    data.append(FP16x16 { mag: 66453, sign: true });
+    data.append(FP16x16 { mag: 41129, sign: true });
+    data.append(FP16x16 { mag: 28771, sign: false });
+    data.append(FP16x16 { mag: 38619, sign: true });
+    data.append(FP16x16 { mag: 12052, sign: true });
+    data.append(FP16x16 { mag: 71742, sign: true });
+    data.append(FP16x16 { mag: 3122, sign: true });
+    data.append(FP16x16 { mag: 7658, sign: false });
+    data.append(FP16x16 { mag: 72650, sign: false });
+    data.append(FP16x16 { mag: 125101, sign: true });
+    data.append(FP16x16 { mag: 123350, sign: true });
+    data.append(FP16x16 { mag: 17299, sign: true });
+    data.append(FP16x16 { mag: 133664, sign: false });
+    data.append(FP16x16 { mag: 95871, sign: false });
+    data.append(FP16x16 { mag: 68174, sign: true });
+    data.append(FP16x16 { mag: 11742, sign: true });
+    data.append(FP16x16 { mag: 39757, sign: false });
+    data.append(FP16x16 { mag: 107969, sign: true });
+    data.append(FP16x16 { mag: 47625, sign: false });
+    data.append(FP16x16 { mag: 25381, sign: false });
+    data.append(FP16x16 { mag: 86657, sign: false });
+    data.append(FP16x16 { mag: 124296, sign: true });
+    data.append(FP16x16 { mag: 84481, sign: true });
+    data.append(FP16x16 { mag: 39857, sign: false });
+    data.append(FP16x16 { mag: 62123, sign: false });
+    data.append(FP16x16 { mag: 51178, sign: true });
+    data.append(FP16x16 { mag: 1008, sign: true });
+    data.append(FP16x16 { mag: 58152, sign: false });
+    data.append(FP16x16 { mag: 23649, sign: false });
+    data.append(FP16x16 { mag: 21472, sign: true });
+    data.append(FP16x16 { mag: 42397, sign: true });
+    data.append(FP16x16 { mag: 9088, sign: true });
+    data.append(FP16x16 { mag: 174345, sign: false });
+    data.append(FP16x16 { mag: 36378, sign: true });
+    data.append(FP16x16 { mag: 57507, sign: false });
+    data.append(FP16x16 { mag: 9867, sign: false });
+    data.append(FP16x16 { mag: 103912, sign: false });
+    data.append(FP16x16 { mag: 14015, sign: false });
+    data.append(FP16x16 { mag: 18604, sign: true });
+    data.append(FP16x16 { mag: 5497, sign: false });
+    data.append(FP16x16 { mag: 47155, sign: false });
+    data.append(FP16x16 { mag: 46396, sign: true });
+    data.append(FP16x16 { mag: 57120, sign: false });
+    data.append(FP16x16 { mag: 74433, sign: true });
+    data.append(FP16x16 { mag: 8714, sign: false });
+    data.append(FP16x16 { mag: 52414, sign: true });
+    data.append(FP16x16 { mag: 29603, sign: false });
+    data.append(FP16x16 { mag: 34762, sign: false });
+    data.append(FP16x16 { mag: 30832, sign: false });
+    data.append(FP16x16 { mag: 71610, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-4/input_2.cairo b/tests/nodes/layer_normalization_4d_axis-4/input_2.cairo
new file mode 100644
index 000000000..ebcaa1b95
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-4/input_2.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 74987, sign: false });
+    data.append(FP16x16 { mag: 102257, sign: true });
+    data.append(FP16x16 { mag: 78717, sign: false });
+    data.append(FP16x16 { mag: 59379, sign: true });
+    data.append(FP16x16 { mag: 11211, sign: true });
+    data.append(FP16x16 { mag: 26255, sign: false });
+    data.append(FP16x16 { mag: 30689, sign: false });
+    data.append(FP16x16 { mag: 80491, sign: true });
+    data.append(FP16x16 { mag: 14461, sign: true });
+    data.append(FP16x16 { mag: 42696, sign: false });
+    data.append(FP16x16 { mag: 56317, sign: false });
+    data.append(FP16x16 { mag: 88135, sign: false });
+    data.append(FP16x16 { mag: 45393, sign: false });
+    data.append(FP16x16 { mag: 18363, sign: true });
+    data.append(FP16x16 { mag: 20773, sign: false });
+    data.append(FP16x16 { mag: 66375, sign: false });
+    data.append(FP16x16 { mag: 35314, sign: true });
+    data.append(FP16x16 { mag: 52902, sign: false });
+    data.append(FP16x16 { mag: 86324, sign: true });
+    data.append(FP16x16 { mag: 40151, sign: true });
+    data.append(FP16x16 { mag: 22446, sign: false });
+    data.append(FP16x16 { mag: 33948, sign: true });
+    data.append(FP16x16 { mag: 30607, sign: false });
+    data.append(FP16x16 { mag: 8106, sign: false });
+    data.append(FP16x16 { mag: 42198, sign: false });
+    data.append(FP16x16 { mag: 128853, sign: true });
+    data.append(FP16x16 { mag: 17078, sign: true });
+    data.append(FP16x16 { mag: 200766, sign: false });
+    data.append(FP16x16 { mag: 8367, sign: false });
+    data.append(FP16x16 { mag: 91621, sign: true });
+    data.append(FP16x16 { mag: 34264, sign: false });
+    data.append(FP16x16 { mag: 46301, sign: false });
+    data.append(FP16x16 { mag: 106288, sign: false });
+    data.append(FP16x16 { mag: 130170, sign: false });
+    data.append(FP16x16 { mag: 5525, sign: false });
+    data.append(FP16x16 { mag: 111843, sign: true });
+    data.append(FP16x16 { mag: 1844, sign: true });
+    data.append(FP16x16 { mag: 73523, sign: true });
+    data.append(FP16x16 { mag: 33663, sign: false });
+    data.append(FP16x16 { mag: 9757, sign: false });
+    data.append(FP16x16 { mag: 16424, sign: true });
+    data.append(FP16x16 { mag: 67890, sign: false });
+    data.append(FP16x16 { mag: 103692, sign: true });
+    data.append(FP16x16 { mag: 37978, sign: false });
+    data.append(FP16x16 { mag: 52354, sign: true });
+    data.append(FP16x16 { mag: 62808, sign: false });
+    data.append(FP16x16 { mag: 73374, sign: true });
+    data.append(FP16x16 { mag: 42169, sign: false });
+    data.append(FP16x16 { mag: 42576, sign: false });
+    data.append(FP16x16 { mag: 76141, sign: false });
+    data.append(FP16x16 { mag: 12231, sign: false });
+    data.append(FP16x16 { mag: 42136, sign: false });
+    data.append(FP16x16 { mag: 47496, sign: true });
+    data.append(FP16x16 { mag: 81264, sign: false });
+    data.append(FP16x16 { mag: 15196, sign: true });
+    data.append(FP16x16 { mag: 70791, sign: true });
+    data.append(FP16x16 { mag: 7825, sign: true });
+    data.append(FP16x16 { mag: 40333, sign: true });
+    data.append(FP16x16 { mag: 30221, sign: false });
+    data.append(FP16x16 { mag: 98040, sign: true });
+    data.append(FP16x16 { mag: 39486, sign: true });
+    data.append(FP16x16 { mag: 6407, sign: false });
+    data.append(FP16x16 { mag: 24512, sign: true });
+    data.append(FP16x16 { mag: 7948, sign: true });
+    data.append(FP16x16 { mag: 11193, sign: true });
+    data.append(FP16x16 { mag: 3711, sign: true });
+    data.append(FP16x16 { mag: 35698, sign: true });
+    data.append(FP16x16 { mag: 3213, sign: false });
+    data.append(FP16x16 { mag: 38235, sign: false });
+    data.append(FP16x16 { mag: 82210, sign: true });
+    data.append(FP16x16 { mag: 6567, sign: true });
+    data.append(FP16x16 { mag: 5350, sign: false });
+    data.append(FP16x16 { mag: 5068, sign: true });
+    data.append(FP16x16 { mag: 27441, sign: true });
+    data.append(FP16x16 { mag: 17130, sign: false });
+    data.append(FP16x16 { mag: 89900, sign: true });
+    data.append(FP16x16 { mag: 77188, sign: false });
+    data.append(FP16x16 { mag: 34983, sign: true });
+    data.append(FP16x16 { mag: 5966, sign: false });
+    data.append(FP16x16 { mag: 16995, sign: true });
+    data.append(FP16x16 { mag: 34228, sign: true });
+    data.append(FP16x16 { mag: 29003, sign: false });
+    data.append(FP16x16 { mag: 9617, sign: false });
+    data.append(FP16x16 { mag: 70272, sign: true });
+    data.append(FP16x16 { mag: 82673, sign: true });
+    data.append(FP16x16 { mag: 12155, sign: false });
+    data.append(FP16x16 { mag: 25123, sign: true });
+    data.append(FP16x16 { mag: 137966, sign: true });
+    data.append(FP16x16 { mag: 69397, sign: true });
+    data.append(FP16x16 { mag: 61271, sign: false });
+    data.append(FP16x16 { mag: 12363, sign: false });
+    data.append(FP16x16 { mag: 13484, sign: true });
+    data.append(FP16x16 { mag: 85721, sign: false });
+    data.append(FP16x16 { mag: 81931, sign: false });
+    data.append(FP16x16 { mag: 15988, sign: false });
+    data.append(FP16x16 { mag: 82208, sign: false });
+    data.append(FP16x16 { mag: 94093, sign: true });
+    data.append(FP16x16 { mag: 111240, sign: true });
+    data.append(FP16x16 { mag: 95369, sign: false });
+    data.append(FP16x16 { mag: 29800, sign: false });
+    data.append(FP16x16 { mag: 18974, sign: false });
+    data.append(FP16x16 { mag: 41527, sign: true });
+    data.append(FP16x16 { mag: 12256, sign: false });
+    data.append(FP16x16 { mag: 28697, sign: false });
+    data.append(FP16x16 { mag: 92475, sign: true });
+    data.append(FP16x16 { mag: 90746, sign: true });
+    data.append(FP16x16 { mag: 4496, sign: true });
+    data.append(FP16x16 { mag: 6464, sign: false });
+    data.append(FP16x16 { mag: 45570, sign: true });
+    data.append(FP16x16 { mag: 28209, sign: true });
+    data.append(FP16x16 { mag: 33084, sign: false });
+    data.append(FP16x16 { mag: 131340, sign: false });
+    data.append(FP16x16 { mag: 15928, sign: true });
+    data.append(FP16x16 { mag: 13786, sign: true });
+    data.append(FP16x16 { mag: 89832, sign: false });
+    data.append(FP16x16 { mag: 59414, sign: true });
+    data.append(FP16x16 { mag: 27186, sign: true });
+    data.append(FP16x16 { mag: 57062, sign: false });
+    data.append(FP16x16 { mag: 8290, sign: false });
+    data.append(FP16x16 { mag: 23747, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis-4/output_0.cairo b/tests/nodes/layer_normalization_4d_axis-4/output_0.cairo
new file mode 100644
index 000000000..3dd5f2734
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis-4/output_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 81938, sign: false });
+    data.append(FP16x16 { mag: 139115, sign: true });
+    data.append(FP16x16 { mag: 99625, sign: false });
+    data.append(FP16x16 { mag: 42553, sign: false });
+    data.append(FP16x16 { mag: 63807, sign: true });
+    data.append(FP16x16 { mag: 29251, sign: false });
+    data.append(FP16x16 { mag: 74903, sign: true });
+    data.append(FP16x16 { mag: 9937, sign: true });
+    data.append(FP16x16 { mag: 65116, sign: true });
+    data.append(FP16x16 { mag: 48943, sign: false });
+    data.append(FP16x16 { mag: 37524, sign: false });
+    data.append(FP16x16 { mag: 77082, sign: false });
+    data.append(FP16x16 { mag: 57309, sign: false });
+    data.append(FP16x16 { mag: 3013, sign: true });
+    data.append(FP16x16 { mag: 22472, sign: false });
+    data.append(FP16x16 { mag: 68481, sign: false });
+    data.append(FP16x16 { mag: 47521, sign: true });
+    data.append(FP16x16 { mag: 29144, sign: false });
+    data.append(FP16x16 { mag: 11016, sign: false });
+    data.append(FP16x16 { mag: 63886, sign: true });
+    data.append(FP16x16 { mag: 88094, sign: false });
+    data.append(FP16x16 { mag: 112894, sign: true });
+    data.append(FP16x16 { mag: 7732, sign: false });
+    data.append(FP16x16 { mag: 45680, sign: true });
+    data.append(FP16x16 { mag: 52166, sign: false });
+    data.append(FP16x16 { mag: 135141, sign: true });
+    data.append(FP16x16 { mag: 18324, sign: true });
+    data.append(FP16x16 { mag: 201528, sign: false });
+    data.append(FP16x16 { mag: 125050, sign: false });
+    data.append(FP16x16 { mag: 50933, sign: true });
+    data.append(FP16x16 { mag: 64223, sign: true });
+    data.append(FP16x16 { mag: 52706, sign: false });
+    data.append(FP16x16 { mag: 106918, sign: false });
+    data.append(FP16x16 { mag: 134854, sign: false });
+    data.append(FP16x16 { mag: 6678, sign: false });
+    data.append(FP16x16 { mag: 100030, sign: true });
+    data.append(FP16x16 { mag: 8001, sign: false });
+    data.append(FP16x16 { mag: 75425, sign: true });
+    data.append(FP16x16 { mag: 3641, sign: false });
+    data.append(FP16x16 { mag: 20349, sign: true });
+    data.append(FP16x16 { mag: 33423, sign: true });
+    data.append(FP16x16 { mag: 135434, sign: false });
+    data.append(FP16x16 { mag: 268826, sign: true });
+    data.append(FP16x16 { mag: 9384, sign: false });
+    data.append(FP16x16 { mag: 42989, sign: true });
+    data.append(FP16x16 { mag: 40596, sign: false });
+    data.append(FP16x16 { mag: 27643, sign: true });
+    data.append(FP16x16 { mag: 14881, sign: false });
+    data.append(FP16x16 { mag: 33184, sign: false });
+    data.append(FP16x16 { mag: 245527, sign: false });
+    data.append(FP16x16 { mag: 12844, sign: false });
+    data.append(FP16x16 { mag: 36484, sign: false });
+    data.append(FP16x16 { mag: 43540, sign: true });
+    data.append(FP16x16 { mag: 98782, sign: false });
+    data.append(FP16x16 { mag: 91559, sign: true });
+    data.append(FP16x16 { mag: 175239, sign: true });
+    data.append(FP16x16 { mag: 36924, sign: true });
+    data.append(FP16x16 { mag: 69880, sign: true });
+    data.append(FP16x16 { mag: 218008, sign: false });
+    data.append(FP16x16 { mag: 89765, sign: true });
+    data.append(FP16x16 { mag: 37583, sign: false });
+    data.append(FP16x16 { mag: 40273, sign: false });
+    data.append(FP16x16 { mag: 47428, sign: true });
+    data.append(FP16x16 { mag: 33322, sign: true });
+    data.append(FP16x16 { mag: 53002, sign: true });
+    data.append(FP16x16 { mag: 51571, sign: false });
+    data.append(FP16x16 { mag: 43023, sign: true });
+    data.append(FP16x16 { mag: 180611, sign: false });
+    data.append(FP16x16 { mag: 27436, sign: false });
+    data.append(FP16x16 { mag: 82088, sign: true });
+    data.append(FP16x16 { mag: 49075, sign: true });
+    data.append(FP16x16 { mag: 93116, sign: true });
+    data.append(FP16x16 { mag: 24713, sign: true });
+    data.append(FP16x16 { mag: 57986, sign: true });
+    data.append(FP16x16 { mag: 11641, sign: false });
+    data.append(FP16x16 { mag: 87906, sign: true });
+    data.append(FP16x16 { mag: 73854, sign: false });
+    data.append(FP16x16 { mag: 22022, sign: true });
+    data.append(FP16x16 { mag: 31528, sign: true });
+    data.append(FP16x16 { mag: 151422, sign: true });
+    data.append(FP16x16 { mag: 270761, sign: true });
+    data.append(FP16x16 { mag: 37881, sign: false });
+    data.append(FP16x16 { mag: 81795, sign: true });
+    data.append(FP16x16 { mag: 65199, sign: false });
+    data.append(FP16x16 { mag: 89952, sign: true });
+    data.append(FP16x16 { mag: 36152, sign: false });
+    data.append(FP16x16 { mag: 20482, sign: true });
+    data.append(FP16x16 { mag: 296268, sign: true });
+    data.append(FP16x16 { mag: 79775, sign: true });
+    data.append(FP16x16 { mag: 66513, sign: false });
+    data.append(FP16x16 { mag: 107842, sign: false });
+    data.append(FP16x16 { mag: 37504, sign: false });
+    data.append(FP16x16 { mag: 170624, sign: false });
+    data.append(FP16x16 { mag: 52118, sign: false });
+    data.append(FP16x16 { mag: 43525, sign: false });
+    data.append(FP16x16 { mag: 138068, sign: false });
+    data.append(FP16x16 { mag: 92822, sign: true });
+    data.append(FP16x16 { mag: 155459, sign: true });
+    data.append(FP16x16 { mag: 101038, sign: false });
+    data.append(FP16x16 { mag: 45944, sign: false });
+    data.append(FP16x16 { mag: 14639, sign: true });
+    data.append(FP16x16 { mag: 30883, sign: true });
+    data.append(FP16x16 { mag: 262024, sign: false });
+    data.append(FP16x16 { mag: 30507, sign: true });
+    data.append(FP16x16 { mag: 43715, sign: true });
+    data.append(FP16x16 { mag: 84662, sign: true });
+    data.append(FP16x16 { mag: 63901, sign: false });
+    data.append(FP16x16 { mag: 3913, sign: false });
+    data.append(FP16x16 { mag: 40400, sign: true });
+    data.append(FP16x16 { mag: 36018, sign: true });
+    data.append(FP16x16 { mag: 49780, sign: true });
+    data.append(FP16x16 { mag: 146468, sign: false });
+    data.append(FP16x16 { mag: 32554, sign: true });
+    data.append(FP16x16 { mag: 39291, sign: true });
+    data.append(FP16x16 { mag: 93325, sign: false });
+    data.append(FP16x16 { mag: 216, sign: false });
+    data.append(FP16x16 { mag: 67978, sign: true });
+    data.append(FP16x16 { mag: 97710, sign: false });
+    data.append(FP16x16 { mag: 19520, sign: false });
+    data.append(FP16x16 { mag: 6998, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis0.cairo b/tests/nodes/layer_normalization_4d_axis0.cairo
new file mode 100644
index 000000000..2a3b930ec
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis0.cairo
@@ -0,0 +1,32 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP8x23Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23TensorPartialEq;
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_4d_axis0() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1,
+            Option::Some(@input_2),
+            Option::Some(IntegerTrait::<i32>::new(0, false)),
+            Option::None,
+            Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_4d_axis0/input_0.cairo b/tests/nodes/layer_normalization_4d_axis0/input_0.cairo
new file mode 100644
index 000000000..d7913bb11
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis0/input_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 9853496, sign: true });
+    data.append(FP8x23 { mag: 12258403, sign: false });
+    data.append(FP8x23 { mag: 872656, sign: false });
+    data.append(FP8x23 { mag: 7388622, sign: true });
+    data.append(FP8x23 { mag: 15454325, sign: false });
+    data.append(FP8x23 { mag: 16251435, sign: false });
+    data.append(FP8x23 { mag: 903277, sign: true });
+    data.append(FP8x23 { mag: 3289794, sign: true });
+    data.append(FP8x23 { mag: 8057933, sign: false });
+    data.append(FP8x23 { mag: 6386388, sign: true });
+    data.append(FP8x23 { mag: 5708410, sign: true });
+    data.append(FP8x23 { mag: 4543373, sign: true });
+    data.append(FP8x23 { mag: 23644376, sign: false });
+    data.append(FP8x23 { mag: 7843321, sign: true });
+    data.append(FP8x23 { mag: 5801261, sign: false });
+    data.append(FP8x23 { mag: 5404517, sign: true });
+    data.append(FP8x23 { mag: 3421350, sign: false });
+    data.append(FP8x23 { mag: 2806284, sign: true });
+    data.append(FP8x23 { mag: 5479745, sign: false });
+    data.append(FP8x23 { mag: 9044852, sign: true });
+    data.append(FP8x23 { mag: 2879371, sign: true });
+    data.append(FP8x23 { mag: 7506722, sign: false });
+    data.append(FP8x23 { mag: 374708, sign: false });
+    data.append(FP8x23 { mag: 16088456, sign: false });
+    data.append(FP8x23 { mag: 7446071, sign: false });
+    data.append(FP8x23 { mag: 12333473, sign: true });
+    data.append(FP8x23 { mag: 2694855, sign: false });
+    data.append(FP8x23 { mag: 29333, sign: false });
+    data.append(FP8x23 { mag: 3210230, sign: true });
+    data.append(FP8x23 { mag: 246487, sign: false });
+    data.append(FP8x23 { mag: 11307238, sign: true });
+    data.append(FP8x23 { mag: 808074, sign: false });
+    data.append(FP8x23 { mag: 2244426, sign: false });
+    data.append(FP8x23 { mag: 4494036, sign: false });
+    data.append(FP8x23 { mag: 9168918, sign: false });
+    data.append(FP8x23 { mag: 11831318, sign: true });
+    data.append(FP8x23 { mag: 11514568, sign: true });
+    data.append(FP8x23 { mag: 3381120, sign: true });
+    data.append(FP8x23 { mag: 6827926, sign: true });
+    data.append(FP8x23 { mag: 2579494, sign: false });
+    data.append(FP8x23 { mag: 4493030, sign: true });
+    data.append(FP8x23 { mag: 4570125, sign: false });
+    data.append(FP8x23 { mag: 8010665, sign: false });
+    data.append(FP8x23 { mag: 5794037, sign: true });
+    data.append(FP8x23 { mag: 9849078, sign: false });
+    data.append(FP8x23 { mag: 11691798, sign: true });
+    data.append(FP8x23 { mag: 3652747, sign: false });
+    data.append(FP8x23 { mag: 1032666, sign: true });
+    data.append(FP8x23 { mag: 9259310, sign: true });
+    data.append(FP8x23 { mag: 7405492, sign: false });
+    data.append(FP8x23 { mag: 4687488, sign: true });
+    data.append(FP8x23 { mag: 1097650, sign: true });
+    data.append(FP8x23 { mag: 2213858, sign: true });
+    data.append(FP8x23 { mag: 1436205, sign: true });
+    data.append(FP8x23 { mag: 10226423, sign: false });
+    data.append(FP8x23 { mag: 6130226, sign: false });
+    data.append(FP8x23 { mag: 1214058, sign: false });
+    data.append(FP8x23 { mag: 12299984, sign: true });
+    data.append(FP8x23 { mag: 829240, sign: false });
+    data.append(FP8x23 { mag: 1612388, sign: false });
+    data.append(FP8x23 { mag: 6632529, sign: true });
+    data.append(FP8x23 { mag: 4410829, sign: true });
+    data.append(FP8x23 { mag: 20654302, sign: false });
+    data.append(FP8x23 { mag: 462475, sign: true });
+    data.append(FP8x23 { mag: 10502841, sign: true });
+    data.append(FP8x23 { mag: 7171902, sign: true });
+    data.append(FP8x23 { mag: 4204962, sign: false });
+    data.append(FP8x23 { mag: 17427142, sign: true });
+    data.append(FP8x23 { mag: 12555224, sign: true });
+    data.append(FP8x23 { mag: 8307885, sign: true });
+    data.append(FP8x23 { mag: 455536, sign: false });
+    data.append(FP8x23 { mag: 577191, sign: false });
+    data.append(FP8x23 { mag: 4412268, sign: true });
+    data.append(FP8x23 { mag: 15812229, sign: false });
+    data.append(FP8x23 { mag: 7098764, sign: true });
+    data.append(FP8x23 { mag: 9127468, sign: false });
+    data.append(FP8x23 { mag: 4753858, sign: false });
+    data.append(FP8x23 { mag: 2074029, sign: true });
+    data.append(FP8x23 { mag: 1651256, sign: false });
+    data.append(FP8x23 { mag: 9617324, sign: true });
+    data.append(FP8x23 { mag: 11400835, sign: false });
+    data.append(FP8x23 { mag: 4263073, sign: true });
+    data.append(FP8x23 { mag: 22170402, sign: false });
+    data.append(FP8x23 { mag: 7715608, sign: false });
+    data.append(FP8x23 { mag: 7511781, sign: true });
+    data.append(FP8x23 { mag: 8686402, sign: false });
+    data.append(FP8x23 { mag: 2710329, sign: true });
+    data.append(FP8x23 { mag: 5540998, sign: true });
+    data.append(FP8x23 { mag: 11608300, sign: true });
+    data.append(FP8x23 { mag: 3020404, sign: true });
+    data.append(FP8x23 { mag: 6342478, sign: true });
+    data.append(FP8x23 { mag: 9399735, sign: true });
+    data.append(FP8x23 { mag: 446463, sign: false });
+    data.append(FP8x23 { mag: 13691013, sign: true });
+    data.append(FP8x23 { mag: 11552903, sign: true });
+    data.append(FP8x23 { mag: 1204731, sign: false });
+    data.append(FP8x23 { mag: 1741592, sign: true });
+    data.append(FP8x23 { mag: 13103082, sign: false });
+    data.append(FP8x23 { mag: 3181444, sign: true });
+    data.append(FP8x23 { mag: 256975, sign: true });
+    data.append(FP8x23 { mag: 9440785, sign: true });
+    data.append(FP8x23 { mag: 2112590, sign: false });
+    data.append(FP8x23 { mag: 13404752, sign: false });
+    data.append(FP8x23 { mag: 760699, sign: false });
+    data.append(FP8x23 { mag: 1588793, sign: true });
+    data.append(FP8x23 { mag: 13026604, sign: true });
+    data.append(FP8x23 { mag: 452707, sign: false });
+    data.append(FP8x23 { mag: 7267348, sign: true });
+    data.append(FP8x23 { mag: 14737007, sign: false });
+    data.append(FP8x23 { mag: 8457998, sign: false });
+    data.append(FP8x23 { mag: 2233703, sign: false });
+    data.append(FP8x23 { mag: 3434673, sign: true });
+    data.append(FP8x23 { mag: 4280157, sign: true });
+    data.append(FP8x23 { mag: 2950181, sign: true });
+    data.append(FP8x23 { mag: 1385553, sign: false });
+    data.append(FP8x23 { mag: 17250056, sign: false });
+    data.append(FP8x23 { mag: 12716927, sign: true });
+    data.append(FP8x23 { mag: 2980452, sign: false });
+    data.append(FP8x23 { mag: 13031106, sign: true });
+    data.append(FP8x23 { mag: 4118717, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis0/input_1.cairo b/tests/nodes/layer_normalization_4d_axis0/input_1.cairo
new file mode 100644
index 000000000..d5d19269f
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis0/input_1.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_1() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 7856434, sign: false });
+    data.append(FP8x23 { mag: 6559353, sign: false });
+    data.append(FP8x23 { mag: 2374243, sign: true });
+    data.append(FP8x23 { mag: 9038396, sign: true });
+    data.append(FP8x23 { mag: 4891209, sign: false });
+    data.append(FP8x23 { mag: 10531353, sign: false });
+    data.append(FP8x23 { mag: 2374580, sign: false });
+    data.append(FP8x23 { mag: 15815884, sign: false });
+    data.append(FP8x23 { mag: 8516677, sign: true });
+    data.append(FP8x23 { mag: 812409, sign: false });
+    data.append(FP8x23 { mag: 17686618, sign: false });
+    data.append(FP8x23 { mag: 7197725, sign: false });
+    data.append(FP8x23 { mag: 10954258, sign: false });
+    data.append(FP8x23 { mag: 8350298, sign: false });
+    data.append(FP8x23 { mag: 4666106, sign: true });
+    data.append(FP8x23 { mag: 7256064, sign: false });
+    data.append(FP8x23 { mag: 8635308, sign: false });
+    data.append(FP8x23 { mag: 5024989, sign: false });
+    data.append(FP8x23 { mag: 1263377, sign: true });
+    data.append(FP8x23 { mag: 10669786, sign: false });
+    data.append(FP8x23 { mag: 12365659, sign: true });
+    data.append(FP8x23 { mag: 9240214, sign: true });
+    data.append(FP8x23 { mag: 6033435, sign: true });
+    data.append(FP8x23 { mag: 2813974, sign: false });
+    data.append(FP8x23 { mag: 2864389, sign: true });
+    data.append(FP8x23 { mag: 7531822, sign: true });
+    data.append(FP8x23 { mag: 14534796, sign: false });
+    data.append(FP8x23 { mag: 12879313, sign: false });
+    data.append(FP8x23 { mag: 23143788, sign: true });
+    data.append(FP8x23 { mag: 956451, sign: true });
+    data.append(FP8x23 { mag: 140224, sign: false });
+    data.append(FP8x23 { mag: 3524534, sign: false });
+    data.append(FP8x23 { mag: 9520969, sign: true });
+    data.append(FP8x23 { mag: 13151858, sign: true });
+    data.append(FP8x23 { mag: 7994295, sign: false });
+    data.append(FP8x23 { mag: 5842236, sign: false });
+    data.append(FP8x23 { mag: 6780944, sign: false });
+    data.append(FP8x23 { mag: 2066721, sign: false });
+    data.append(FP8x23 { mag: 2105799, sign: true });
+    data.append(FP8x23 { mag: 1298593, sign: true });
+    data.append(FP8x23 { mag: 4229235, sign: true });
+    data.append(FP8x23 { mag: 7262550, sign: true });
+    data.append(FP8x23 { mag: 6216754, sign: false });
+    data.append(FP8x23 { mag: 10747679, sign: true });
+    data.append(FP8x23 { mag: 6150248, sign: true });
+    data.append(FP8x23 { mag: 11662506, sign: true });
+    data.append(FP8x23 { mag: 2114116, sign: false });
+    data.append(FP8x23 { mag: 3345752, sign: false });
+    data.append(FP8x23 { mag: 10971260, sign: false });
+    data.append(FP8x23 { mag: 1397404, sign: true });
+    data.append(FP8x23 { mag: 7777503, sign: false });
+    data.append(FP8x23 { mag: 614354, sign: true });
+    data.append(FP8x23 { mag: 2547461, sign: true });
+    data.append(FP8x23 { mag: 9306342, sign: false });
+    data.append(FP8x23 { mag: 8136902, sign: true });
+    data.append(FP8x23 { mag: 2523700, sign: false });
+    data.append(FP8x23 { mag: 5851470, sign: true });
+    data.append(FP8x23 { mag: 2397709, sign: true });
+    data.append(FP8x23 { mag: 2791362, sign: true });
+    data.append(FP8x23 { mag: 11438024, sign: true });
+    data.append(FP8x23 { mag: 2376167, sign: false });
+    data.append(FP8x23 { mag: 11929178, sign: true });
+    data.append(FP8x23 { mag: 2334309, sign: false });
+    data.append(FP8x23 { mag: 11396586, sign: false });
+    data.append(FP8x23 { mag: 9273483, sign: true });
+    data.append(FP8x23 { mag: 5663225, sign: true });
+    data.append(FP8x23 { mag: 7022748, sign: false });
+    data.append(FP8x23 { mag: 3415334, sign: true });
+    data.append(FP8x23 { mag: 5583578, sign: false });
+    data.append(FP8x23 { mag: 3376007, sign: false });
+    data.append(FP8x23 { mag: 9800954, sign: true });
+    data.append(FP8x23 { mag: 557269, sign: true });
+    data.append(FP8x23 { mag: 1332713, sign: false });
+    data.append(FP8x23 { mag: 16394277, sign: false });
+    data.append(FP8x23 { mag: 2404128, sign: false });
+    data.append(FP8x23 { mag: 7264511, sign: true });
+    data.append(FP8x23 { mag: 2959914, sign: false });
+    data.append(FP8x23 { mag: 8465123, sign: false });
+    data.append(FP8x23 { mag: 2179246, sign: false });
+    data.append(FP8x23 { mag: 1403755, sign: true });
+    data.append(FP8x23 { mag: 8841557, sign: true });
+    data.append(FP8x23 { mag: 714874, sign: true });
+    data.append(FP8x23 { mag: 3087264, sign: true });
+    data.append(FP8x23 { mag: 3259074, sign: true });
+    data.append(FP8x23 { mag: 10403024, sign: true });
+    data.append(FP8x23 { mag: 9207515, sign: false });
+    data.append(FP8x23 { mag: 10106154, sign: true });
+    data.append(FP8x23 { mag: 22441932, sign: false });
+    data.append(FP8x23 { mag: 2506462, sign: false });
+    data.append(FP8x23 { mag: 7772606, sign: true });
+    data.append(FP8x23 { mag: 10066387, sign: false });
+    data.append(FP8x23 { mag: 13018190, sign: false });
+    data.append(FP8x23 { mag: 551564, sign: true });
+    data.append(FP8x23 { mag: 15173981, sign: false });
+    data.append(FP8x23 { mag: 537916, sign: true });
+    data.append(FP8x23 { mag: 1144261, sign: true });
+    data.append(FP8x23 { mag: 10201860, sign: false });
+    data.append(FP8x23 { mag: 14629321, sign: false });
+    data.append(FP8x23 { mag: 10980985, sign: true });
+    data.append(FP8x23 { mag: 8271637, sign: false });
+    data.append(FP8x23 { mag: 12637380, sign: false });
+    data.append(FP8x23 { mag: 7736607, sign: true });
+    data.append(FP8x23 { mag: 1168929, sign: false });
+    data.append(FP8x23 { mag: 7719282, sign: false });
+    data.append(FP8x23 { mag: 8013562, sign: true });
+    data.append(FP8x23 { mag: 1909398, sign: false });
+    data.append(FP8x23 { mag: 7909437, sign: false });
+    data.append(FP8x23 { mag: 9358508, sign: true });
+    data.append(FP8x23 { mag: 2679626, sign: true });
+    data.append(FP8x23 { mag: 10816482, sign: false });
+    data.append(FP8x23 { mag: 5938359, sign: true });
+    data.append(FP8x23 { mag: 3868879, sign: true });
+    data.append(FP8x23 { mag: 17720398, sign: false });
+    data.append(FP8x23 { mag: 8780306, sign: false });
+    data.append(FP8x23 { mag: 8182772, sign: true });
+    data.append(FP8x23 { mag: 8474158, sign: true });
+    data.append(FP8x23 { mag: 10484711, sign: false });
+    data.append(FP8x23 { mag: 6278095, sign: false });
+    data.append(FP8x23 { mag: 13616979, sign: false });
+    data.append(FP8x23 { mag: 683891, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis0/input_2.cairo b/tests/nodes/layer_normalization_4d_axis0/input_2.cairo
new file mode 100644
index 000000000..576e62784
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis0/input_2.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_2() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 2658357, sign: true });
+    data.append(FP8x23 { mag: 1102321, sign: true });
+    data.append(FP8x23 { mag: 9403754, sign: false });
+    data.append(FP8x23 { mag: 6280169, sign: true });
+    data.append(FP8x23 { mag: 322990, sign: true });
+    data.append(FP8x23 { mag: 1855990, sign: false });
+    data.append(FP8x23 { mag: 7959357, sign: false });
+    data.append(FP8x23 { mag: 3813579, sign: true });
+    data.append(FP8x23 { mag: 17190620, sign: false });
+    data.append(FP8x23 { mag: 7625388, sign: false });
+    data.append(FP8x23 { mag: 6326665, sign: true });
+    data.append(FP8x23 { mag: 2144242, sign: false });
+    data.append(FP8x23 { mag: 1652040, sign: true });
+    data.append(FP8x23 { mag: 1708413, sign: true });
+    data.append(FP8x23 { mag: 5577434, sign: false });
+    data.append(FP8x23 { mag: 12958978, sign: false });
+    data.append(FP8x23 { mag: 14472776, sign: true });
+    data.append(FP8x23 { mag: 12142315, sign: true });
+    data.append(FP8x23 { mag: 5004137, sign: true });
+    data.append(FP8x23 { mag: 10687620, sign: false });
+    data.append(FP8x23 { mag: 11911291, sign: true });
+    data.append(FP8x23 { mag: 10598514, sign: true });
+    data.append(FP8x23 { mag: 2129772, sign: true });
+    data.append(FP8x23 { mag: 6883996, sign: false });
+    data.append(FP8x23 { mag: 4546950, sign: true });
+    data.append(FP8x23 { mag: 3450860, sign: true });
+    data.append(FP8x23 { mag: 1775511, sign: true });
+    data.append(FP8x23 { mag: 512918, sign: false });
+    data.append(FP8x23 { mag: 3693486, sign: false });
+    data.append(FP8x23 { mag: 4656135, sign: true });
+    data.append(FP8x23 { mag: 5018284, sign: false });
+    data.append(FP8x23 { mag: 17579972, sign: false });
+    data.append(FP8x23 { mag: 5429984, sign: true });
+    data.append(FP8x23 { mag: 3455574, sign: false });
+    data.append(FP8x23 { mag: 2776885, sign: false });
+    data.append(FP8x23 { mag: 12394373, sign: false });
+    data.append(FP8x23 { mag: 12252363, sign: true });
+    data.append(FP8x23 { mag: 9655402, sign: false });
+    data.append(FP8x23 { mag: 11694762, sign: false });
+    data.append(FP8x23 { mag: 2352758, sign: false });
+    data.append(FP8x23 { mag: 14214209, sign: false });
+    data.append(FP8x23 { mag: 4904045, sign: true });
+    data.append(FP8x23 { mag: 750954, sign: false });
+    data.append(FP8x23 { mag: 5521106, sign: true });
+    data.append(FP8x23 { mag: 8124032, sign: true });
+    data.append(FP8x23 { mag: 3155244, sign: true });
+    data.append(FP8x23 { mag: 2794749, sign: true });
+    data.append(FP8x23 { mag: 11913728, sign: true });
+    data.append(FP8x23 { mag: 3406536, sign: false });
+    data.append(FP8x23 { mag: 17572360, sign: false });
+    data.append(FP8x23 { mag: 3846474, sign: false });
+    data.append(FP8x23 { mag: 4679753, sign: true });
+    data.append(FP8x23 { mag: 3526965, sign: false });
+    data.append(FP8x23 { mag: 2361175, sign: true });
+    data.append(FP8x23 { mag: 6800555, sign: false });
+    data.append(FP8x23 { mag: 8765096, sign: false });
+    data.append(FP8x23 { mag: 2069919, sign: true });
+    data.append(FP8x23 { mag: 10541365, sign: false });
+    data.append(FP8x23 { mag: 5702066, sign: false });
+    data.append(FP8x23 { mag: 1901244, sign: true });
+    data.append(FP8x23 { mag: 4795815, sign: true });
+    data.append(FP8x23 { mag: 5440175, sign: true });
+    data.append(FP8x23 { mag: 6145038, sign: true });
+    data.append(FP8x23 { mag: 8155758, sign: true });
+    data.append(FP8x23 { mag: 12882916, sign: false });
+    data.append(FP8x23 { mag: 8512317, sign: true });
+    data.append(FP8x23 { mag: 8827800, sign: false });
+    data.append(FP8x23 { mag: 4780680, sign: true });
+    data.append(FP8x23 { mag: 10233073, sign: false });
+    data.append(FP8x23 { mag: 3987405, sign: false });
+    data.append(FP8x23 { mag: 9797702, sign: true });
+    data.append(FP8x23 { mag: 11709163, sign: true });
+    data.append(FP8x23 { mag: 5010523, sign: false });
+    data.append(FP8x23 { mag: 5898579, sign: false });
+    data.append(FP8x23 { mag: 3610233, sign: false });
+    data.append(FP8x23 { mag: 4366597, sign: true });
+    data.append(FP8x23 { mag: 4121505, sign: false });
+    data.append(FP8x23 { mag: 1386837, sign: true });
+    data.append(FP8x23 { mag: 2197332, sign: false });
+    data.append(FP8x23 { mag: 1583611, sign: false });
+    data.append(FP8x23 { mag: 1204922, sign: false });
+    data.append(FP8x23 { mag: 1731995, sign: true });
+    data.append(FP8x23 { mag: 1196413, sign: true });
+    data.append(FP8x23 { mag: 11399971, sign: true });
+    data.append(FP8x23 { mag: 1303591, sign: true });
+    data.append(FP8x23 { mag: 1306507, sign: true });
+    data.append(FP8x23 { mag: 8296480, sign: false });
+    data.append(FP8x23 { mag: 6644877, sign: true });
+    data.append(FP8x23 { mag: 7522441, sign: false });
+    data.append(FP8x23 { mag: 13195509, sign: false });
+    data.append(FP8x23 { mag: 5912413, sign: false });
+    data.append(FP8x23 { mag: 6827008, sign: true });
+    data.append(FP8x23 { mag: 6046144, sign: true });
+    data.append(FP8x23 { mag: 8475255, sign: true });
+    data.append(FP8x23 { mag: 15398520, sign: false });
+    data.append(FP8x23 { mag: 2626721, sign: false });
+    data.append(FP8x23 { mag: 4142304, sign: true });
+    data.append(FP8x23 { mag: 6868178, sign: false });
+    data.append(FP8x23 { mag: 1537408, sign: true });
+    data.append(FP8x23 { mag: 4760507, sign: true });
+    data.append(FP8x23 { mag: 16526955, sign: false });
+    data.append(FP8x23 { mag: 4766056, sign: false });
+    data.append(FP8x23 { mag: 4099142, sign: true });
+    data.append(FP8x23 { mag: 2694894, sign: false });
+    data.append(FP8x23 { mag: 6008844, sign: true });
+    data.append(FP8x23 { mag: 15054491, sign: false });
+    data.append(FP8x23 { mag: 2926959, sign: true });
+    data.append(FP8x23 { mag: 4325199, sign: false });
+    data.append(FP8x23 { mag: 1262017, sign: false });
+    data.append(FP8x23 { mag: 8352226, sign: false });
+    data.append(FP8x23 { mag: 5432226, sign: true });
+    data.append(FP8x23 { mag: 7565847, sign: true });
+    data.append(FP8x23 { mag: 1398099, sign: true });
+    data.append(FP8x23 { mag: 10744126, sign: false });
+    data.append(FP8x23 { mag: 10154080, sign: false });
+    data.append(FP8x23 { mag: 543398, sign: true });
+    data.append(FP8x23 { mag: 4199815, sign: false });
+    data.append(FP8x23 { mag: 3981547, sign: false });
+    data.append(FP8x23 { mag: 3843559, sign: true });
+    data.append(FP8x23 { mag: 2002995, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis0/output_0.cairo b/tests/nodes/layer_normalization_4d_axis0/output_0.cairo
new file mode 100644
index 000000000..ecfa40d38
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis0/output_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn output_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 11485245, sign: true });
+    data.append(FP8x23 { mag: 8639736, sign: false });
+    data.append(FP8x23 { mag: 9066761, sign: false });
+    data.append(FP8x23 { mag: 1246285, sign: false });
+    data.append(FP8x23 { mag: 8785751, sign: false });
+    data.append(FP8x23 { mag: 22458580, sign: false });
+    data.append(FP8x23 { mag: 7798869, sign: false });
+    data.append(FP8x23 { mag: 9335609, sign: true });
+    data.append(FP8x23 { mag: 8762101, sign: false });
+    data.append(FP8x23 { mag: 7044940, sign: false });
+    data.append(FP8x23 { mag: 17548656, sign: true });
+    data.append(FP8x23 { mag: 1433318, sign: true });
+    data.append(FP8x23 { mag: 29332314, sign: false });
+    data.append(FP8x23 { mag: 9109826, sign: true });
+    data.append(FP8x23 { mag: 2201931, sign: false });
+    data.append(FP8x23 { mag: 8615226, sign: false });
+    data.append(FP8x23 { mag: 10650534, sign: true });
+    data.append(FP8x23 { mag: 13610117, sign: true });
+    data.append(FP8x23 { mag: 5870152, sign: true });
+    data.append(FP8x23 { mag: 282205, sign: true });
+    data.append(FP8x23 { mag: 8192649, sign: true });
+    data.append(FP8x23 { mag: 19142176, sign: true });
+    data.append(FP8x23 { mag: 2631691, sign: true });
+    data.append(FP8x23 { mag: 12334894, sign: false });
+    data.append(FP8x23 { mag: 7174919, sign: true });
+    data.append(FP8x23 { mag: 7215017, sign: false });
+    data.append(FP8x23 { mag: 3412223, sign: false });
+    data.append(FP8x23 { mag: 1059551, sign: false });
+    data.append(FP8x23 { mag: 11556768, sign: false });
+    data.append(FP8x23 { mag: 4721233, sign: true });
+    data.append(FP8x23 { mag: 4836688, sign: false });
+    data.append(FP8x23 { mag: 18053380, sign: false });
+    data.append(FP8x23 { mag: 8322237, sign: true });
+    data.append(FP8x23 { mag: 4030251, sign: true });
+    data.append(FP8x23 { mag: 11736265, sign: false });
+    data.append(FP8x23 { mag: 4467248, sign: false });
+    data.append(FP8x23 { mag: 21199784, sign: true });
+    data.append(FP8x23 { mag: 8911549, sign: false });
+    data.append(FP8x23 { mag: 13309004, sign: false });
+    data.append(FP8x23 { mag: 1906941, sign: false });
+    data.append(FP8x23 { mag: 16291191, sign: false });
+    data.append(FP8x23 { mag: 9102967, sign: true });
+    data.append(FP8x23 { mag: 6868689, sign: false });
+    data.append(FP8x23 { mag: 1406769, sign: false });
+    data.append(FP8x23 { mag: 15510274, sign: true });
+    data.append(FP8x23 { mag: 12477229, sign: false });
+    data.append(FP8x23 { mag: 1801263, sign: true });
+    data.append(FP8x23 { mag: 12190927, sign: true });
+    data.append(FP8x23 { mag: 8150830, sign: true });
+    data.append(FP8x23 { mag: 16296984, sign: false });
+    data.append(FP8x23 { mag: 151497, sign: true });
+    data.append(FP8x23 { mag: 4624143, sign: true });
+    data.append(FP8x23 { mag: 4093028, sign: false });
+    data.append(FP8x23 { mag: 3575281, sign: true });
+    data.append(FP8x23 { mag: 3333834, sign: true });
+    data.append(FP8x23 { mag: 10688711, sign: false });
+    data.append(FP8x23 { mag: 3136147, sign: true });
+    data.append(FP8x23 { mag: 13927308, sign: false });
+    data.append(FP8x23 { mag: 5320165, sign: false });
+    data.append(FP8x23 { mag: 4522953, sign: true });
+    data.append(FP8x23 { mag: 6562536, sign: true });
+    data.append(FP8x23 { mag: 302568, sign: false });
+    data.append(FP8x23 { mag: 365858, sign: true });
+    data.append(FP8x23 { mag: 8333322, sign: true });
+    data.append(FP8x23 { mag: 24012324, sign: false });
+    data.append(FP8x23 { mag: 3941235, sign: true });
+    data.append(FP8x23 { mag: 12585527, sign: false });
+    data.append(FP8x23 { mag: 2108236, sign: false });
+    data.append(FP8x23 { mag: 2180042, sign: false });
+    data.append(FP8x23 { mag: 809995, sign: false });
+    data.append(FP8x23 { mag: 10706503, sign: true });
+    data.append(FP8x23 { mag: 11768835, sign: true });
+    data.append(FP8x23 { mag: 4368724, sign: false });
+    data.append(FP8x23 { mag: 37121360, sign: false });
+    data.append(FP8x23 { mag: 1690481, sign: false });
+    data.append(FP8x23 { mag: 12472568, sign: true });
+    data.append(FP8x23 { mag: 5896973, sign: false });
+    data.append(FP8x23 { mag: 3128197, sign: true });
+    data.append(FP8x23 { mag: 2706830, sign: false });
+    data.append(FP8x23 { mag: 3121650, sign: false });
+    data.append(FP8x23 { mag: 11032154, sign: true });
+    data.append(FP8x23 { mag: 1400313, sign: true });
+    data.append(FP8x23 { mag: 9391938, sign: true });
+    data.append(FP8x23 { mag: 14493685, sign: true });
+    data.append(FP8x23 { mag: 7510374, sign: false });
+    data.append(FP8x23 { mag: 8488401, sign: false });
+    data.append(FP8x23 { mag: 11134086, sign: false });
+    data.append(FP8x23 { mag: 20440816, sign: true });
+    data.append(FP8x23 { mag: 4187458, sign: false });
+    data.append(FP8x23 { mag: 15662241, sign: false });
+    data.append(FP8x23 { mag: 1227645, sign: true });
+    data.append(FP8x23 { mag: 20756332, sign: true });
+    data.append(FP8x23 { mag: 6096698, sign: true });
+    data.append(FP8x23 { mag: 32393544, sign: true });
+    data.append(FP8x23 { mag: 16110731, sign: false });
+    data.append(FP8x23 { mag: 2419478, sign: false });
+    data.append(FP8x23 { mag: 5840805, sign: true });
+    data.append(FP8x23 { mag: 30053754, sign: false });
+    data.append(FP8x23 { mag: 2156172, sign: false });
+    data.append(FP8x23 { mag: 4688839, sign: true });
+    data.append(FP8x23 { mag: 2943892, sign: false });
+    data.append(FP8x23 { mag: 2536186, sign: false });
+    data.append(FP8x23 { mag: 2204937, sign: true });
+    data.append(FP8x23 { mag: 3688586, sign: false });
+    data.append(FP8x23 { mag: 4819133, sign: true });
+    data.append(FP8x23 { mag: 12194435, sign: false });
+    data.append(FP8x23 { mag: 2196191, sign: true });
+    data.append(FP8x23 { mag: 11984318, sign: false });
+    data.append(FP8x23 { mag: 3501392, sign: true });
+    data.append(FP8x23 { mag: 19567274, sign: false });
+    data.append(FP8x23 { mag: 7228652, sign: true });
+    data.append(FP8x23 { mag: 6148920, sign: true });
+    data.append(FP8x23 { mag: 9655568, sign: true });
+    data.append(FP8x23 { mag: 8030332, sign: false });
+    data.append(FP8x23 { mag: 8497492, sign: false });
+    data.append(FP8x23 { mag: 18119870, sign: true });
+    data.append(FP8x23 { mag: 11121997, sign: true });
+    data.append(FP8x23 { mag: 6433851, sign: false });
+    data.append(FP8x23 { mag: 24247440, sign: true });
+    data.append(FP8x23 { mag: 1697336, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis1.cairo b/tests/nodes/layer_normalization_4d_axis1.cairo
new file mode 100644
index 000000000..d3d8808ce
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis1.cairo
@@ -0,0 +1,32 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP8x23Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23TensorPartialEq;
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_4d_axis1() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1,
+            Option::Some(@input_2),
+            Option::Some(IntegerTrait::<i32>::new(1, false)),
+            Option::None,
+            Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_4d_axis1/input_0.cairo b/tests/nodes/layer_normalization_4d_axis1/input_0.cairo
new file mode 100644
index 000000000..d7913bb11
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis1/input_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 9853496, sign: true });
+    data.append(FP8x23 { mag: 12258403, sign: false });
+    data.append(FP8x23 { mag: 872656, sign: false });
+    data.append(FP8x23 { mag: 7388622, sign: true });
+    data.append(FP8x23 { mag: 15454325, sign: false });
+    data.append(FP8x23 { mag: 16251435, sign: false });
+    data.append(FP8x23 { mag: 903277, sign: true });
+    data.append(FP8x23 { mag: 3289794, sign: true });
+    data.append(FP8x23 { mag: 8057933, sign: false });
+    data.append(FP8x23 { mag: 6386388, sign: true });
+    data.append(FP8x23 { mag: 5708410, sign: true });
+    data.append(FP8x23 { mag: 4543373, sign: true });
+    data.append(FP8x23 { mag: 23644376, sign: false });
+    data.append(FP8x23 { mag: 7843321, sign: true });
+    data.append(FP8x23 { mag: 5801261, sign: false });
+    data.append(FP8x23 { mag: 5404517, sign: true });
+    data.append(FP8x23 { mag: 3421350, sign: false });
+    data.append(FP8x23 { mag: 2806284, sign: true });
+    data.append(FP8x23 { mag: 5479745, sign: false });
+    data.append(FP8x23 { mag: 9044852, sign: true });
+    data.append(FP8x23 { mag: 2879371, sign: true });
+    data.append(FP8x23 { mag: 7506722, sign: false });
+    data.append(FP8x23 { mag: 374708, sign: false });
+    data.append(FP8x23 { mag: 16088456, sign: false });
+    data.append(FP8x23 { mag: 7446071, sign: false });
+    data.append(FP8x23 { mag: 12333473, sign: true });
+    data.append(FP8x23 { mag: 2694855, sign: false });
+    data.append(FP8x23 { mag: 29333, sign: false });
+    data.append(FP8x23 { mag: 3210230, sign: true });
+    data.append(FP8x23 { mag: 246487, sign: false });
+    data.append(FP8x23 { mag: 11307238, sign: true });
+    data.append(FP8x23 { mag: 808074, sign: false });
+    data.append(FP8x23 { mag: 2244426, sign: false });
+    data.append(FP8x23 { mag: 4494036, sign: false });
+    data.append(FP8x23 { mag: 9168918, sign: false });
+    data.append(FP8x23 { mag: 11831318, sign: true });
+    data.append(FP8x23 { mag: 11514568, sign: true });
+    data.append(FP8x23 { mag: 3381120, sign: true });
+    data.append(FP8x23 { mag: 6827926, sign: true });
+    data.append(FP8x23 { mag: 2579494, sign: false });
+    data.append(FP8x23 { mag: 4493030, sign: true });
+    data.append(FP8x23 { mag: 4570125, sign: false });
+    data.append(FP8x23 { mag: 8010665, sign: false });
+    data.append(FP8x23 { mag: 5794037, sign: true });
+    data.append(FP8x23 { mag: 9849078, sign: false });
+    data.append(FP8x23 { mag: 11691798, sign: true });
+    data.append(FP8x23 { mag: 3652747, sign: false });
+    data.append(FP8x23 { mag: 1032666, sign: true });
+    data.append(FP8x23 { mag: 9259310, sign: true });
+    data.append(FP8x23 { mag: 7405492, sign: false });
+    data.append(FP8x23 { mag: 4687488, sign: true });
+    data.append(FP8x23 { mag: 1097650, sign: true });
+    data.append(FP8x23 { mag: 2213858, sign: true });
+    data.append(FP8x23 { mag: 1436205, sign: true });
+    data.append(FP8x23 { mag: 10226423, sign: false });
+    data.append(FP8x23 { mag: 6130226, sign: false });
+    data.append(FP8x23 { mag: 1214058, sign: false });
+    data.append(FP8x23 { mag: 12299984, sign: true });
+    data.append(FP8x23 { mag: 829240, sign: false });
+    data.append(FP8x23 { mag: 1612388, sign: false });
+    data.append(FP8x23 { mag: 6632529, sign: true });
+    data.append(FP8x23 { mag: 4410829, sign: true });
+    data.append(FP8x23 { mag: 20654302, sign: false });
+    data.append(FP8x23 { mag: 462475, sign: true });
+    data.append(FP8x23 { mag: 10502841, sign: true });
+    data.append(FP8x23 { mag: 7171902, sign: true });
+    data.append(FP8x23 { mag: 4204962, sign: false });
+    data.append(FP8x23 { mag: 17427142, sign: true });
+    data.append(FP8x23 { mag: 12555224, sign: true });
+    data.append(FP8x23 { mag: 8307885, sign: true });
+    data.append(FP8x23 { mag: 455536, sign: false });
+    data.append(FP8x23 { mag: 577191, sign: false });
+    data.append(FP8x23 { mag: 4412268, sign: true });
+    data.append(FP8x23 { mag: 15812229, sign: false });
+    data.append(FP8x23 { mag: 7098764, sign: true });
+    data.append(FP8x23 { mag: 9127468, sign: false });
+    data.append(FP8x23 { mag: 4753858, sign: false });
+    data.append(FP8x23 { mag: 2074029, sign: true });
+    data.append(FP8x23 { mag: 1651256, sign: false });
+    data.append(FP8x23 { mag: 9617324, sign: true });
+    data.append(FP8x23 { mag: 11400835, sign: false });
+    data.append(FP8x23 { mag: 4263073, sign: true });
+    data.append(FP8x23 { mag: 22170402, sign: false });
+    data.append(FP8x23 { mag: 7715608, sign: false });
+    data.append(FP8x23 { mag: 7511781, sign: true });
+    data.append(FP8x23 { mag: 8686402, sign: false });
+    data.append(FP8x23 { mag: 2710329, sign: true });
+    data.append(FP8x23 { mag: 5540998, sign: true });
+    data.append(FP8x23 { mag: 11608300, sign: true });
+    data.append(FP8x23 { mag: 3020404, sign: true });
+    data.append(FP8x23 { mag: 6342478, sign: true });
+    data.append(FP8x23 { mag: 9399735, sign: true });
+    data.append(FP8x23 { mag: 446463, sign: false });
+    data.append(FP8x23 { mag: 13691013, sign: true });
+    data.append(FP8x23 { mag: 11552903, sign: true });
+    data.append(FP8x23 { mag: 1204731, sign: false });
+    data.append(FP8x23 { mag: 1741592, sign: true });
+    data.append(FP8x23 { mag: 13103082, sign: false });
+    data.append(FP8x23 { mag: 3181444, sign: true });
+    data.append(FP8x23 { mag: 256975, sign: true });
+    data.append(FP8x23 { mag: 9440785, sign: true });
+    data.append(FP8x23 { mag: 2112590, sign: false });
+    data.append(FP8x23 { mag: 13404752, sign: false });
+    data.append(FP8x23 { mag: 760699, sign: false });
+    data.append(FP8x23 { mag: 1588793, sign: true });
+    data.append(FP8x23 { mag: 13026604, sign: true });
+    data.append(FP8x23 { mag: 452707, sign: false });
+    data.append(FP8x23 { mag: 7267348, sign: true });
+    data.append(FP8x23 { mag: 14737007, sign: false });
+    data.append(FP8x23 { mag: 8457998, sign: false });
+    data.append(FP8x23 { mag: 2233703, sign: false });
+    data.append(FP8x23 { mag: 3434673, sign: true });
+    data.append(FP8x23 { mag: 4280157, sign: true });
+    data.append(FP8x23 { mag: 2950181, sign: true });
+    data.append(FP8x23 { mag: 1385553, sign: false });
+    data.append(FP8x23 { mag: 17250056, sign: false });
+    data.append(FP8x23 { mag: 12716927, sign: true });
+    data.append(FP8x23 { mag: 2980452, sign: false });
+    data.append(FP8x23 { mag: 13031106, sign: true });
+    data.append(FP8x23 { mag: 4118717, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis1/input_1.cairo b/tests/nodes/layer_normalization_4d_axis1/input_1.cairo
new file mode 100644
index 000000000..9097a814d
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis1/input_1.cairo
@@ -0,0 +1,74 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_1() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 848181, sign: true });
+    data.append(FP8x23 { mag: 81518, sign: true });
+    data.append(FP8x23 { mag: 945519, sign: true });
+    data.append(FP8x23 { mag: 6487248, sign: false });
+    data.append(FP8x23 { mag: 9672423, sign: false });
+    data.append(FP8x23 { mag: 1904835, sign: false });
+    data.append(FP8x23 { mag: 2382261, sign: false });
+    data.append(FP8x23 { mag: 7115567, sign: false });
+    data.append(FP8x23 { mag: 2500882, sign: true });
+    data.append(FP8x23 { mag: 7175046, sign: false });
+    data.append(FP8x23 { mag: 9187167, sign: true });
+    data.append(FP8x23 { mag: 8462170, sign: true });
+    data.append(FP8x23 { mag: 696867, sign: false });
+    data.append(FP8x23 { mag: 21124896, sign: true });
+    data.append(FP8x23 { mag: 5267251, sign: true });
+    data.append(FP8x23 { mag: 2079757, sign: true });
+    data.append(FP8x23 { mag: 1167805, sign: false });
+    data.append(FP8x23 { mag: 1152176, sign: false });
+    data.append(FP8x23 { mag: 10891270, sign: false });
+    data.append(FP8x23 { mag: 14322228, sign: false });
+    data.append(FP8x23 { mag: 8522531, sign: false });
+    data.append(FP8x23 { mag: 10739640, sign: false });
+    data.append(FP8x23 { mag: 2595621, sign: false });
+    data.append(FP8x23 { mag: 8005426, sign: true });
+    data.append(FP8x23 { mag: 17252540, sign: false });
+    data.append(FP8x23 { mag: 240957, sign: false });
+    data.append(FP8x23 { mag: 11486831, sign: true });
+    data.append(FP8x23 { mag: 586966, sign: false });
+    data.append(FP8x23 { mag: 9136961, sign: true });
+    data.append(FP8x23 { mag: 6509073, sign: false });
+    data.append(FP8x23 { mag: 5880972, sign: true });
+    data.append(FP8x23 { mag: 13107216, sign: true });
+    data.append(FP8x23 { mag: 3496295, sign: true });
+    data.append(FP8x23 { mag: 8424994, sign: true });
+    data.append(FP8x23 { mag: 6071613, sign: false });
+    data.append(FP8x23 { mag: 2613672, sign: false });
+    data.append(FP8x23 { mag: 201175, sign: true });
+    data.append(FP8x23 { mag: 9019674, sign: true });
+    data.append(FP8x23 { mag: 8834173, sign: true });
+    data.append(FP8x23 { mag: 3624311, sign: false });
+    data.append(FP8x23 { mag: 11146336, sign: true });
+    data.append(FP8x23 { mag: 2676569, sign: false });
+    data.append(FP8x23 { mag: 2174599, sign: true });
+    data.append(FP8x23 { mag: 7756654, sign: false });
+    data.append(FP8x23 { mag: 4488299, sign: false });
+    data.append(FP8x23 { mag: 13736548, sign: false });
+    data.append(FP8x23 { mag: 8146999, sign: true });
+    data.append(FP8x23 { mag: 14514717, sign: false });
+    data.append(FP8x23 { mag: 194204, sign: true });
+    data.append(FP8x23 { mag: 1722858, sign: true });
+    data.append(FP8x23 { mag: 16956510, sign: true });
+    data.append(FP8x23 { mag: 938525, sign: true });
+    data.append(FP8x23 { mag: 12229289, sign: true });
+    data.append(FP8x23 { mag: 10339694, sign: false });
+    data.append(FP8x23 { mag: 5410863, sign: true });
+    data.append(FP8x23 { mag: 2290298, sign: false });
+    data.append(FP8x23 { mag: 523371, sign: true });
+    data.append(FP8x23 { mag: 3917128, sign: true });
+    data.append(FP8x23 { mag: 8846368, sign: false });
+    data.append(FP8x23 { mag: 878873, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis1/input_2.cairo b/tests/nodes/layer_normalization_4d_axis1/input_2.cairo
new file mode 100644
index 000000000..7f46fa541
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis1/input_2.cairo
@@ -0,0 +1,74 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_2() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 17573086, sign: false });
+    data.append(FP8x23 { mag: 8662834, sign: false });
+    data.append(FP8x23 { mag: 184643, sign: true });
+    data.append(FP8x23 { mag: 1609098, sign: false });
+    data.append(FP8x23 { mag: 7906768, sign: false });
+    data.append(FP8x23 { mag: 2763381, sign: false });
+    data.append(FP8x23 { mag: 6890297, sign: false });
+    data.append(FP8x23 { mag: 10693457, sign: true });
+    data.append(FP8x23 { mag: 12330995, sign: false });
+    data.append(FP8x23 { mag: 4256058, sign: false });
+    data.append(FP8x23 { mag: 9437667, sign: true });
+    data.append(FP8x23 { mag: 163731, sign: false });
+    data.append(FP8x23 { mag: 7124038, sign: false });
+    data.append(FP8x23 { mag: 9805754, sign: false });
+    data.append(FP8x23 { mag: 28536, sign: false });
+    data.append(FP8x23 { mag: 2693033, sign: true });
+    data.append(FP8x23 { mag: 2150848, sign: false });
+    data.append(FP8x23 { mag: 10084242, sign: true });
+    data.append(FP8x23 { mag: 8917886, sign: true });
+    data.append(FP8x23 { mag: 1425777, sign: false });
+    data.append(FP8x23 { mag: 2606228, sign: true });
+    data.append(FP8x23 { mag: 12616894, sign: false });
+    data.append(FP8x23 { mag: 2222915, sign: true });
+    data.append(FP8x23 { mag: 7726104, sign: true });
+    data.append(FP8x23 { mag: 2657657, sign: true });
+    data.append(FP8x23 { mag: 6077975, sign: false });
+    data.append(FP8x23 { mag: 13813849, sign: true });
+    data.append(FP8x23 { mag: 6292283, sign: false });
+    data.append(FP8x23 { mag: 10348040, sign: true });
+    data.append(FP8x23 { mag: 185170, sign: false });
+    data.append(FP8x23 { mag: 5951156, sign: true });
+    data.append(FP8x23 { mag: 17316840, sign: false });
+    data.append(FP8x23 { mag: 10838919, sign: false });
+    data.append(FP8x23 { mag: 1531418, sign: true });
+    data.append(FP8x23 { mag: 2328055, sign: true });
+    data.append(FP8x23 { mag: 2260306, sign: true });
+    data.append(FP8x23 { mag: 6598532, sign: true });
+    data.append(FP8x23 { mag: 13454879, sign: false });
+    data.append(FP8x23 { mag: 3992621, sign: true });
+    data.append(FP8x23 { mag: 2841648, sign: false });
+    data.append(FP8x23 { mag: 2219350, sign: false });
+    data.append(FP8x23 { mag: 1366422, sign: false });
+    data.append(FP8x23 { mag: 18715506, sign: false });
+    data.append(FP8x23 { mag: 818771, sign: true });
+    data.append(FP8x23 { mag: 16038561, sign: true });
+    data.append(FP8x23 { mag: 605075, sign: true });
+    data.append(FP8x23 { mag: 9018968, sign: true });
+    data.append(FP8x23 { mag: 12957025, sign: true });
+    data.append(FP8x23 { mag: 7949557, sign: true });
+    data.append(FP8x23 { mag: 4287990, sign: true });
+    data.append(FP8x23 { mag: 6013668, sign: true });
+    data.append(FP8x23 { mag: 5667271, sign: false });
+    data.append(FP8x23 { mag: 3089234, sign: true });
+    data.append(FP8x23 { mag: 8146239, sign: true });
+    data.append(FP8x23 { mag: 3678288, sign: true });
+    data.append(FP8x23 { mag: 4910526, sign: true });
+    data.append(FP8x23 { mag: 7866112, sign: false });
+    data.append(FP8x23 { mag: 7090234, sign: true });
+    data.append(FP8x23 { mag: 7707693, sign: true });
+    data.append(FP8x23 { mag: 3506913, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis1/output_0.cairo b/tests/nodes/layer_normalization_4d_axis1/output_0.cairo
new file mode 100644
index 000000000..25fb0235f
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis1/output_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn output_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 18660112, sign: false });
+    data.append(FP8x23 { mag: 8539773, sign: false });
+    data.append(FP8x23 { mag: 253071, sign: true });
+    data.append(FP8x23 { mag: 4686473, sign: true });
+    data.append(FP8x23 { mag: 26410314, sign: false });
+    data.append(FP8x23 { mag: 6599035, sign: false });
+    data.append(FP8x23 { mag: 6528656, sign: false });
+    data.append(FP8x23 { mag: 13917213, sign: true });
+    data.append(FP8x23 { mag: 9881702, sign: false });
+    data.append(FP8x23 { mag: 1799257, sign: true });
+    data.append(FP8x23 { mag: 2470490, sign: true });
+    data.append(FP8x23 { mag: 5336625, sign: false });
+    data.append(FP8x23 { mag: 9177605, sign: false });
+    data.append(FP8x23 { mag: 31518982, sign: false });
+    data.append(FP8x23 { mag: 3629625, sign: true });
+    data.append(FP8x23 { mag: 1195610, sign: true });
+    data.append(FP8x23 { mag: 2611073, sign: false });
+    data.append(FP8x23 { mag: 10535922, sign: true });
+    data.append(FP8x23 { mag: 1795805, sign: true });
+    data.append(FP8x23 { mag: 15467576, sign: true });
+    data.append(FP8x23 { mag: 6025885, sign: true });
+    data.append(FP8x23 { mag: 22387728, sign: false });
+    data.append(FP8x23 { mag: 2198218, sign: true });
+    data.append(FP8x23 { mag: 23681456, sign: true });
+    data.append(FP8x23 { mag: 12906475, sign: false });
+    data.append(FP8x23 { mag: 5693734, sign: false });
+    data.append(FP8x23 { mag: 17287328, sign: true });
+    data.append(FP8x23 { mag: 6272278, sign: false });
+    data.append(FP8x23 { mag: 6300243, sign: true });
+    data.append(FP8x23 { mag: 141751, sign: false });
+    data.append(FP8x23 { mag: 2665067, sign: false });
+    data.append(FP8x23 { mag: 16475111, sign: false });
+    data.append(FP8x23 { mag: 9980473, sign: false });
+    data.append(FP8x23 { mag: 5992447, sign: true });
+    data.append(FP8x23 { mag: 4469792, sign: false });
+    data.append(FP8x23 { mag: 6262509, sign: true });
+    data.append(FP8x23 { mag: 6298524, sign: true });
+    data.append(FP8x23 { mag: 17645284, sign: false });
+    data.append(FP8x23 { mag: 3955277, sign: false });
+    data.append(FP8x23 { mag: 3884818, sign: false });
+    data.append(FP8x23 { mag: 8962234, sign: false });
+    data.append(FP8x23 { mag: 2809372, sign: false });
+    data.append(FP8x23 { mag: 16598741, sign: false });
+    data.append(FP8x23 { mag: 6784943, sign: true });
+    data.append(FP8x23 { mag: 10628059, sign: true });
+    data.append(FP8x23 { mag: 21397334, sign: true });
+    data.append(FP8x23 { mag: 12467617, sign: true });
+    data.append(FP8x23 { mag: 15397510, sign: true });
+    data.append(FP8x23 { mag: 7715232, sign: true });
+    data.append(FP8x23 { mag: 5833417, sign: true });
+    data.append(FP8x23 { mag: 4660253, sign: false });
+    data.append(FP8x23 { mag: 5832772, sign: false });
+    data.append(FP8x23 { mag: 790398, sign: false });
+    data.append(FP8x23 { mag: 10411434, sign: true });
+    data.append(FP8x23 { mag: 10458644, sign: true });
+    data.append(FP8x23 { mag: 3224784, sign: true });
+    data.append(FP8x23 { mag: 7805680, sign: false });
+    data.append(FP8x23 { mag: 860368, sign: true });
+    data.append(FP8x23 { mag: 7115954, sign: true });
+    data.append(FP8x23 { mag: 3652584, sign: false });
+    data.append(FP8x23 { mag: 18110536, sign: false });
+    data.append(FP8x23 { mag: 8694257, sign: false });
+    data.append(FP8x23 { mag: 2467611, sign: true });
+    data.append(FP8x23 { mag: 1969761, sign: false });
+    data.append(FP8x23 { mag: 2403990, sign: true });
+    data.append(FP8x23 { mag: 1441612, sign: false });
+    data.append(FP8x23 { mag: 8264830, sign: false });
+    data.append(FP8x23 { mag: 23782516, sign: true });
+    data.append(FP8x23 { mag: 15570296, sign: false });
+    data.append(FP8x23 { mag: 1633225, sign: true });
+    data.append(FP8x23 { mag: 10890572, sign: true });
+    data.append(FP8x23 { mag: 1289518, sign: true });
+    data.append(FP8x23 { mag: 6855306, sign: false });
+    data.append(FP8x23 { mag: 29774126, sign: true });
+    data.append(FP8x23 { mag: 3640459, sign: false });
+    data.append(FP8x23 { mag: 5036650, sign: true });
+    data.append(FP8x23 { mag: 2896261, sign: false });
+    data.append(FP8x23 { mag: 10227605, sign: true });
+    data.append(FP8x23 { mag: 5740720, sign: true });
+    data.append(FP8x23 { mag: 12424902, sign: true });
+    data.append(FP8x23 { mag: 9161889, sign: false });
+    data.append(FP8x23 { mag: 8654373, sign: false });
+    data.append(FP8x23 { mag: 4483841, sign: false });
+    data.append(FP8x23 { mag: 15484597, sign: true });
+    data.append(FP8x23 { mag: 15284265, sign: true });
+    data.append(FP8x23 { mag: 6337630, sign: false });
+    data.append(FP8x23 { mag: 11568081, sign: true });
+    data.append(FP8x23 { mag: 5991922, sign: false });
+    data.append(FP8x23 { mag: 520230, sign: false });
+    data.append(FP8x23 { mag: 1312866, sign: true });
+    data.append(FP8x23 { mag: 2415229, sign: true });
+    data.append(FP8x23 { mag: 29673920, sign: false });
+    data.append(FP8x23 { mag: 10289541, sign: false });
+    data.append(FP8x23 { mag: 10450103, sign: false });
+    data.append(FP8x23 { mag: 9512570, sign: true });
+    data.append(FP8x23 { mag: 1628225, sign: true });
+    data.append(FP8x23 { mag: 6580971, sign: true });
+    data.append(FP8x23 { mag: 714841, sign: true });
+    data.append(FP8x23 { mag: 1800550, sign: true });
+    data.append(FP8x23 { mag: 3126343, sign: false });
+    data.append(FP8x23 { mag: 12778888, sign: false });
+    data.append(FP8x23 { mag: 2285159, sign: false });
+    data.append(FP8x23 { mag: 15225974, sign: false });
+    data.append(FP8x23 { mag: 672323, sign: false });
+    data.append(FP8x23 { mag: 16353744, sign: true });
+    data.append(FP8x23 { mag: 19120848, sign: true });
+    data.append(FP8x23 { mag: 10304800, sign: true });
+    data.append(FP8x23 { mag: 23183576, sign: true });
+    data.append(FP8x23 { mag: 8290095, sign: true });
+    data.append(FP8x23 { mag: 6100584, sign: true });
+    data.append(FP8x23 { mag: 12063426, sign: true });
+    data.append(FP8x23 { mag: 5926701, sign: false });
+    data.append(FP8x23 { mag: 1446250, sign: false });
+    data.append(FP8x23 { mag: 10444769, sign: true });
+    data.append(FP8x23 { mag: 5096127, sign: true });
+    data.append(FP8x23 { mag: 251532, sign: true });
+    data.append(FP8x23 { mag: 8553469, sign: false });
+    data.append(FP8x23 { mag: 8814550, sign: true });
+    data.append(FP8x23 { mag: 19636340, sign: true });
+    data.append(FP8x23 { mag: 3196814, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis2.cairo b/tests/nodes/layer_normalization_4d_axis2.cairo
new file mode 100644
index 000000000..27748871e
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis2.cairo
@@ -0,0 +1,32 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP8x23Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23TensorPartialEq;
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_4d_axis2() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1,
+            Option::Some(@input_2),
+            Option::Some(IntegerTrait::<i32>::new(2, false)),
+            Option::None,
+            Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_4d_axis2/input_0.cairo b/tests/nodes/layer_normalization_4d_axis2/input_0.cairo
new file mode 100644
index 000000000..d7913bb11
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis2/input_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 9853496, sign: true });
+    data.append(FP8x23 { mag: 12258403, sign: false });
+    data.append(FP8x23 { mag: 872656, sign: false });
+    data.append(FP8x23 { mag: 7388622, sign: true });
+    data.append(FP8x23 { mag: 15454325, sign: false });
+    data.append(FP8x23 { mag: 16251435, sign: false });
+    data.append(FP8x23 { mag: 903277, sign: true });
+    data.append(FP8x23 { mag: 3289794, sign: true });
+    data.append(FP8x23 { mag: 8057933, sign: false });
+    data.append(FP8x23 { mag: 6386388, sign: true });
+    data.append(FP8x23 { mag: 5708410, sign: true });
+    data.append(FP8x23 { mag: 4543373, sign: true });
+    data.append(FP8x23 { mag: 23644376, sign: false });
+    data.append(FP8x23 { mag: 7843321, sign: true });
+    data.append(FP8x23 { mag: 5801261, sign: false });
+    data.append(FP8x23 { mag: 5404517, sign: true });
+    data.append(FP8x23 { mag: 3421350, sign: false });
+    data.append(FP8x23 { mag: 2806284, sign: true });
+    data.append(FP8x23 { mag: 5479745, sign: false });
+    data.append(FP8x23 { mag: 9044852, sign: true });
+    data.append(FP8x23 { mag: 2879371, sign: true });
+    data.append(FP8x23 { mag: 7506722, sign: false });
+    data.append(FP8x23 { mag: 374708, sign: false });
+    data.append(FP8x23 { mag: 16088456, sign: false });
+    data.append(FP8x23 { mag: 7446071, sign: false });
+    data.append(FP8x23 { mag: 12333473, sign: true });
+    data.append(FP8x23 { mag: 2694855, sign: false });
+    data.append(FP8x23 { mag: 29333, sign: false });
+    data.append(FP8x23 { mag: 3210230, sign: true });
+    data.append(FP8x23 { mag: 246487, sign: false });
+    data.append(FP8x23 { mag: 11307238, sign: true });
+    data.append(FP8x23 { mag: 808074, sign: false });
+    data.append(FP8x23 { mag: 2244426, sign: false });
+    data.append(FP8x23 { mag: 4494036, sign: false });
+    data.append(FP8x23 { mag: 9168918, sign: false });
+    data.append(FP8x23 { mag: 11831318, sign: true });
+    data.append(FP8x23 { mag: 11514568, sign: true });
+    data.append(FP8x23 { mag: 3381120, sign: true });
+    data.append(FP8x23 { mag: 6827926, sign: true });
+    data.append(FP8x23 { mag: 2579494, sign: false });
+    data.append(FP8x23 { mag: 4493030, sign: true });
+    data.append(FP8x23 { mag: 4570125, sign: false });
+    data.append(FP8x23 { mag: 8010665, sign: false });
+    data.append(FP8x23 { mag: 5794037, sign: true });
+    data.append(FP8x23 { mag: 9849078, sign: false });
+    data.append(FP8x23 { mag: 11691798, sign: true });
+    data.append(FP8x23 { mag: 3652747, sign: false });
+    data.append(FP8x23 { mag: 1032666, sign: true });
+    data.append(FP8x23 { mag: 9259310, sign: true });
+    data.append(FP8x23 { mag: 7405492, sign: false });
+    data.append(FP8x23 { mag: 4687488, sign: true });
+    data.append(FP8x23 { mag: 1097650, sign: true });
+    data.append(FP8x23 { mag: 2213858, sign: true });
+    data.append(FP8x23 { mag: 1436205, sign: true });
+    data.append(FP8x23 { mag: 10226423, sign: false });
+    data.append(FP8x23 { mag: 6130226, sign: false });
+    data.append(FP8x23 { mag: 1214058, sign: false });
+    data.append(FP8x23 { mag: 12299984, sign: true });
+    data.append(FP8x23 { mag: 829240, sign: false });
+    data.append(FP8x23 { mag: 1612388, sign: false });
+    data.append(FP8x23 { mag: 6632529, sign: true });
+    data.append(FP8x23 { mag: 4410829, sign: true });
+    data.append(FP8x23 { mag: 20654302, sign: false });
+    data.append(FP8x23 { mag: 462475, sign: true });
+    data.append(FP8x23 { mag: 10502841, sign: true });
+    data.append(FP8x23 { mag: 7171902, sign: true });
+    data.append(FP8x23 { mag: 4204962, sign: false });
+    data.append(FP8x23 { mag: 17427142, sign: true });
+    data.append(FP8x23 { mag: 12555224, sign: true });
+    data.append(FP8x23 { mag: 8307885, sign: true });
+    data.append(FP8x23 { mag: 455536, sign: false });
+    data.append(FP8x23 { mag: 577191, sign: false });
+    data.append(FP8x23 { mag: 4412268, sign: true });
+    data.append(FP8x23 { mag: 15812229, sign: false });
+    data.append(FP8x23 { mag: 7098764, sign: true });
+    data.append(FP8x23 { mag: 9127468, sign: false });
+    data.append(FP8x23 { mag: 4753858, sign: false });
+    data.append(FP8x23 { mag: 2074029, sign: true });
+    data.append(FP8x23 { mag: 1651256, sign: false });
+    data.append(FP8x23 { mag: 9617324, sign: true });
+    data.append(FP8x23 { mag: 11400835, sign: false });
+    data.append(FP8x23 { mag: 4263073, sign: true });
+    data.append(FP8x23 { mag: 22170402, sign: false });
+    data.append(FP8x23 { mag: 7715608, sign: false });
+    data.append(FP8x23 { mag: 7511781, sign: true });
+    data.append(FP8x23 { mag: 8686402, sign: false });
+    data.append(FP8x23 { mag: 2710329, sign: true });
+    data.append(FP8x23 { mag: 5540998, sign: true });
+    data.append(FP8x23 { mag: 11608300, sign: true });
+    data.append(FP8x23 { mag: 3020404, sign: true });
+    data.append(FP8x23 { mag: 6342478, sign: true });
+    data.append(FP8x23 { mag: 9399735, sign: true });
+    data.append(FP8x23 { mag: 446463, sign: false });
+    data.append(FP8x23 { mag: 13691013, sign: true });
+    data.append(FP8x23 { mag: 11552903, sign: true });
+    data.append(FP8x23 { mag: 1204731, sign: false });
+    data.append(FP8x23 { mag: 1741592, sign: true });
+    data.append(FP8x23 { mag: 13103082, sign: false });
+    data.append(FP8x23 { mag: 3181444, sign: true });
+    data.append(FP8x23 { mag: 256975, sign: true });
+    data.append(FP8x23 { mag: 9440785, sign: true });
+    data.append(FP8x23 { mag: 2112590, sign: false });
+    data.append(FP8x23 { mag: 13404752, sign: false });
+    data.append(FP8x23 { mag: 760699, sign: false });
+    data.append(FP8x23 { mag: 1588793, sign: true });
+    data.append(FP8x23 { mag: 13026604, sign: true });
+    data.append(FP8x23 { mag: 452707, sign: false });
+    data.append(FP8x23 { mag: 7267348, sign: true });
+    data.append(FP8x23 { mag: 14737007, sign: false });
+    data.append(FP8x23 { mag: 8457998, sign: false });
+    data.append(FP8x23 { mag: 2233703, sign: false });
+    data.append(FP8x23 { mag: 3434673, sign: true });
+    data.append(FP8x23 { mag: 4280157, sign: true });
+    data.append(FP8x23 { mag: 2950181, sign: true });
+    data.append(FP8x23 { mag: 1385553, sign: false });
+    data.append(FP8x23 { mag: 17250056, sign: false });
+    data.append(FP8x23 { mag: 12716927, sign: true });
+    data.append(FP8x23 { mag: 2980452, sign: false });
+    data.append(FP8x23 { mag: 13031106, sign: true });
+    data.append(FP8x23 { mag: 4118717, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis2/input_1.cairo b/tests/nodes/layer_normalization_4d_axis2/input_1.cairo
new file mode 100644
index 000000000..73de56051
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis2/input_1.cairo
@@ -0,0 +1,33 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_1() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 16045555, sign: false });
+    data.append(FP8x23 { mag: 6797189, sign: true });
+    data.append(FP8x23 { mag: 338571, sign: true });
+    data.append(FP8x23 { mag: 14826208, sign: true });
+    data.append(FP8x23 { mag: 6612261, sign: false });
+    data.append(FP8x23 { mag: 2255963, sign: false });
+    data.append(FP8x23 { mag: 7694826, sign: false });
+    data.append(FP8x23 { mag: 8157877, sign: true });
+    data.append(FP8x23 { mag: 10027904, sign: true });
+    data.append(FP8x23 { mag: 4144258, sign: false });
+    data.append(FP8x23 { mag: 12368555, sign: true });
+    data.append(FP8x23 { mag: 1431810, sign: false });
+    data.append(FP8x23 { mag: 993247, sign: true });
+    data.append(FP8x23 { mag: 10015980, sign: true });
+    data.append(FP8x23 { mag: 11250731, sign: false });
+    data.append(FP8x23 { mag: 12224184, sign: true });
+    data.append(FP8x23 { mag: 14407597, sign: true });
+    data.append(FP8x23 { mag: 1255469, sign: true });
+    data.append(FP8x23 { mag: 48578, sign: true });
+    data.append(FP8x23 { mag: 14580561, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis2/input_2.cairo b/tests/nodes/layer_normalization_4d_axis2/input_2.cairo
new file mode 100644
index 000000000..5dd984a06
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis2/input_2.cairo
@@ -0,0 +1,33 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_2() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 10596986, sign: false });
+    data.append(FP8x23 { mag: 13797276, sign: true });
+    data.append(FP8x23 { mag: 726032, sign: true });
+    data.append(FP8x23 { mag: 2944650, sign: true });
+    data.append(FP8x23 { mag: 5288885, sign: true });
+    data.append(FP8x23 { mag: 12046768, sign: true });
+    data.append(FP8x23 { mag: 3375686, sign: false });
+    data.append(FP8x23 { mag: 8744354, sign: true });
+    data.append(FP8x23 { mag: 8940485, sign: false });
+    data.append(FP8x23 { mag: 6541405, sign: true });
+    data.append(FP8x23 { mag: 3256492, sign: false });
+    data.append(FP8x23 { mag: 6889087, sign: false });
+    data.append(FP8x23 { mag: 2560312, sign: true });
+    data.append(FP8x23 { mag: 9717397, sign: true });
+    data.append(FP8x23 { mag: 8774793, sign: true });
+    data.append(FP8x23 { mag: 893052, sign: true });
+    data.append(FP8x23 { mag: 7995400, sign: false });
+    data.append(FP8x23 { mag: 9505615, sign: true });
+    data.append(FP8x23 { mag: 541572, sign: false });
+    data.append(FP8x23 { mag: 13005167, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis2/output_0.cairo b/tests/nodes/layer_normalization_4d_axis2/output_0.cairo
new file mode 100644
index 000000000..b3de0fa1c
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis2/output_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn output_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 8703072, sign: true });
+    data.append(FP8x23 { mag: 21681156, sign: true });
+    data.append(FP8x23 { mag: 706829, sign: true });
+    data.append(FP8x23 { mag: 10983862, sign: false });
+    data.append(FP8x23 { mag: 4638530, sign: false });
+    data.append(FP8x23 { mag: 8467598, sign: true });
+    data.append(FP8x23 { mag: 1479070, sign: false });
+    data.append(FP8x23 { mag: 4653312, sign: true });
+    data.append(FP8x23 { mag: 1810198, sign: false });
+    data.append(FP8x23 { mag: 9990925, sign: true });
+    data.append(FP8x23 { mag: 12655580, sign: false });
+    data.append(FP8x23 { mag: 5979269, sign: false });
+    data.append(FP8x23 { mag: 4920753, sign: true });
+    data.append(FP8x23 { mag: 178767, sign: false });
+    data.append(FP8x23 { mag: 3487911, sign: true });
+    data.append(FP8x23 { mag: 7999385, sign: false });
+    data.append(FP8x23 { mag: 4888891, sign: false });
+    data.append(FP8x23 { mag: 8940880, sign: true });
+    data.append(FP8x23 { mag: 520413, sign: false });
+    data.append(FP8x23 { mag: 29283250, sign: true });
+    data.append(FP8x23 { mag: 5451075, sign: false });
+    data.append(FP8x23 { mag: 21054176, sign: true });
+    data.append(FP8x23 { mag: 764722, sign: true });
+    data.append(FP8x23 { mag: 35781404, sign: true });
+    data.append(FP8x23 { mag: 1716971, sign: false });
+    data.append(FP8x23 { mag: 15621259, sign: true });
+    data.append(FP8x23 { mag: 6641498, sign: false });
+    data.append(FP8x23 { mag: 9299975, sign: true });
+    data.append(FP8x23 { mag: 12600001, sign: false });
+    data.append(FP8x23 { mag: 6138847, sign: true });
+    data.append(FP8x23 { mag: 21157298, sign: false });
+    data.append(FP8x23 { mag: 7135652, sign: false });
+    data.append(FP8x23 { mag: 2922059, sign: true });
+    data.append(FP8x23 { mag: 16377204, sign: true });
+    data.append(FP8x23 { mag: 5736659, sign: false });
+    data.append(FP8x23 { mag: 17655176, sign: false });
+    data.append(FP8x23 { mag: 29246576, sign: false });
+    data.append(FP8x23 { mag: 9018773, sign: true });
+    data.append(FP8x23 { mag: 582792, sign: false });
+    data.append(FP8x23 { mag: 7041778, sign: true });
+    data.append(FP8x23 { mag: 361699, sign: true });
+    data.append(FP8x23 { mag: 18572216, sign: true });
+    data.append(FP8x23 { mag: 1141944, sign: true });
+    data.append(FP8x23 { mag: 10129914, sign: false });
+    data.append(FP8x23 { mag: 4692104, sign: false });
+    data.append(FP8x23 { mag: 16070120, sign: true });
+    data.append(FP8x23 { mag: 7702104, sign: false });
+    data.append(FP8x23 { mag: 7488068, sign: true });
+    data.append(FP8x23 { mag: 23095692, sign: false });
+    data.append(FP8x23 { mag: 1833849, sign: true });
+    data.append(FP8x23 { mag: 12071556, sign: false });
+    data.append(FP8x23 { mag: 6654369, sign: false });
+    data.append(FP8x23 { mag: 2228009, sign: true });
+    data.append(FP8x23 { mag: 7557105, sign: true });
+    data.append(FP8x23 { mag: 8856797, sign: false });
+    data.append(FP8x23 { mag: 12395714, sign: true });
+    data.append(FP8x23 { mag: 5265819, sign: false });
+    data.append(FP8x23 { mag: 7149849, sign: true });
+    data.append(FP8x23 { mag: 535227, sign: false });
+    data.append(FP8x23 { mag: 9354983, sign: true });
+    data.append(FP8x23 { mag: 1886297, sign: false });
+    data.append(FP8x23 { mag: 11759874, sign: true });
+    data.append(FP8x23 { mag: 1553244, sign: true });
+    data.append(FP8x23 { mag: 4906809, sign: true });
+    data.append(FP8x23 { mag: 11679092, sign: true });
+    data.append(FP8x23 { mag: 13404629, sign: true });
+    data.append(FP8x23 { mag: 8324403, sign: false });
+    data.append(FP8x23 { mag: 5321254, sign: false });
+    data.append(FP8x23 { mag: 20883912, sign: false });
+    data.append(FP8x23 { mag: 9551024, sign: true });
+    data.append(FP8x23 { mag: 377017, sign: false });
+    data.append(FP8x23 { mag: 7241483, sign: false });
+    data.append(FP8x23 { mag: 2262438, sign: true });
+    data.append(FP8x23 { mag: 28881548, sign: true });
+    data.append(FP8x23 { mag: 15456542, sign: true });
+    data.append(FP8x23 { mag: 15339766, sign: true });
+    data.append(FP8x23 { mag: 2135888, sign: true });
+    data.append(FP8x23 { mag: 9450355, sign: true });
+    data.append(FP8x23 { mag: 523906, sign: false });
+    data.append(FP8x23 { mag: 25683136, sign: true });
+    data.append(FP8x23 { mag: 32249876, sign: false });
+    data.append(FP8x23 { mag: 11198256, sign: true });
+    data.append(FP8x23 { mag: 1586058, sign: true });
+    data.append(FP8x23 { mag: 16911206, sign: true });
+    data.append(FP8x23 { mag: 10192207, sign: true });
+    data.append(FP8x23 { mag: 9679471, sign: true });
+    data.append(FP8x23 { mag: 1754443, sign: false });
+    data.append(FP8x23 { mag: 4472428, sign: true });
+    data.append(FP8x23 { mag: 20918500, sign: false });
+    data.append(FP8x23 { mag: 7556645, sign: true });
+    data.append(FP8x23 { mag: 10829383, sign: false });
+    data.append(FP8x23 { mag: 5528458, sign: false });
+    data.append(FP8x23 { mag: 2697706, sign: true });
+    data.append(FP8x23 { mag: 4552741, sign: false });
+    data.append(FP8x23 { mag: 22144528, sign: true });
+    data.append(FP8x23 { mag: 3608822, sign: true });
+    data.append(FP8x23 { mag: 9487844, sign: false });
+    data.append(FP8x23 { mag: 11436110, sign: true });
+    data.append(FP8x23 { mag: 554338, sign: false });
+    data.append(FP8x23 { mag: 12122246, sign: true });
+    data.append(FP8x23 { mag: 6229258, sign: true });
+    data.append(FP8x23 { mag: 15782264, sign: true });
+    data.append(FP8x23 { mag: 1268559, sign: true });
+    data.append(FP8x23 { mag: 4948465, sign: true });
+    data.append(FP8x23 { mag: 6197991, sign: true });
+    data.append(FP8x23 { mag: 15351219, sign: true });
+    data.append(FP8x23 { mag: 4140654, sign: false });
+    data.append(FP8x23 { mag: 2247070, sign: true });
+    data.append(FP8x23 { mag: 8678569, sign: true });
+    data.append(FP8x23 { mag: 2279577, sign: true });
+    data.append(FP8x23 { mag: 529337, sign: true });
+    data.append(FP8x23 { mag: 6385535, sign: false });
+    data.append(FP8x23 { mag: 2113548, sign: true });
+    data.append(FP8x23 { mag: 6758004, sign: true });
+    data.append(FP8x23 { mag: 6438428, sign: true });
+    data.append(FP8x23 { mag: 25935804, sign: true });
+    data.append(FP8x23 { mag: 28581364, sign: false });
+    data.append(FP8x23 { mag: 9998688, sign: true });
+    data.append(FP8x23 { mag: 612754, sign: false });
+    data.append(FP8x23 { mag: 19290370, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis3.cairo b/tests/nodes/layer_normalization_4d_axis3.cairo
new file mode 100644
index 000000000..2de09a8d6
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis3.cairo
@@ -0,0 +1,32 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP8x23Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23TensorPartialEq;
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_4d_axis3() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1,
+            Option::Some(@input_2),
+            Option::Some(IntegerTrait::<i32>::new(3, false)),
+            Option::None,
+            Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_4d_axis3/input_0.cairo b/tests/nodes/layer_normalization_4d_axis3/input_0.cairo
new file mode 100644
index 000000000..d7913bb11
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis3/input_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 9853496, sign: true });
+    data.append(FP8x23 { mag: 12258403, sign: false });
+    data.append(FP8x23 { mag: 872656, sign: false });
+    data.append(FP8x23 { mag: 7388622, sign: true });
+    data.append(FP8x23 { mag: 15454325, sign: false });
+    data.append(FP8x23 { mag: 16251435, sign: false });
+    data.append(FP8x23 { mag: 903277, sign: true });
+    data.append(FP8x23 { mag: 3289794, sign: true });
+    data.append(FP8x23 { mag: 8057933, sign: false });
+    data.append(FP8x23 { mag: 6386388, sign: true });
+    data.append(FP8x23 { mag: 5708410, sign: true });
+    data.append(FP8x23 { mag: 4543373, sign: true });
+    data.append(FP8x23 { mag: 23644376, sign: false });
+    data.append(FP8x23 { mag: 7843321, sign: true });
+    data.append(FP8x23 { mag: 5801261, sign: false });
+    data.append(FP8x23 { mag: 5404517, sign: true });
+    data.append(FP8x23 { mag: 3421350, sign: false });
+    data.append(FP8x23 { mag: 2806284, sign: true });
+    data.append(FP8x23 { mag: 5479745, sign: false });
+    data.append(FP8x23 { mag: 9044852, sign: true });
+    data.append(FP8x23 { mag: 2879371, sign: true });
+    data.append(FP8x23 { mag: 7506722, sign: false });
+    data.append(FP8x23 { mag: 374708, sign: false });
+    data.append(FP8x23 { mag: 16088456, sign: false });
+    data.append(FP8x23 { mag: 7446071, sign: false });
+    data.append(FP8x23 { mag: 12333473, sign: true });
+    data.append(FP8x23 { mag: 2694855, sign: false });
+    data.append(FP8x23 { mag: 29333, sign: false });
+    data.append(FP8x23 { mag: 3210230, sign: true });
+    data.append(FP8x23 { mag: 246487, sign: false });
+    data.append(FP8x23 { mag: 11307238, sign: true });
+    data.append(FP8x23 { mag: 808074, sign: false });
+    data.append(FP8x23 { mag: 2244426, sign: false });
+    data.append(FP8x23 { mag: 4494036, sign: false });
+    data.append(FP8x23 { mag: 9168918, sign: false });
+    data.append(FP8x23 { mag: 11831318, sign: true });
+    data.append(FP8x23 { mag: 11514568, sign: true });
+    data.append(FP8x23 { mag: 3381120, sign: true });
+    data.append(FP8x23 { mag: 6827926, sign: true });
+    data.append(FP8x23 { mag: 2579494, sign: false });
+    data.append(FP8x23 { mag: 4493030, sign: true });
+    data.append(FP8x23 { mag: 4570125, sign: false });
+    data.append(FP8x23 { mag: 8010665, sign: false });
+    data.append(FP8x23 { mag: 5794037, sign: true });
+    data.append(FP8x23 { mag: 9849078, sign: false });
+    data.append(FP8x23 { mag: 11691798, sign: true });
+    data.append(FP8x23 { mag: 3652747, sign: false });
+    data.append(FP8x23 { mag: 1032666, sign: true });
+    data.append(FP8x23 { mag: 9259310, sign: true });
+    data.append(FP8x23 { mag: 7405492, sign: false });
+    data.append(FP8x23 { mag: 4687488, sign: true });
+    data.append(FP8x23 { mag: 1097650, sign: true });
+    data.append(FP8x23 { mag: 2213858, sign: true });
+    data.append(FP8x23 { mag: 1436205, sign: true });
+    data.append(FP8x23 { mag: 10226423, sign: false });
+    data.append(FP8x23 { mag: 6130226, sign: false });
+    data.append(FP8x23 { mag: 1214058, sign: false });
+    data.append(FP8x23 { mag: 12299984, sign: true });
+    data.append(FP8x23 { mag: 829240, sign: false });
+    data.append(FP8x23 { mag: 1612388, sign: false });
+    data.append(FP8x23 { mag: 6632529, sign: true });
+    data.append(FP8x23 { mag: 4410829, sign: true });
+    data.append(FP8x23 { mag: 20654302, sign: false });
+    data.append(FP8x23 { mag: 462475, sign: true });
+    data.append(FP8x23 { mag: 10502841, sign: true });
+    data.append(FP8x23 { mag: 7171902, sign: true });
+    data.append(FP8x23 { mag: 4204962, sign: false });
+    data.append(FP8x23 { mag: 17427142, sign: true });
+    data.append(FP8x23 { mag: 12555224, sign: true });
+    data.append(FP8x23 { mag: 8307885, sign: true });
+    data.append(FP8x23 { mag: 455536, sign: false });
+    data.append(FP8x23 { mag: 577191, sign: false });
+    data.append(FP8x23 { mag: 4412268, sign: true });
+    data.append(FP8x23 { mag: 15812229, sign: false });
+    data.append(FP8x23 { mag: 7098764, sign: true });
+    data.append(FP8x23 { mag: 9127468, sign: false });
+    data.append(FP8x23 { mag: 4753858, sign: false });
+    data.append(FP8x23 { mag: 2074029, sign: true });
+    data.append(FP8x23 { mag: 1651256, sign: false });
+    data.append(FP8x23 { mag: 9617324, sign: true });
+    data.append(FP8x23 { mag: 11400835, sign: false });
+    data.append(FP8x23 { mag: 4263073, sign: true });
+    data.append(FP8x23 { mag: 22170402, sign: false });
+    data.append(FP8x23 { mag: 7715608, sign: false });
+    data.append(FP8x23 { mag: 7511781, sign: true });
+    data.append(FP8x23 { mag: 8686402, sign: false });
+    data.append(FP8x23 { mag: 2710329, sign: true });
+    data.append(FP8x23 { mag: 5540998, sign: true });
+    data.append(FP8x23 { mag: 11608300, sign: true });
+    data.append(FP8x23 { mag: 3020404, sign: true });
+    data.append(FP8x23 { mag: 6342478, sign: true });
+    data.append(FP8x23 { mag: 9399735, sign: true });
+    data.append(FP8x23 { mag: 446463, sign: false });
+    data.append(FP8x23 { mag: 13691013, sign: true });
+    data.append(FP8x23 { mag: 11552903, sign: true });
+    data.append(FP8x23 { mag: 1204731, sign: false });
+    data.append(FP8x23 { mag: 1741592, sign: true });
+    data.append(FP8x23 { mag: 13103082, sign: false });
+    data.append(FP8x23 { mag: 3181444, sign: true });
+    data.append(FP8x23 { mag: 256975, sign: true });
+    data.append(FP8x23 { mag: 9440785, sign: true });
+    data.append(FP8x23 { mag: 2112590, sign: false });
+    data.append(FP8x23 { mag: 13404752, sign: false });
+    data.append(FP8x23 { mag: 760699, sign: false });
+    data.append(FP8x23 { mag: 1588793, sign: true });
+    data.append(FP8x23 { mag: 13026604, sign: true });
+    data.append(FP8x23 { mag: 452707, sign: false });
+    data.append(FP8x23 { mag: 7267348, sign: true });
+    data.append(FP8x23 { mag: 14737007, sign: false });
+    data.append(FP8x23 { mag: 8457998, sign: false });
+    data.append(FP8x23 { mag: 2233703, sign: false });
+    data.append(FP8x23 { mag: 3434673, sign: true });
+    data.append(FP8x23 { mag: 4280157, sign: true });
+    data.append(FP8x23 { mag: 2950181, sign: true });
+    data.append(FP8x23 { mag: 1385553, sign: false });
+    data.append(FP8x23 { mag: 17250056, sign: false });
+    data.append(FP8x23 { mag: 12716927, sign: true });
+    data.append(FP8x23 { mag: 2980452, sign: false });
+    data.append(FP8x23 { mag: 13031106, sign: true });
+    data.append(FP8x23 { mag: 4118717, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis3/input_1.cairo b/tests/nodes/layer_normalization_4d_axis3/input_1.cairo
new file mode 100644
index 000000000..c4f5b5a1b
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis3/input_1.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_1() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 6200252, sign: false });
+    data.append(FP8x23 { mag: 4645727, sign: true });
+    data.append(FP8x23 { mag: 18131524, sign: false });
+    data.append(FP8x23 { mag: 9670945, sign: true });
+    data.append(FP8x23 { mag: 4399430, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis3/input_2.cairo b/tests/nodes/layer_normalization_4d_axis3/input_2.cairo
new file mode 100644
index 000000000..6dac76016
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis3/input_2.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_2() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 538440, sign: false });
+    data.append(FP8x23 { mag: 3468236, sign: false });
+    data.append(FP8x23 { mag: 8790979, sign: false });
+    data.append(FP8x23 { mag: 32869270, sign: false });
+    data.append(FP8x23 { mag: 630553, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis3/output_0.cairo b/tests/nodes/layer_normalization_4d_axis3/output_0.cairo
new file mode 100644
index 000000000..9be521f05
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis3/output_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn output_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 6861451, sign: true });
+    data.append(FP8x23 { mag: 1101011, sign: true });
+    data.append(FP8x23 { mag: 6298939, sign: false });
+    data.append(FP8x23 { mag: 42064444, sign: false });
+    data.append(FP8x23 { mag: 6341855, sign: false });
+    data.append(FP8x23 { mag: 10637301, sign: false });
+    data.append(FP8x23 { mag: 5512855, sign: false });
+    data.append(FP8x23 { mag: 4407421, sign: true });
+    data.append(FP8x23 { mag: 26673762, sign: false });
+    data.append(FP8x23 { mag: 4214892, sign: true });
+    data.append(FP8x23 { mag: 3698203, sign: true });
+    data.append(FP8x23 { mag: 6179134, sign: false });
+    data.append(FP8x23 { mag: 41981648, sign: false });
+    data.append(FP8x23 { mag: 41245676, sign: false });
+    data.append(FP8x23 { mag: 1961019, sign: false });
+    data.append(FP8x23 { mag: 3735727, sign: true });
+    data.append(FP8x23 { mag: 899718, sign: true });
+    data.append(FP8x23 { mag: 4990078, sign: false });
+    data.append(FP8x23 { mag: 20101102, sign: false });
+    data.append(FP8x23 { mag: 5359211, sign: true });
+    data.append(FP8x23 { mag: 7557954, sign: true });
+    data.append(FP8x23 { mag: 2196963, sign: false });
+    data.append(FP8x23 { mag: 5912856, sign: true });
+    data.append(FP8x23 { mag: 17601684, sign: false });
+    data.append(FP8x23 { mag: 1793852, sign: false });
+    data.append(FP8x23 { mag: 11044257, sign: true });
+    data.append(FP8x23 { mag: 1136295, sign: true });
+    data.append(FP8x23 { mag: 17566640, sign: false });
+    data.append(FP8x23 { mag: 34149192, sign: false });
+    data.append(FP8x23 { mag: 2941641, sign: false });
+    data.append(FP8x23 { mag: 10739165, sign: true });
+    data.append(FP8x23 { mag: 3654829, sign: false });
+    data.append(FP8x23 { mag: 11886320, sign: false });
+    data.append(FP8x23 { mag: 28024164, sign: false });
+    data.append(FP8x23 { mag: 5854203, sign: false });
+    data.append(FP8x23 { mag: 5942399, sign: true });
+    data.append(FP8x23 { mag: 8051302, sign: false });
+    data.append(FP8x23 { mag: 18253056, sign: false });
+    data.append(FP8x23 { mag: 34004268, sign: false });
+    data.append(FP8x23 { mag: 7789614, sign: false });
+    data.append(FP8x23 { mag: 6142160, sign: true });
+    data.append(FP8x23 { mag: 1919472, sign: false });
+    data.append(FP8x23 { mag: 24546494, sign: false });
+    data.append(FP8x23 { mag: 45248064, sign: false });
+    data.append(FP8x23 { mag: 5712510, sign: false });
+    data.append(FP8x23 { mag: 7515486, sign: true });
+    data.append(FP8x23 { mag: 237511, sign: true });
+    data.append(FP8x23 { mag: 11646082, sign: false });
+    data.append(FP8x23 { mag: 42217200, sign: false });
+    data.append(FP8x23 { mag: 6395711, sign: false });
+    data.append(FP8x23 { mag: 5252277, sign: true });
+    data.append(FP8x23 { mag: 4592765, sign: false });
+    data.append(FP8x23 { mag: 501418, sign: false });
+    data.append(FP8x23 { mag: 35841232, sign: false });
+    data.append(FP8x23 { mag: 9167666, sign: false });
+    data.append(FP8x23 { mag: 7168818, sign: false });
+    data.append(FP8x23 { mag: 2182334, sign: false });
+    data.append(FP8x23 { mag: 25693876, sign: true });
+    data.append(FP8x23 { mag: 30792410, sign: false });
+    data.append(FP8x23 { mag: 2130807, sign: false });
+    data.append(FP8x23 { mag: 3061656, sign: true });
+    data.append(FP8x23 { mag: 5223668, sign: false });
+    data.append(FP8x23 { mag: 43419840, sign: false });
+    data.append(FP8x23 { mag: 33038392, sign: false });
+    data.append(FP8x23 { mag: 3478012, sign: true });
+    data.append(FP8x23 { mag: 1468348, sign: false });
+    data.append(FP8x23 { mag: 4571455, sign: true });
+    data.append(FP8x23 { mag: 14322581, sign: true });
+    data.append(FP8x23 { mag: 38651728, sign: false });
+    data.append(FP8x23 { mag: 596053, sign: false });
+    data.append(FP8x23 { mag: 60762, sign: false });
+    data.append(FP8x23 { mag: 3754916, sign: false });
+    data.append(FP8x23 { mag: 3730281, sign: true });
+    data.append(FP8x23 { mag: 14895674, sign: false });
+    data.append(FP8x23 { mag: 3897275, sign: true });
+    data.append(FP8x23 { mag: 8682959, sign: false });
+    data.append(FP8x23 { mag: 558598, sign: false });
+    data.append(FP8x23 { mag: 692740, sign: false });
+    data.append(FP8x23 { mag: 31527354, sign: false });
+    data.append(FP8x23 { mag: 6549321, sign: true });
+    data.append(FP8x23 { mag: 3699828, sign: false });
+    data.append(FP8x23 { mag: 7847593, sign: false });
+    data.append(FP8x23 { mag: 36143524, sign: false });
+    data.append(FP8x23 { mag: 31243174, sign: false });
+    data.append(FP8x23 { mag: 4841996, sign: true });
+    data.append(FP8x23 { mag: 11383225, sign: false });
+    data.append(FP8x23 { mag: 3377710, sign: false });
+    data.append(FP8x23 { mag: 1355152, sign: false });
+    data.append(FP8x23 { mag: 45740296, sign: false });
+    data.append(FP8x23 { mag: 509252, sign: false });
+    data.append(FP8x23 { mag: 2763536, sign: false });
+    data.append(FP8x23 { mag: 4688158, sign: false });
+    data.append(FP8x23 { mag: 40319700, sign: false });
+    data.append(FP8x23 { mag: 43844796, sign: false });
+    data.append(FP8x23 { mag: 2450252, sign: true });
+    data.append(FP8x23 { mag: 122285, sign: true });
+    data.append(FP8x23 { mag: 6312799, sign: false });
+    data.append(FP8x23 { mag: 43889444, sign: false });
+    data.append(FP8x23 { mag: 41180916, sign: false });
+    data.append(FP8x23 { mag: 942084, sign: true });
+    data.append(FP8x23 { mag: 8298549, sign: true });
+    data.append(FP8x23 { mag: 2797356, sign: false });
+    data.append(FP8x23 { mag: 39226368, sign: false });
+    data.append(FP8x23 { mag: 33248984, sign: false });
+    data.append(FP8x23 { mag: 946515, sign: true });
+    data.append(FP8x23 { mag: 7877337, sign: true });
+    data.append(FP8x23 { mag: 3568616, sign: false });
+    data.append(FP8x23 { mag: 5471631, sign: true });
+    data.append(FP8x23 { mag: 19389094, sign: false });
+    data.append(FP8x23 { mag: 4025465, sign: false });
+    data.append(FP8x23 { mag: 8978928, sign: false });
+    data.append(FP8x23 { mag: 6984708, sign: false });
+    data.append(FP8x23 { mag: 10661943, sign: true });
+    data.append(FP8x23 { mag: 38438520, sign: false });
+    data.append(FP8x23 { mag: 5225164, sign: false });
+    data.append(FP8x23 { mag: 11078083, sign: false });
+    data.append(FP8x23 { mag: 7911388, sign: false });
+    data.append(FP8x23 { mag: 16678516, sign: false });
+    data.append(FP8x23 { mag: 42387840, sign: false });
+    data.append(FP8x23 { mag: 224042, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_1.cairo b/tests/nodes/layer_normalization_4d_axis_negative_1.cairo
new file mode 100644
index 000000000..693dc4311
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_1.cairo
@@ -0,0 +1,32 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP8x23Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23TensorPartialEq;
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_4d_axis_negative_1() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1,
+            Option::Some(@input_2),
+            Option::Some(IntegerTrait::<i32>::new(1, true)),
+            Option::None,
+            Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_1/input_0.cairo b/tests/nodes/layer_normalization_4d_axis_negative_1/input_0.cairo
new file mode 100644
index 000000000..d7913bb11
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_1/input_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 9853496, sign: true });
+    data.append(FP8x23 { mag: 12258403, sign: false });
+    data.append(FP8x23 { mag: 872656, sign: false });
+    data.append(FP8x23 { mag: 7388622, sign: true });
+    data.append(FP8x23 { mag: 15454325, sign: false });
+    data.append(FP8x23 { mag: 16251435, sign: false });
+    data.append(FP8x23 { mag: 903277, sign: true });
+    data.append(FP8x23 { mag: 3289794, sign: true });
+    data.append(FP8x23 { mag: 8057933, sign: false });
+    data.append(FP8x23 { mag: 6386388, sign: true });
+    data.append(FP8x23 { mag: 5708410, sign: true });
+    data.append(FP8x23 { mag: 4543373, sign: true });
+    data.append(FP8x23 { mag: 23644376, sign: false });
+    data.append(FP8x23 { mag: 7843321, sign: true });
+    data.append(FP8x23 { mag: 5801261, sign: false });
+    data.append(FP8x23 { mag: 5404517, sign: true });
+    data.append(FP8x23 { mag: 3421350, sign: false });
+    data.append(FP8x23 { mag: 2806284, sign: true });
+    data.append(FP8x23 { mag: 5479745, sign: false });
+    data.append(FP8x23 { mag: 9044852, sign: true });
+    data.append(FP8x23 { mag: 2879371, sign: true });
+    data.append(FP8x23 { mag: 7506722, sign: false });
+    data.append(FP8x23 { mag: 374708, sign: false });
+    data.append(FP8x23 { mag: 16088456, sign: false });
+    data.append(FP8x23 { mag: 7446071, sign: false });
+    data.append(FP8x23 { mag: 12333473, sign: true });
+    data.append(FP8x23 { mag: 2694855, sign: false });
+    data.append(FP8x23 { mag: 29333, sign: false });
+    data.append(FP8x23 { mag: 3210230, sign: true });
+    data.append(FP8x23 { mag: 246487, sign: false });
+    data.append(FP8x23 { mag: 11307238, sign: true });
+    data.append(FP8x23 { mag: 808074, sign: false });
+    data.append(FP8x23 { mag: 2244426, sign: false });
+    data.append(FP8x23 { mag: 4494036, sign: false });
+    data.append(FP8x23 { mag: 9168918, sign: false });
+    data.append(FP8x23 { mag: 11831318, sign: true });
+    data.append(FP8x23 { mag: 11514568, sign: true });
+    data.append(FP8x23 { mag: 3381120, sign: true });
+    data.append(FP8x23 { mag: 6827926, sign: true });
+    data.append(FP8x23 { mag: 2579494, sign: false });
+    data.append(FP8x23 { mag: 4493030, sign: true });
+    data.append(FP8x23 { mag: 4570125, sign: false });
+    data.append(FP8x23 { mag: 8010665, sign: false });
+    data.append(FP8x23 { mag: 5794037, sign: true });
+    data.append(FP8x23 { mag: 9849078, sign: false });
+    data.append(FP8x23 { mag: 11691798, sign: true });
+    data.append(FP8x23 { mag: 3652747, sign: false });
+    data.append(FP8x23 { mag: 1032666, sign: true });
+    data.append(FP8x23 { mag: 9259310, sign: true });
+    data.append(FP8x23 { mag: 7405492, sign: false });
+    data.append(FP8x23 { mag: 4687488, sign: true });
+    data.append(FP8x23 { mag: 1097650, sign: true });
+    data.append(FP8x23 { mag: 2213858, sign: true });
+    data.append(FP8x23 { mag: 1436205, sign: true });
+    data.append(FP8x23 { mag: 10226423, sign: false });
+    data.append(FP8x23 { mag: 6130226, sign: false });
+    data.append(FP8x23 { mag: 1214058, sign: false });
+    data.append(FP8x23 { mag: 12299984, sign: true });
+    data.append(FP8x23 { mag: 829240, sign: false });
+    data.append(FP8x23 { mag: 1612388, sign: false });
+    data.append(FP8x23 { mag: 6632529, sign: true });
+    data.append(FP8x23 { mag: 4410829, sign: true });
+    data.append(FP8x23 { mag: 20654302, sign: false });
+    data.append(FP8x23 { mag: 462475, sign: true });
+    data.append(FP8x23 { mag: 10502841, sign: true });
+    data.append(FP8x23 { mag: 7171902, sign: true });
+    data.append(FP8x23 { mag: 4204962, sign: false });
+    data.append(FP8x23 { mag: 17427142, sign: true });
+    data.append(FP8x23 { mag: 12555224, sign: true });
+    data.append(FP8x23 { mag: 8307885, sign: true });
+    data.append(FP8x23 { mag: 455536, sign: false });
+    data.append(FP8x23 { mag: 577191, sign: false });
+    data.append(FP8x23 { mag: 4412268, sign: true });
+    data.append(FP8x23 { mag: 15812229, sign: false });
+    data.append(FP8x23 { mag: 7098764, sign: true });
+    data.append(FP8x23 { mag: 9127468, sign: false });
+    data.append(FP8x23 { mag: 4753858, sign: false });
+    data.append(FP8x23 { mag: 2074029, sign: true });
+    data.append(FP8x23 { mag: 1651256, sign: false });
+    data.append(FP8x23 { mag: 9617324, sign: true });
+    data.append(FP8x23 { mag: 11400835, sign: false });
+    data.append(FP8x23 { mag: 4263073, sign: true });
+    data.append(FP8x23 { mag: 22170402, sign: false });
+    data.append(FP8x23 { mag: 7715608, sign: false });
+    data.append(FP8x23 { mag: 7511781, sign: true });
+    data.append(FP8x23 { mag: 8686402, sign: false });
+    data.append(FP8x23 { mag: 2710329, sign: true });
+    data.append(FP8x23 { mag: 5540998, sign: true });
+    data.append(FP8x23 { mag: 11608300, sign: true });
+    data.append(FP8x23 { mag: 3020404, sign: true });
+    data.append(FP8x23 { mag: 6342478, sign: true });
+    data.append(FP8x23 { mag: 9399735, sign: true });
+    data.append(FP8x23 { mag: 446463, sign: false });
+    data.append(FP8x23 { mag: 13691013, sign: true });
+    data.append(FP8x23 { mag: 11552903, sign: true });
+    data.append(FP8x23 { mag: 1204731, sign: false });
+    data.append(FP8x23 { mag: 1741592, sign: true });
+    data.append(FP8x23 { mag: 13103082, sign: false });
+    data.append(FP8x23 { mag: 3181444, sign: true });
+    data.append(FP8x23 { mag: 256975, sign: true });
+    data.append(FP8x23 { mag: 9440785, sign: true });
+    data.append(FP8x23 { mag: 2112590, sign: false });
+    data.append(FP8x23 { mag: 13404752, sign: false });
+    data.append(FP8x23 { mag: 760699, sign: false });
+    data.append(FP8x23 { mag: 1588793, sign: true });
+    data.append(FP8x23 { mag: 13026604, sign: true });
+    data.append(FP8x23 { mag: 452707, sign: false });
+    data.append(FP8x23 { mag: 7267348, sign: true });
+    data.append(FP8x23 { mag: 14737007, sign: false });
+    data.append(FP8x23 { mag: 8457998, sign: false });
+    data.append(FP8x23 { mag: 2233703, sign: false });
+    data.append(FP8x23 { mag: 3434673, sign: true });
+    data.append(FP8x23 { mag: 4280157, sign: true });
+    data.append(FP8x23 { mag: 2950181, sign: true });
+    data.append(FP8x23 { mag: 1385553, sign: false });
+    data.append(FP8x23 { mag: 17250056, sign: false });
+    data.append(FP8x23 { mag: 12716927, sign: true });
+    data.append(FP8x23 { mag: 2980452, sign: false });
+    data.append(FP8x23 { mag: 13031106, sign: true });
+    data.append(FP8x23 { mag: 4118717, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_1/input_1.cairo b/tests/nodes/layer_normalization_4d_axis_negative_1/input_1.cairo
new file mode 100644
index 000000000..51b37b20c
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_1/input_1.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_1() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 7892951, sign: true });
+    data.append(FP8x23 { mag: 7153170, sign: false });
+    data.append(FP8x23 { mag: 6305733, sign: false });
+    data.append(FP8x23 { mag: 6298263, sign: true });
+    data.append(FP8x23 { mag: 924383, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_1/input_2.cairo b/tests/nodes/layer_normalization_4d_axis_negative_1/input_2.cairo
new file mode 100644
index 000000000..3fb4ef013
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_1/input_2.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_2() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 9596129, sign: false });
+    data.append(FP8x23 { mag: 8524695, sign: false });
+    data.append(FP8x23 { mag: 8030491, sign: true });
+    data.append(FP8x23 { mag: 8640310, sign: false });
+    data.append(FP8x23 { mag: 12854812, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_1/output_0.cairo b/tests/nodes/layer_normalization_4d_axis_negative_1/output_0.cairo
new file mode 100644
index 000000000..5b69011e6
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_1/output_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn output_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 19016228, sign: false });
+    data.append(FP8x23 { mag: 15560107, sign: false });
+    data.append(FP8x23 { mag: 8897166, sign: true });
+    data.append(FP8x23 { mag: 14628724, sign: false });
+    data.append(FP8x23 { mag: 14054839, sign: false });
+    data.append(FP8x23 { mag: 3259770, sign: true });
+    data.append(FP8x23 { mag: 5376533, sign: false });
+    data.append(FP8x23 { mag: 12620596, sign: true });
+    data.append(FP8x23 { mag: 4605447, sign: false });
+    data.append(FP8x23 { mag: 11836714, sign: false });
+    data.append(FP8x23 { mag: 14989397, sign: false });
+    data.append(FP8x23 { mag: 4350642, sign: false });
+    data.append(FP8x23 { mag: 3512472, sign: false });
+    data.append(FP8x23 { mag: 14095496, sign: false });
+    data.append(FP8x23 { mag: 13134362, sign: false });
+    data.append(FP8x23 { mag: 15037165, sign: false });
+    data.append(FP8x23 { mag: 15250170, sign: false });
+    data.append(FP8x23 { mag: 9352358, sign: true });
+    data.append(FP8x23 { mag: 324961, sign: false });
+    data.append(FP8x23 { mag: 11596276, sign: false });
+    data.append(FP8x23 { mag: 19902878, sign: false });
+    data.append(FP8x23 { mag: 10482114, sign: false });
+    data.append(FP8x23 { mag: 13144152, sign: true });
+    data.append(FP8x23 { mag: 1302801, sign: true });
+    data.append(FP8x23 { mag: 13099238, sign: false });
+    data.append(FP8x23 { mag: 24340960, sign: false });
+    data.append(FP8x23 { mag: 15614434, sign: false });
+    data.append(FP8x23 { mag: 4978514, sign: true });
+    data.append(FP8x23 { mag: 9473866, sign: false });
+    data.append(FP8x23 { mag: 13340405, sign: false });
+    data.append(FP8x23 { mag: 23952576, sign: false });
+    data.append(FP8x23 { mag: 8237391, sign: false });
+    data.append(FP8x23 { mag: 6954001, sign: true });
+    data.append(FP8x23 { mag: 5484904, sign: false });
+    data.append(FP8x23 { mag: 13952376, sign: false });
+    data.append(FP8x23 { mag: 17846270, sign: false });
+    data.append(FP8x23 { mag: 1468006, sign: false });
+    data.append(FP8x23 { mag: 4739795, sign: true });
+    data.append(FP8x23 { mag: 9379485, sign: false });
+    data.append(FP8x23 { mag: 14359034, sign: false });
+    data.append(FP8x23 { mag: 18100568, sign: false });
+    data.append(FP8x23 { mag: 10909375, sign: false });
+    data.append(FP8x23 { mag: 2551079, sign: true });
+    data.append(FP8x23 { mag: 16702075, sign: false });
+    data.append(FP8x23 { mag: 13922604, sign: false });
+    data.append(FP8x23 { mag: 19848816, sign: false });
+    data.append(FP8x23 { mag: 14230550, sign: false });
+    data.append(FP8x23 { mag: 7037550, sign: true });
+    data.append(FP8x23 { mag: 14728206, sign: false });
+    data.append(FP8x23 { mag: 14066155, sign: false });
+    data.append(FP8x23 { mag: 16967740, sign: false });
+    data.append(FP8x23 { mag: 6793223, sign: false });
+    data.append(FP8x23 { mag: 10913412, sign: true });
+    data.append(FP8x23 { mag: 10575819, sign: false });
+    data.append(FP8x23 { mag: 14648582, sign: false });
+    data.append(FP8x23 { mag: 1155626, sign: false });
+    data.append(FP8x23 { mag: 10504639, sign: false });
+    data.append(FP8x23 { mag: 20023544, sign: true });
+    data.append(FP8x23 { mag: 7287742, sign: false });
+    data.append(FP8x23 { mag: 13170037, sign: false });
+    data.append(FP8x23 { mag: 14179070, sign: false });
+    data.append(FP8x23 { mag: 5821802, sign: false });
+    data.append(FP8x23 { mag: 4012642, sign: false });
+    data.append(FP8x23 { mag: 8750452, sign: false });
+    data.append(FP8x23 { mag: 11991543, sign: false });
+    data.append(FP8x23 { mag: 8412352, sign: false });
+    data.append(FP8x23 { mag: 20903656, sign: false });
+    data.append(FP8x23 { mag: 16068864, sign: true });
+    data.append(FP8x23 { mag: 12406173, sign: false });
+    data.append(FP8x23 { mag: 12847563, sign: false });
+    data.append(FP8x23 { mag: 10204216, sign: false });
+    data.append(FP8x23 { mag: 8083285, sign: false });
+    data.append(FP8x23 { mag: 12385102, sign: true });
+    data.append(FP8x23 { mag: 3065106, sign: true });
+    data.append(FP8x23 { mag: 11903450, sign: false });
+    data.append(FP8x23 { mag: 771882, sign: true });
+    data.append(FP8x23 { mag: 13004755, sign: false });
+    data.append(FP8x23 { mag: 10846875, sign: true });
+    data.append(FP8x23 { mag: 7766379, sign: false });
+    data.append(FP8x23 { mag: 11346216, sign: false });
+    data.append(FP8x23 { mag: 5571666, sign: false });
+    data.append(FP8x23 { mag: 1781663, sign: false });
+    data.append(FP8x23 { mag: 1482105, sign: false });
+    data.append(FP8x23 { mag: 7581305, sign: false });
+    data.append(FP8x23 { mag: 11704950, sign: false });
+    data.append(FP8x23 { mag: 4209335, sign: true });
+    data.append(FP8x23 { mag: 8664081, sign: false });
+    data.append(FP8x23 { mag: 10616503, sign: true });
+    data.append(FP8x23 { mag: 17022648, sign: false });
+    data.append(FP8x23 { mag: 12829325, sign: false });
+    data.append(FP8x23 { mag: 6763571, sign: false });
+    data.append(FP8x23 { mag: 6646343, sign: false });
+    data.append(FP8x23 { mag: 2934484, sign: false });
+    data.append(FP8x23 { mag: 15788190, sign: false });
+    data.append(FP8x23 { mag: 12207490, sign: false });
+    data.append(FP8x23 { mag: 10437236, sign: false });
+    data.append(FP8x23 { mag: 4144833, sign: false });
+    data.append(FP8x23 { mag: 4175960, sign: false });
+    data.append(FP8x23 { mag: 14053320, sign: false });
+    data.append(FP8x23 { mag: 12524378, sign: false });
+    data.append(FP8x23 { mag: 20845660, sign: false });
+    data.append(FP8x23 { mag: 9557670, sign: false });
+    data.append(FP8x23 { mag: 2554248, sign: false });
+    data.append(FP8x23 { mag: 8887601, sign: false });
+    data.append(FP8x23 { mag: 12523447, sign: false });
+    data.append(FP8x23 { mag: 20309456, sign: false });
+    data.append(FP8x23 { mag: 8370136, sign: false });
+    data.append(FP8x23 { mag: 12990704, sign: true });
+    data.append(FP8x23 { mag: 138738, sign: true });
+    data.append(FP8x23 { mag: 13568132, sign: false });
+    data.append(FP8x23 { mag: 1148653, sign: true });
+    data.append(FP8x23 { mag: 3110274, sign: false });
+    data.append(FP8x23 { mag: 14795777, sign: true });
+    data.append(FP8x23 { mag: 12267317, sign: false });
+    data.append(FP8x23 { mag: 13820206, sign: false });
+    data.append(FP8x23 { mag: 3820888, sign: true });
+    data.append(FP8x23 { mag: 1683436, sign: false });
+    data.append(FP8x23 { mag: 5287384, sign: true });
+    data.append(FP8x23 { mag: 14839339, sign: false });
+    data.append(FP8x23 { mag: 12675249, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_2.cairo b/tests/nodes/layer_normalization_4d_axis_negative_2.cairo
new file mode 100644
index 000000000..52785fddf
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_2.cairo
@@ -0,0 +1,32 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP8x23Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23TensorPartialEq;
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_4d_axis_negative_2() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1,
+            Option::Some(@input_2),
+            Option::Some(IntegerTrait::<i32>::new(2, true)),
+            Option::None,
+            Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_2/input_0.cairo b/tests/nodes/layer_normalization_4d_axis_negative_2/input_0.cairo
new file mode 100644
index 000000000..d7913bb11
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_2/input_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 9853496, sign: true });
+    data.append(FP8x23 { mag: 12258403, sign: false });
+    data.append(FP8x23 { mag: 872656, sign: false });
+    data.append(FP8x23 { mag: 7388622, sign: true });
+    data.append(FP8x23 { mag: 15454325, sign: false });
+    data.append(FP8x23 { mag: 16251435, sign: false });
+    data.append(FP8x23 { mag: 903277, sign: true });
+    data.append(FP8x23 { mag: 3289794, sign: true });
+    data.append(FP8x23 { mag: 8057933, sign: false });
+    data.append(FP8x23 { mag: 6386388, sign: true });
+    data.append(FP8x23 { mag: 5708410, sign: true });
+    data.append(FP8x23 { mag: 4543373, sign: true });
+    data.append(FP8x23 { mag: 23644376, sign: false });
+    data.append(FP8x23 { mag: 7843321, sign: true });
+    data.append(FP8x23 { mag: 5801261, sign: false });
+    data.append(FP8x23 { mag: 5404517, sign: true });
+    data.append(FP8x23 { mag: 3421350, sign: false });
+    data.append(FP8x23 { mag: 2806284, sign: true });
+    data.append(FP8x23 { mag: 5479745, sign: false });
+    data.append(FP8x23 { mag: 9044852, sign: true });
+    data.append(FP8x23 { mag: 2879371, sign: true });
+    data.append(FP8x23 { mag: 7506722, sign: false });
+    data.append(FP8x23 { mag: 374708, sign: false });
+    data.append(FP8x23 { mag: 16088456, sign: false });
+    data.append(FP8x23 { mag: 7446071, sign: false });
+    data.append(FP8x23 { mag: 12333473, sign: true });
+    data.append(FP8x23 { mag: 2694855, sign: false });
+    data.append(FP8x23 { mag: 29333, sign: false });
+    data.append(FP8x23 { mag: 3210230, sign: true });
+    data.append(FP8x23 { mag: 246487, sign: false });
+    data.append(FP8x23 { mag: 11307238, sign: true });
+    data.append(FP8x23 { mag: 808074, sign: false });
+    data.append(FP8x23 { mag: 2244426, sign: false });
+    data.append(FP8x23 { mag: 4494036, sign: false });
+    data.append(FP8x23 { mag: 9168918, sign: false });
+    data.append(FP8x23 { mag: 11831318, sign: true });
+    data.append(FP8x23 { mag: 11514568, sign: true });
+    data.append(FP8x23 { mag: 3381120, sign: true });
+    data.append(FP8x23 { mag: 6827926, sign: true });
+    data.append(FP8x23 { mag: 2579494, sign: false });
+    data.append(FP8x23 { mag: 4493030, sign: true });
+    data.append(FP8x23 { mag: 4570125, sign: false });
+    data.append(FP8x23 { mag: 8010665, sign: false });
+    data.append(FP8x23 { mag: 5794037, sign: true });
+    data.append(FP8x23 { mag: 9849078, sign: false });
+    data.append(FP8x23 { mag: 11691798, sign: true });
+    data.append(FP8x23 { mag: 3652747, sign: false });
+    data.append(FP8x23 { mag: 1032666, sign: true });
+    data.append(FP8x23 { mag: 9259310, sign: true });
+    data.append(FP8x23 { mag: 7405492, sign: false });
+    data.append(FP8x23 { mag: 4687488, sign: true });
+    data.append(FP8x23 { mag: 1097650, sign: true });
+    data.append(FP8x23 { mag: 2213858, sign: true });
+    data.append(FP8x23 { mag: 1436205, sign: true });
+    data.append(FP8x23 { mag: 10226423, sign: false });
+    data.append(FP8x23 { mag: 6130226, sign: false });
+    data.append(FP8x23 { mag: 1214058, sign: false });
+    data.append(FP8x23 { mag: 12299984, sign: true });
+    data.append(FP8x23 { mag: 829240, sign: false });
+    data.append(FP8x23 { mag: 1612388, sign: false });
+    data.append(FP8x23 { mag: 6632529, sign: true });
+    data.append(FP8x23 { mag: 4410829, sign: true });
+    data.append(FP8x23 { mag: 20654302, sign: false });
+    data.append(FP8x23 { mag: 462475, sign: true });
+    data.append(FP8x23 { mag: 10502841, sign: true });
+    data.append(FP8x23 { mag: 7171902, sign: true });
+    data.append(FP8x23 { mag: 4204962, sign: false });
+    data.append(FP8x23 { mag: 17427142, sign: true });
+    data.append(FP8x23 { mag: 12555224, sign: true });
+    data.append(FP8x23 { mag: 8307885, sign: true });
+    data.append(FP8x23 { mag: 455536, sign: false });
+    data.append(FP8x23 { mag: 577191, sign: false });
+    data.append(FP8x23 { mag: 4412268, sign: true });
+    data.append(FP8x23 { mag: 15812229, sign: false });
+    data.append(FP8x23 { mag: 7098764, sign: true });
+    data.append(FP8x23 { mag: 9127468, sign: false });
+    data.append(FP8x23 { mag: 4753858, sign: false });
+    data.append(FP8x23 { mag: 2074029, sign: true });
+    data.append(FP8x23 { mag: 1651256, sign: false });
+    data.append(FP8x23 { mag: 9617324, sign: true });
+    data.append(FP8x23 { mag: 11400835, sign: false });
+    data.append(FP8x23 { mag: 4263073, sign: true });
+    data.append(FP8x23 { mag: 22170402, sign: false });
+    data.append(FP8x23 { mag: 7715608, sign: false });
+    data.append(FP8x23 { mag: 7511781, sign: true });
+    data.append(FP8x23 { mag: 8686402, sign: false });
+    data.append(FP8x23 { mag: 2710329, sign: true });
+    data.append(FP8x23 { mag: 5540998, sign: true });
+    data.append(FP8x23 { mag: 11608300, sign: true });
+    data.append(FP8x23 { mag: 3020404, sign: true });
+    data.append(FP8x23 { mag: 6342478, sign: true });
+    data.append(FP8x23 { mag: 9399735, sign: true });
+    data.append(FP8x23 { mag: 446463, sign: false });
+    data.append(FP8x23 { mag: 13691013, sign: true });
+    data.append(FP8x23 { mag: 11552903, sign: true });
+    data.append(FP8x23 { mag: 1204731, sign: false });
+    data.append(FP8x23 { mag: 1741592, sign: true });
+    data.append(FP8x23 { mag: 13103082, sign: false });
+    data.append(FP8x23 { mag: 3181444, sign: true });
+    data.append(FP8x23 { mag: 256975, sign: true });
+    data.append(FP8x23 { mag: 9440785, sign: true });
+    data.append(FP8x23 { mag: 2112590, sign: false });
+    data.append(FP8x23 { mag: 13404752, sign: false });
+    data.append(FP8x23 { mag: 760699, sign: false });
+    data.append(FP8x23 { mag: 1588793, sign: true });
+    data.append(FP8x23 { mag: 13026604, sign: true });
+    data.append(FP8x23 { mag: 452707, sign: false });
+    data.append(FP8x23 { mag: 7267348, sign: true });
+    data.append(FP8x23 { mag: 14737007, sign: false });
+    data.append(FP8x23 { mag: 8457998, sign: false });
+    data.append(FP8x23 { mag: 2233703, sign: false });
+    data.append(FP8x23 { mag: 3434673, sign: true });
+    data.append(FP8x23 { mag: 4280157, sign: true });
+    data.append(FP8x23 { mag: 2950181, sign: true });
+    data.append(FP8x23 { mag: 1385553, sign: false });
+    data.append(FP8x23 { mag: 17250056, sign: false });
+    data.append(FP8x23 { mag: 12716927, sign: true });
+    data.append(FP8x23 { mag: 2980452, sign: false });
+    data.append(FP8x23 { mag: 13031106, sign: true });
+    data.append(FP8x23 { mag: 4118717, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_2/input_1.cairo b/tests/nodes/layer_normalization_4d_axis_negative_2/input_1.cairo
new file mode 100644
index 000000000..3356d848b
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_2/input_1.cairo
@@ -0,0 +1,33 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_1() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 1860902, sign: true });
+    data.append(FP8x23 { mag: 7189990, sign: false });
+    data.append(FP8x23 { mag: 5594953, sign: false });
+    data.append(FP8x23 { mag: 14949612, sign: false });
+    data.append(FP8x23 { mag: 1598676, sign: true });
+    data.append(FP8x23 { mag: 19332304, sign: true });
+    data.append(FP8x23 { mag: 13237330, sign: true });
+    data.append(FP8x23 { mag: 13876161, sign: true });
+    data.append(FP8x23 { mag: 2710915, sign: false });
+    data.append(FP8x23 { mag: 1998193, sign: false });
+    data.append(FP8x23 { mag: 10029104, sign: true });
+    data.append(FP8x23 { mag: 5128877, sign: true });
+    data.append(FP8x23 { mag: 12692706, sign: false });
+    data.append(FP8x23 { mag: 7217481, sign: false });
+    data.append(FP8x23 { mag: 2729123, sign: true });
+    data.append(FP8x23 { mag: 12888666, sign: false });
+    data.append(FP8x23 { mag: 4258854, sign: false });
+    data.append(FP8x23 { mag: 1006706, sign: true });
+    data.append(FP8x23 { mag: 3116978, sign: false });
+    data.append(FP8x23 { mag: 10767356, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_2/input_2.cairo b/tests/nodes/layer_normalization_4d_axis_negative_2/input_2.cairo
new file mode 100644
index 000000000..f973cd66d
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_2/input_2.cairo
@@ -0,0 +1,33 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_2() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 14435005, sign: false });
+    data.append(FP8x23 { mag: 275345, sign: false });
+    data.append(FP8x23 { mag: 7948101, sign: true });
+    data.append(FP8x23 { mag: 124471, sign: true });
+    data.append(FP8x23 { mag: 11083371, sign: true });
+    data.append(FP8x23 { mag: 2513924, sign: true });
+    data.append(FP8x23 { mag: 6387124, sign: true });
+    data.append(FP8x23 { mag: 5452904, sign: false });
+    data.append(FP8x23 { mag: 12271809, sign: true });
+    data.append(FP8x23 { mag: 15327354, sign: true });
+    data.append(FP8x23 { mag: 3795402, sign: true });
+    data.append(FP8x23 { mag: 2307268, sign: false });
+    data.append(FP8x23 { mag: 5731544, sign: false });
+    data.append(FP8x23 { mag: 4011370, sign: true });
+    data.append(FP8x23 { mag: 3178152, sign: false });
+    data.append(FP8x23 { mag: 14982171, sign: false });
+    data.append(FP8x23 { mag: 2850000, sign: true });
+    data.append(FP8x23 { mag: 9445099, sign: false });
+    data.append(FP8x23 { mag: 8149556, sign: false });
+    data.append(FP8x23 { mag: 8935026, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_2/output_0.cairo b/tests/nodes/layer_normalization_4d_axis_negative_2/output_0.cairo
new file mode 100644
index 000000000..d1025c8b9
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_2/output_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn output_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 16673352, sign: false });
+    data.append(FP8x23 { mag: 8614825, sign: false });
+    data.append(FP8x23 { mag: 8265431, sign: true });
+    data.append(FP8x23 { mag: 14168915, sign: true });
+    data.append(FP8x23 { mag: 13483567, sign: true });
+    data.append(FP8x23 { mag: 33185346, sign: true });
+    data.append(FP8x23 { mag: 3124395, sign: true });
+    data.append(FP8x23 { mag: 12411572, sign: false });
+    data.append(FP8x23 { mag: 10344227, sign: true });
+    data.append(FP8x23 { mag: 16990572, sign: true });
+    data.append(FP8x23 { mag: 3825893, sign: false });
+    data.append(FP8x23 { mag: 5566316, sign: false });
+    data.append(FP8x23 { mag: 35895616, sign: false });
+    data.append(FP8x23 { mag: 11142513, sign: true });
+    data.append(FP8x23 { mag: 1895698, sign: false });
+    data.append(FP8x23 { mag: 5606359, sign: false });
+    data.append(FP8x23 { mag: 1931723, sign: true });
+    data.append(FP8x23 { mag: 9897935, sign: false });
+    data.append(FP8x23 { mag: 9507187, sign: false });
+    data.append(FP8x23 { mag: 20955956, sign: false });
+    data.append(FP8x23 { mag: 15031808, sign: false });
+    data.append(FP8x23 { mag: 7951613, sign: false });
+    data.append(FP8x23 { mag: 7308734, sign: true });
+    data.append(FP8x23 { mag: 32985592, sign: false });
+    data.append(FP8x23 { mag: 12777209, sign: true });
+    data.append(FP8x23 { mag: 28117402, sign: false });
+    data.append(FP8x23 { mag: 12005266, sign: true });
+    data.append(FP8x23 { mag: 4507819, sign: false });
+    data.append(FP8x23 { mag: 13261112, sign: true });
+    data.append(FP8x23 { mag: 15133257, sign: true });
+    data.append(FP8x23 { mag: 10719554, sign: false });
+    data.append(FP8x23 { mag: 1424049, sign: false });
+    data.append(FP8x23 { mag: 10354309, sign: false });
+    data.append(FP8x23 { mag: 787664, sign: false });
+    data.append(FP8x23 { mag: 341933, sign: true });
+    data.append(FP8x23 { mag: 4574301, sign: true });
+    data.append(FP8x23 { mag: 9131801, sign: true });
+    data.append(FP8x23 { mag: 9835476, sign: false });
+    data.append(FP8x23 { mag: 5504734, sign: false });
+    data.append(FP8x23 { mag: 4531222, sign: false });
+    data.append(FP8x23 { mag: 15705951, sign: false });
+    data.append(FP8x23 { mag: 5326222, sign: false });
+    data.append(FP8x23 { mag: 1075073, sign: true });
+    data.append(FP8x23 { mag: 13307859, sign: true });
+    data.append(FP8x23 { mag: 13496520, sign: true });
+    data.append(FP8x23 { mag: 31963888, sign: false });
+    data.append(FP8x23 { mag: 13829816, sign: true });
+    data.append(FP8x23 { mag: 7589786, sign: false });
+    data.append(FP8x23 { mag: 16098488, sign: true });
+    data.append(FP8x23 { mag: 13057562, sign: true });
+    data.append(FP8x23 { mag: 3352336, sign: false });
+    data.append(FP8x23 { mag: 3148048, sign: false });
+    data.append(FP8x23 { mag: 1485042, sign: false });
+    data.append(FP8x23 { mag: 5568070, sign: true });
+    data.append(FP8x23 { mag: 1098794, sign: true });
+    data.append(FP8x23 { mag: 27110094, sign: false });
+    data.append(FP8x23 { mag: 2043142, sign: true });
+    data.append(FP8x23 { mag: 11334084, sign: false });
+    data.append(FP8x23 { mag: 8556721, sign: false });
+    data.append(FP8x23 { mag: 6239462, sign: false });
+    data.append(FP8x23 { mag: 15445237, sign: false });
+    data.append(FP8x23 { mag: 1879795, sign: true });
+    data.append(FP8x23 { mag: 5721750, sign: false });
+    data.append(FP8x23 { mag: 1854019, sign: false });
+    data.append(FP8x23 { mag: 9538382, sign: true });
+    data.append(FP8x23 { mag: 9122158, sign: false });
+    data.append(FP8x23 { mag: 14900349, sign: true });
+    data.append(FP8x23 { mag: 29377836, sign: false });
+    data.append(FP8x23 { mag: 15500562, sign: true });
+    data.append(FP8x23 { mag: 16778470, sign: true });
+    data.append(FP8x23 { mag: 6130239, sign: true });
+    data.append(FP8x23 { mag: 1044951, sign: false });
+    data.append(FP8x23 { mag: 1925009, sign: false });
+    data.append(FP8x23 { mag: 9798252, sign: false });
+    data.append(FP8x23 { mag: 4798964, sign: false });
+    data.append(FP8x23 { mag: 30214180, sign: false });
+    data.append(FP8x23 { mag: 144786, sign: false });
+    data.append(FP8x23 { mag: 9489410, sign: false });
+    data.append(FP8x23 { mag: 9283074, sign: false });
+    data.append(FP8x23 { mag: 18297370, sign: false });
+    data.append(FP8x23 { mag: 11923786, sign: false });
+    data.append(FP8x23 { mag: 2473868, sign: true });
+    data.append(FP8x23 { mag: 6264012, sign: false });
+    data.append(FP8x23 { mag: 13958334, sign: false });
+    data.append(FP8x23 { mag: 9897873, sign: true });
+    data.append(FP8x23 { mag: 22800294, sign: true });
+    data.append(FP8x23 { mag: 3598116, sign: true });
+    data.append(FP8x23 { mag: 12719248, sign: false });
+    data.append(FP8x23 { mag: 15509912, sign: true });
+    data.append(FP8x23 { mag: 15816862, sign: true });
+    data.append(FP8x23 { mag: 2345113, sign: false });
+    data.append(FP8x23 { mag: 7181165, sign: false });
+    data.append(FP8x23 { mag: 7487303, sign: false });
+    data.append(FP8x23 { mag: 14294384, sign: true });
+    data.append(FP8x23 { mag: 6421289, sign: false });
+    data.append(FP8x23 { mag: 17845564, sign: false });
+    data.append(FP8x23 { mag: 3291163, sign: true });
+    data.append(FP8x23 { mag: 7897120, sign: false });
+    data.append(FP8x23 { mag: 7330476, sign: false });
+    data.append(FP8x23 { mag: 8283012, sign: false });
+    data.append(FP8x23 { mag: 16386448, sign: false });
+    data.append(FP8x23 { mag: 2375043, sign: false });
+    data.append(FP8x23 { mag: 1017275, sign: false });
+    data.append(FP8x23 { mag: 1896022, sign: false });
+    data.append(FP8x23 { mag: 10863572, sign: true });
+    data.append(FP8x23 { mag: 25803316, sign: false });
+    data.append(FP8x23 { mag: 7703091, sign: true });
+    data.append(FP8x23 { mag: 16504475, sign: false });
+    data.append(FP8x23 { mag: 7508723, sign: true });
+    data.append(FP8x23 { mag: 13272474, sign: true });
+    data.append(FP8x23 { mag: 6865161, sign: true });
+    data.append(FP8x23 { mag: 4111037, sign: false });
+    data.append(FP8x23 { mag: 22349, sign: false });
+    data.append(FP8x23 { mag: 6143899, sign: true });
+    data.append(FP8x23 { mag: 2611413, sign: false });
+    data.append(FP8x23 { mag: 41386196, sign: false });
+    data.append(FP8x23 { mag: 8935166, sign: true });
+    data.append(FP8x23 { mag: 9049725, sign: false });
+    data.append(FP8x23 { mag: 3582293, sign: false });
+    data.append(FP8x23 { mag: 13576482, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_3.cairo b/tests/nodes/layer_normalization_4d_axis_negative_3.cairo
new file mode 100644
index 000000000..3b01a1d4a
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_3.cairo
@@ -0,0 +1,32 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP8x23Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23TensorPartialEq;
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_4d_axis_negative_3() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1,
+            Option::Some(@input_2),
+            Option::Some(IntegerTrait::<i32>::new(3, true)),
+            Option::None,
+            Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_3/input_0.cairo b/tests/nodes/layer_normalization_4d_axis_negative_3/input_0.cairo
new file mode 100644
index 000000000..d7913bb11
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_3/input_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 9853496, sign: true });
+    data.append(FP8x23 { mag: 12258403, sign: false });
+    data.append(FP8x23 { mag: 872656, sign: false });
+    data.append(FP8x23 { mag: 7388622, sign: true });
+    data.append(FP8x23 { mag: 15454325, sign: false });
+    data.append(FP8x23 { mag: 16251435, sign: false });
+    data.append(FP8x23 { mag: 903277, sign: true });
+    data.append(FP8x23 { mag: 3289794, sign: true });
+    data.append(FP8x23 { mag: 8057933, sign: false });
+    data.append(FP8x23 { mag: 6386388, sign: true });
+    data.append(FP8x23 { mag: 5708410, sign: true });
+    data.append(FP8x23 { mag: 4543373, sign: true });
+    data.append(FP8x23 { mag: 23644376, sign: false });
+    data.append(FP8x23 { mag: 7843321, sign: true });
+    data.append(FP8x23 { mag: 5801261, sign: false });
+    data.append(FP8x23 { mag: 5404517, sign: true });
+    data.append(FP8x23 { mag: 3421350, sign: false });
+    data.append(FP8x23 { mag: 2806284, sign: true });
+    data.append(FP8x23 { mag: 5479745, sign: false });
+    data.append(FP8x23 { mag: 9044852, sign: true });
+    data.append(FP8x23 { mag: 2879371, sign: true });
+    data.append(FP8x23 { mag: 7506722, sign: false });
+    data.append(FP8x23 { mag: 374708, sign: false });
+    data.append(FP8x23 { mag: 16088456, sign: false });
+    data.append(FP8x23 { mag: 7446071, sign: false });
+    data.append(FP8x23 { mag: 12333473, sign: true });
+    data.append(FP8x23 { mag: 2694855, sign: false });
+    data.append(FP8x23 { mag: 29333, sign: false });
+    data.append(FP8x23 { mag: 3210230, sign: true });
+    data.append(FP8x23 { mag: 246487, sign: false });
+    data.append(FP8x23 { mag: 11307238, sign: true });
+    data.append(FP8x23 { mag: 808074, sign: false });
+    data.append(FP8x23 { mag: 2244426, sign: false });
+    data.append(FP8x23 { mag: 4494036, sign: false });
+    data.append(FP8x23 { mag: 9168918, sign: false });
+    data.append(FP8x23 { mag: 11831318, sign: true });
+    data.append(FP8x23 { mag: 11514568, sign: true });
+    data.append(FP8x23 { mag: 3381120, sign: true });
+    data.append(FP8x23 { mag: 6827926, sign: true });
+    data.append(FP8x23 { mag: 2579494, sign: false });
+    data.append(FP8x23 { mag: 4493030, sign: true });
+    data.append(FP8x23 { mag: 4570125, sign: false });
+    data.append(FP8x23 { mag: 8010665, sign: false });
+    data.append(FP8x23 { mag: 5794037, sign: true });
+    data.append(FP8x23 { mag: 9849078, sign: false });
+    data.append(FP8x23 { mag: 11691798, sign: true });
+    data.append(FP8x23 { mag: 3652747, sign: false });
+    data.append(FP8x23 { mag: 1032666, sign: true });
+    data.append(FP8x23 { mag: 9259310, sign: true });
+    data.append(FP8x23 { mag: 7405492, sign: false });
+    data.append(FP8x23 { mag: 4687488, sign: true });
+    data.append(FP8x23 { mag: 1097650, sign: true });
+    data.append(FP8x23 { mag: 2213858, sign: true });
+    data.append(FP8x23 { mag: 1436205, sign: true });
+    data.append(FP8x23 { mag: 10226423, sign: false });
+    data.append(FP8x23 { mag: 6130226, sign: false });
+    data.append(FP8x23 { mag: 1214058, sign: false });
+    data.append(FP8x23 { mag: 12299984, sign: true });
+    data.append(FP8x23 { mag: 829240, sign: false });
+    data.append(FP8x23 { mag: 1612388, sign: false });
+    data.append(FP8x23 { mag: 6632529, sign: true });
+    data.append(FP8x23 { mag: 4410829, sign: true });
+    data.append(FP8x23 { mag: 20654302, sign: false });
+    data.append(FP8x23 { mag: 462475, sign: true });
+    data.append(FP8x23 { mag: 10502841, sign: true });
+    data.append(FP8x23 { mag: 7171902, sign: true });
+    data.append(FP8x23 { mag: 4204962, sign: false });
+    data.append(FP8x23 { mag: 17427142, sign: true });
+    data.append(FP8x23 { mag: 12555224, sign: true });
+    data.append(FP8x23 { mag: 8307885, sign: true });
+    data.append(FP8x23 { mag: 455536, sign: false });
+    data.append(FP8x23 { mag: 577191, sign: false });
+    data.append(FP8x23 { mag: 4412268, sign: true });
+    data.append(FP8x23 { mag: 15812229, sign: false });
+    data.append(FP8x23 { mag: 7098764, sign: true });
+    data.append(FP8x23 { mag: 9127468, sign: false });
+    data.append(FP8x23 { mag: 4753858, sign: false });
+    data.append(FP8x23 { mag: 2074029, sign: true });
+    data.append(FP8x23 { mag: 1651256, sign: false });
+    data.append(FP8x23 { mag: 9617324, sign: true });
+    data.append(FP8x23 { mag: 11400835, sign: false });
+    data.append(FP8x23 { mag: 4263073, sign: true });
+    data.append(FP8x23 { mag: 22170402, sign: false });
+    data.append(FP8x23 { mag: 7715608, sign: false });
+    data.append(FP8x23 { mag: 7511781, sign: true });
+    data.append(FP8x23 { mag: 8686402, sign: false });
+    data.append(FP8x23 { mag: 2710329, sign: true });
+    data.append(FP8x23 { mag: 5540998, sign: true });
+    data.append(FP8x23 { mag: 11608300, sign: true });
+    data.append(FP8x23 { mag: 3020404, sign: true });
+    data.append(FP8x23 { mag: 6342478, sign: true });
+    data.append(FP8x23 { mag: 9399735, sign: true });
+    data.append(FP8x23 { mag: 446463, sign: false });
+    data.append(FP8x23 { mag: 13691013, sign: true });
+    data.append(FP8x23 { mag: 11552903, sign: true });
+    data.append(FP8x23 { mag: 1204731, sign: false });
+    data.append(FP8x23 { mag: 1741592, sign: true });
+    data.append(FP8x23 { mag: 13103082, sign: false });
+    data.append(FP8x23 { mag: 3181444, sign: true });
+    data.append(FP8x23 { mag: 256975, sign: true });
+    data.append(FP8x23 { mag: 9440785, sign: true });
+    data.append(FP8x23 { mag: 2112590, sign: false });
+    data.append(FP8x23 { mag: 13404752, sign: false });
+    data.append(FP8x23 { mag: 760699, sign: false });
+    data.append(FP8x23 { mag: 1588793, sign: true });
+    data.append(FP8x23 { mag: 13026604, sign: true });
+    data.append(FP8x23 { mag: 452707, sign: false });
+    data.append(FP8x23 { mag: 7267348, sign: true });
+    data.append(FP8x23 { mag: 14737007, sign: false });
+    data.append(FP8x23 { mag: 8457998, sign: false });
+    data.append(FP8x23 { mag: 2233703, sign: false });
+    data.append(FP8x23 { mag: 3434673, sign: true });
+    data.append(FP8x23 { mag: 4280157, sign: true });
+    data.append(FP8x23 { mag: 2950181, sign: true });
+    data.append(FP8x23 { mag: 1385553, sign: false });
+    data.append(FP8x23 { mag: 17250056, sign: false });
+    data.append(FP8x23 { mag: 12716927, sign: true });
+    data.append(FP8x23 { mag: 2980452, sign: false });
+    data.append(FP8x23 { mag: 13031106, sign: true });
+    data.append(FP8x23 { mag: 4118717, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_3/input_1.cairo b/tests/nodes/layer_normalization_4d_axis_negative_3/input_1.cairo
new file mode 100644
index 000000000..99425c1b7
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_3/input_1.cairo
@@ -0,0 +1,74 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_1() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 16380343, sign: false });
+    data.append(FP8x23 { mag: 4257343, sign: false });
+    data.append(FP8x23 { mag: 600530, sign: false });
+    data.append(FP8x23 { mag: 2445196, sign: false });
+    data.append(FP8x23 { mag: 9479607, sign: true });
+    data.append(FP8x23 { mag: 978145, sign: true });
+    data.append(FP8x23 { mag: 7810708, sign: false });
+    data.append(FP8x23 { mag: 3402716, sign: true });
+    data.append(FP8x23 { mag: 23762706, sign: true });
+    data.append(FP8x23 { mag: 3969772, sign: true });
+    data.append(FP8x23 { mag: 287406, sign: false });
+    data.append(FP8x23 { mag: 17010742, sign: true });
+    data.append(FP8x23 { mag: 13856942, sign: true });
+    data.append(FP8x23 { mag: 8047151, sign: true });
+    data.append(FP8x23 { mag: 7959897, sign: false });
+    data.append(FP8x23 { mag: 13270756, sign: true });
+    data.append(FP8x23 { mag: 2099848, sign: true });
+    data.append(FP8x23 { mag: 10634067, sign: false });
+    data.append(FP8x23 { mag: 12591738, sign: false });
+    data.append(FP8x23 { mag: 3020733, sign: true });
+    data.append(FP8x23 { mag: 6136434, sign: false });
+    data.append(FP8x23 { mag: 3475208, sign: false });
+    data.append(FP8x23 { mag: 5530273, sign: true });
+    data.append(FP8x23 { mag: 3210239, sign: true });
+    data.append(FP8x23 { mag: 1963138, sign: false });
+    data.append(FP8x23 { mag: 12035630, sign: false });
+    data.append(FP8x23 { mag: 1743576, sign: false });
+    data.append(FP8x23 { mag: 1123297, sign: true });
+    data.append(FP8x23 { mag: 3857322, sign: false });
+    data.append(FP8x23 { mag: 10535923, sign: false });
+    data.append(FP8x23 { mag: 22905288, sign: false });
+    data.append(FP8x23 { mag: 6554809, sign: false });
+    data.append(FP8x23 { mag: 4404682, sign: false });
+    data.append(FP8x23 { mag: 2731185, sign: true });
+    data.append(FP8x23 { mag: 1388899, sign: true });
+    data.append(FP8x23 { mag: 6849233, sign: false });
+    data.append(FP8x23 { mag: 2696271, sign: true });
+    data.append(FP8x23 { mag: 4022892, sign: true });
+    data.append(FP8x23 { mag: 10206850, sign: true });
+    data.append(FP8x23 { mag: 1922098, sign: false });
+    data.append(FP8x23 { mag: 5108807, sign: false });
+    data.append(FP8x23 { mag: 6197766, sign: false });
+    data.append(FP8x23 { mag: 3598686, sign: true });
+    data.append(FP8x23 { mag: 12654023, sign: true });
+    data.append(FP8x23 { mag: 5122151, sign: false });
+    data.append(FP8x23 { mag: 7636231, sign: false });
+    data.append(FP8x23 { mag: 3971308, sign: false });
+    data.append(FP8x23 { mag: 798761, sign: false });
+    data.append(FP8x23 { mag: 6869233, sign: false });
+    data.append(FP8x23 { mag: 8740030, sign: true });
+    data.append(FP8x23 { mag: 4664523, sign: false });
+    data.append(FP8x23 { mag: 3437420, sign: false });
+    data.append(FP8x23 { mag: 3958479, sign: false });
+    data.append(FP8x23 { mag: 6298427, sign: true });
+    data.append(FP8x23 { mag: 3669742, sign: false });
+    data.append(FP8x23 { mag: 1711541, sign: false });
+    data.append(FP8x23 { mag: 22958802, sign: false });
+    data.append(FP8x23 { mag: 684171, sign: true });
+    data.append(FP8x23 { mag: 1802568, sign: true });
+    data.append(FP8x23 { mag: 1621301, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_3/input_2.cairo b/tests/nodes/layer_normalization_4d_axis_negative_3/input_2.cairo
new file mode 100644
index 000000000..8a5d49e10
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_3/input_2.cairo
@@ -0,0 +1,74 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_2() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 9802310, sign: true });
+    data.append(FP8x23 { mag: 18025368, sign: false });
+    data.append(FP8x23 { mag: 17682696, sign: true });
+    data.append(FP8x23 { mag: 7029352, sign: true });
+    data.append(FP8x23 { mag: 2764347, sign: true });
+    data.append(FP8x23 { mag: 7626198, sign: true });
+    data.append(FP8x23 { mag: 7957030, sign: false });
+    data.append(FP8x23 { mag: 7354525, sign: false });
+    data.append(FP8x23 { mag: 2783866, sign: false });
+    data.append(FP8x23 { mag: 3333849, sign: true });
+    data.append(FP8x23 { mag: 906374, sign: true });
+    data.append(FP8x23 { mag: 6164127, sign: true });
+    data.append(FP8x23 { mag: 4297991, sign: true });
+    data.append(FP8x23 { mag: 5427655, sign: false });
+    data.append(FP8x23 { mag: 18113416, sign: true });
+    data.append(FP8x23 { mag: 2255928, sign: false });
+    data.append(FP8x23 { mag: 1389584, sign: false });
+    data.append(FP8x23 { mag: 4815558, sign: false });
+    data.append(FP8x23 { mag: 2817187, sign: true });
+    data.append(FP8x23 { mag: 440217, sign: false });
+    data.append(FP8x23 { mag: 190737, sign: false });
+    data.append(FP8x23 { mag: 8139391, sign: false });
+    data.append(FP8x23 { mag: 11498979, sign: true });
+    data.append(FP8x23 { mag: 4351939, sign: true });
+    data.append(FP8x23 { mag: 5194729, sign: false });
+    data.append(FP8x23 { mag: 1153310, sign: true });
+    data.append(FP8x23 { mag: 2229956, sign: true });
+    data.append(FP8x23 { mag: 4621022, sign: true });
+    data.append(FP8x23 { mag: 1791765, sign: false });
+    data.append(FP8x23 { mag: 6755727, sign: false });
+    data.append(FP8x23 { mag: 3423500, sign: true });
+    data.append(FP8x23 { mag: 4643245, sign: true });
+    data.append(FP8x23 { mag: 343398, sign: true });
+    data.append(FP8x23 { mag: 18973418, sign: true });
+    data.append(FP8x23 { mag: 22705418, sign: false });
+    data.append(FP8x23 { mag: 4003045, sign: false });
+    data.append(FP8x23 { mag: 14684607, sign: true });
+    data.append(FP8x23 { mag: 9470858, sign: false });
+    data.append(FP8x23 { mag: 4521178, sign: true });
+    data.append(FP8x23 { mag: 8692518, sign: true });
+    data.append(FP8x23 { mag: 5888156, sign: false });
+    data.append(FP8x23 { mag: 7325508, sign: true });
+    data.append(FP8x23 { mag: 10220334, sign: true });
+    data.append(FP8x23 { mag: 408916, sign: true });
+    data.append(FP8x23 { mag: 2895708, sign: true });
+    data.append(FP8x23 { mag: 8610215, sign: false });
+    data.append(FP8x23 { mag: 11150253, sign: true });
+    data.append(FP8x23 { mag: 5360206, sign: false });
+    data.append(FP8x23 { mag: 674939, sign: false });
+    data.append(FP8x23 { mag: 11169360, sign: true });
+    data.append(FP8x23 { mag: 4025671, sign: false });
+    data.append(FP8x23 { mag: 9509782, sign: false });
+    data.append(FP8x23 { mag: 5718525, sign: true });
+    data.append(FP8x23 { mag: 8584055, sign: false });
+    data.append(FP8x23 { mag: 5100969, sign: true });
+    data.append(FP8x23 { mag: 13275665, sign: true });
+    data.append(FP8x23 { mag: 14069922, sign: true });
+    data.append(FP8x23 { mag: 9485429, sign: true });
+    data.append(FP8x23 { mag: 3613096, sign: false });
+    data.append(FP8x23 { mag: 11158159, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_3/output_0.cairo b/tests/nodes/layer_normalization_4d_axis_negative_3/output_0.cairo
new file mode 100644
index 000000000..125162520
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_3/output_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn output_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 30795284, sign: true });
+    data.append(FP8x23 { mag: 24452244, sign: false });
+    data.append(FP8x23 { mag: 17639236, sign: true });
+    data.append(FP8x23 { mag: 9402301, sign: true });
+    data.append(FP8x23 { mag: 20899032, sign: true });
+    data.append(FP8x23 { mag: 9595832, sign: true });
+    data.append(FP8x23 { mag: 6771320, sign: false });
+    data.append(FP8x23 { mag: 8896149, sign: false });
+    data.append(FP8x23 { mag: 20488648, sign: true });
+    data.append(FP8x23 { mag: 16405, sign: false });
+    data.append(FP8x23 { mag: 1124332, sign: true });
+    data.append(FP8x23 { mag: 4234479, sign: false });
+    data.append(FP8x23 { mag: 45132380, sign: true });
+    data.append(FP8x23 { mag: 13698921, sign: false });
+    data.append(FP8x23 { mag: 12585182, sign: true });
+    data.append(FP8x23 { mag: 11810859, sign: false });
+    data.append(FP8x23 { mag: 562048, sign: false });
+    data.append(FP8x23 { mag: 646758, sign: false });
+    data.append(FP8x23 { mag: 5416873, sign: false });
+    data.append(FP8x23 { mag: 4003232, sign: false });
+    data.append(FP8x23 { mag: 2271500, sign: true });
+    data.append(FP8x23 { mag: 11301107, sign: false });
+    data.append(FP8x23 { mag: 11551598, sign: true });
+    data.append(FP8x23 { mag: 10750163, sign: true });
+    data.append(FP8x23 { mag: 6965746, sign: false });
+    data.append(FP8x23 { mag: 20345854, sign: true });
+    data.append(FP8x23 { mag: 1702719, sign: true });
+    data.append(FP8x23 { mag: 4582737, sign: true });
+    data.append(FP8x23 { mag: 82919, sign: false });
+    data.append(FP8x23 { mag: 6685446, sign: false });
+    data.append(FP8x23 { mag: 36982080, sign: true });
+    data.append(FP8x23 { mag: 4222303, sign: true });
+    data.append(FP8x23 { mag: 738083, sign: false });
+    data.append(FP8x23 { mag: 20419578, sign: true });
+    data.append(FP8x23 { mag: 21150390, sign: false });
+    data.append(FP8x23 { mag: 6484889, sign: true });
+    data.append(FP8x23 { mag: 10663730, sign: true });
+    data.append(FP8x23 { mag: 11339833, sign: false });
+    data.append(FP8x23 { mag: 4661686, sign: false });
+    data.append(FP8x23 { mag: 8139288, sign: true });
+    data.append(FP8x23 { mag: 2797626, sign: false });
+    data.append(FP8x23 { mag: 3984268, sign: true });
+    data.append(FP8x23 { mag: 13723312, sign: true });
+    data.append(FP8x23 { mag: 9324158, sign: false });
+    data.append(FP8x23 { mag: 3278882, sign: false });
+    data.append(FP8x23 { mag: 2948328, sign: true });
+    data.append(FP8x23 { mag: 9469186, sign: true });
+    data.append(FP8x23 { mag: 5225903, sign: false });
+    data.append(FP8x23 { mag: 7613417, sign: true });
+    data.append(FP8x23 { mag: 19009284, sign: true });
+    data.append(FP8x23 { mag: 1089409, sign: false });
+    data.append(FP8x23 { mag: 8903622, sign: false });
+    data.append(FP8x23 { mag: 6974317, sign: true });
+    data.append(FP8x23 { mag: 9963899, sign: false });
+    data.append(FP8x23 { mag: 502413, sign: true });
+    data.append(FP8x23 { mag: 12015909, sign: true });
+    data.append(FP8x23 { mag: 11418954, sign: true });
+    data.append(FP8x23 { mag: 8397312, sign: true });
+    data.append(FP8x23 { mag: 3492521, sign: false });
+    data.append(FP8x23 { mag: 11426886, sign: true });
+    data.append(FP8x23 { mag: 20181712, sign: true });
+    data.append(FP8x23 { mag: 16384300, sign: false });
+    data.append(FP8x23 { mag: 16232709, sign: true });
+    data.append(FP8x23 { mag: 6893410, sign: true });
+    data.append(FP8x23 { mag: 7340870, sign: false });
+    data.append(FP8x23 { mag: 6947460, sign: true });
+    data.append(FP8x23 { mag: 12463704, sign: false });
+    data.append(FP8x23 { mag: 13613810, sign: false });
+    data.append(FP8x23 { mag: 33562828, sign: false });
+    data.append(FP8x23 { mag: 75456, sign: true });
+    data.append(FP8x23 { mag: 860922, sign: true });
+    data.append(FP8x23 { mag: 9085466, sign: true });
+    data.append(FP8x23 { mag: 1045640, sign: false });
+    data.append(FP8x23 { mag: 9649592, sign: true });
+    data.append(FP8x23 { mag: 23571772, sign: true });
+    data.append(FP8x23 { mag: 12698496, sign: true });
+    data.append(FP8x23 { mag: 49246, sign: false });
+    data.append(FP8x23 { mag: 3492380, sign: false });
+    data.append(FP8x23 { mag: 856033, sign: false });
+    data.append(FP8x23 { mag: 3361495, sign: false });
+    data.append(FP8x23 { mag: 8664075, sign: false });
+    data.append(FP8x23 { mag: 6857170, sign: false });
+    data.append(FP8x23 { mag: 25788504, sign: true });
+    data.append(FP8x23 { mag: 7463156, sign: true });
+    data.append(FP8x23 { mag: 3757968, sign: false });
+    data.append(FP8x23 { mag: 11816282, sign: false });
+    data.append(FP8x23 { mag: 2570839, sign: true });
+    data.append(FP8x23 { mag: 4046212, sign: true });
+    data.append(FP8x23 { mag: 2796458, sign: true });
+    data.append(FP8x23 { mag: 4330926, sign: false });
+    data.append(FP8x23 { mag: 17195276, sign: true });
+    data.append(FP8x23 { mag: 10822918, sign: true });
+    data.append(FP8x23 { mag: 348715, sign: false });
+    data.append(FP8x23 { mag: 15089289, sign: true });
+    data.append(FP8x23 { mag: 24348896, sign: false });
+    data.append(FP8x23 { mag: 5659438, sign: false });
+    data.append(FP8x23 { mag: 14449243, sign: true });
+    data.append(FP8x23 { mag: 3150978, sign: false });
+    data.append(FP8x23 { mag: 1988497, sign: true });
+    data.append(FP8x23 { mag: 8541534, sign: true });
+    data.append(FP8x23 { mag: 1048302, sign: false });
+    data.append(FP8x23 { mag: 5198116, sign: true });
+    data.append(FP8x23 { mag: 15995067, sign: true });
+    data.append(FP8x23 { mag: 2841452, sign: true });
+    data.append(FP8x23 { mag: 3255403, sign: true });
+    data.append(FP8x23 { mag: 1682816, sign: true });
+    data.append(FP8x23 { mag: 10523466, sign: true });
+    data.append(FP8x23 { mag: 4797427, sign: false });
+    data.append(FP8x23 { mag: 12720153, sign: false });
+    data.append(FP8x23 { mag: 20364616, sign: true });
+    data.append(FP8x23 { mag: 5689884, sign: false });
+    data.append(FP8x23 { mag: 8559599, sign: false });
+    data.append(FP8x23 { mag: 7186609, sign: true });
+    data.append(FP8x23 { mag: 9984205, sign: false });
+    data.append(FP8x23 { mag: 4139366, sign: true });
+    data.append(FP8x23 { mag: 9793996, sign: true });
+    data.append(FP8x23 { mag: 44222332, sign: true });
+    data.append(FP8x23 { mag: 9786600, sign: true });
+    data.append(FP8x23 { mag: 6043721, sign: false });
+    data.append(FP8x23 { mag: 10586105, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_4.cairo b/tests/nodes/layer_normalization_4d_axis_negative_4.cairo
new file mode 100644
index 000000000..7acaf24b3
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_4.cairo
@@ -0,0 +1,32 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP8x23Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23TensorPartialEq;
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_4d_axis_negative_4() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1,
+            Option::Some(@input_2),
+            Option::Some(IntegerTrait::<i32>::new(4, true)),
+            Option::None,
+            Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_4/input_0.cairo b/tests/nodes/layer_normalization_4d_axis_negative_4/input_0.cairo
new file mode 100644
index 000000000..d7913bb11
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_4/input_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 9853496, sign: true });
+    data.append(FP8x23 { mag: 12258403, sign: false });
+    data.append(FP8x23 { mag: 872656, sign: false });
+    data.append(FP8x23 { mag: 7388622, sign: true });
+    data.append(FP8x23 { mag: 15454325, sign: false });
+    data.append(FP8x23 { mag: 16251435, sign: false });
+    data.append(FP8x23 { mag: 903277, sign: true });
+    data.append(FP8x23 { mag: 3289794, sign: true });
+    data.append(FP8x23 { mag: 8057933, sign: false });
+    data.append(FP8x23 { mag: 6386388, sign: true });
+    data.append(FP8x23 { mag: 5708410, sign: true });
+    data.append(FP8x23 { mag: 4543373, sign: true });
+    data.append(FP8x23 { mag: 23644376, sign: false });
+    data.append(FP8x23 { mag: 7843321, sign: true });
+    data.append(FP8x23 { mag: 5801261, sign: false });
+    data.append(FP8x23 { mag: 5404517, sign: true });
+    data.append(FP8x23 { mag: 3421350, sign: false });
+    data.append(FP8x23 { mag: 2806284, sign: true });
+    data.append(FP8x23 { mag: 5479745, sign: false });
+    data.append(FP8x23 { mag: 9044852, sign: true });
+    data.append(FP8x23 { mag: 2879371, sign: true });
+    data.append(FP8x23 { mag: 7506722, sign: false });
+    data.append(FP8x23 { mag: 374708, sign: false });
+    data.append(FP8x23 { mag: 16088456, sign: false });
+    data.append(FP8x23 { mag: 7446071, sign: false });
+    data.append(FP8x23 { mag: 12333473, sign: true });
+    data.append(FP8x23 { mag: 2694855, sign: false });
+    data.append(FP8x23 { mag: 29333, sign: false });
+    data.append(FP8x23 { mag: 3210230, sign: true });
+    data.append(FP8x23 { mag: 246487, sign: false });
+    data.append(FP8x23 { mag: 11307238, sign: true });
+    data.append(FP8x23 { mag: 808074, sign: false });
+    data.append(FP8x23 { mag: 2244426, sign: false });
+    data.append(FP8x23 { mag: 4494036, sign: false });
+    data.append(FP8x23 { mag: 9168918, sign: false });
+    data.append(FP8x23 { mag: 11831318, sign: true });
+    data.append(FP8x23 { mag: 11514568, sign: true });
+    data.append(FP8x23 { mag: 3381120, sign: true });
+    data.append(FP8x23 { mag: 6827926, sign: true });
+    data.append(FP8x23 { mag: 2579494, sign: false });
+    data.append(FP8x23 { mag: 4493030, sign: true });
+    data.append(FP8x23 { mag: 4570125, sign: false });
+    data.append(FP8x23 { mag: 8010665, sign: false });
+    data.append(FP8x23 { mag: 5794037, sign: true });
+    data.append(FP8x23 { mag: 9849078, sign: false });
+    data.append(FP8x23 { mag: 11691798, sign: true });
+    data.append(FP8x23 { mag: 3652747, sign: false });
+    data.append(FP8x23 { mag: 1032666, sign: true });
+    data.append(FP8x23 { mag: 9259310, sign: true });
+    data.append(FP8x23 { mag: 7405492, sign: false });
+    data.append(FP8x23 { mag: 4687488, sign: true });
+    data.append(FP8x23 { mag: 1097650, sign: true });
+    data.append(FP8x23 { mag: 2213858, sign: true });
+    data.append(FP8x23 { mag: 1436205, sign: true });
+    data.append(FP8x23 { mag: 10226423, sign: false });
+    data.append(FP8x23 { mag: 6130226, sign: false });
+    data.append(FP8x23 { mag: 1214058, sign: false });
+    data.append(FP8x23 { mag: 12299984, sign: true });
+    data.append(FP8x23 { mag: 829240, sign: false });
+    data.append(FP8x23 { mag: 1612388, sign: false });
+    data.append(FP8x23 { mag: 6632529, sign: true });
+    data.append(FP8x23 { mag: 4410829, sign: true });
+    data.append(FP8x23 { mag: 20654302, sign: false });
+    data.append(FP8x23 { mag: 462475, sign: true });
+    data.append(FP8x23 { mag: 10502841, sign: true });
+    data.append(FP8x23 { mag: 7171902, sign: true });
+    data.append(FP8x23 { mag: 4204962, sign: false });
+    data.append(FP8x23 { mag: 17427142, sign: true });
+    data.append(FP8x23 { mag: 12555224, sign: true });
+    data.append(FP8x23 { mag: 8307885, sign: true });
+    data.append(FP8x23 { mag: 455536, sign: false });
+    data.append(FP8x23 { mag: 577191, sign: false });
+    data.append(FP8x23 { mag: 4412268, sign: true });
+    data.append(FP8x23 { mag: 15812229, sign: false });
+    data.append(FP8x23 { mag: 7098764, sign: true });
+    data.append(FP8x23 { mag: 9127468, sign: false });
+    data.append(FP8x23 { mag: 4753858, sign: false });
+    data.append(FP8x23 { mag: 2074029, sign: true });
+    data.append(FP8x23 { mag: 1651256, sign: false });
+    data.append(FP8x23 { mag: 9617324, sign: true });
+    data.append(FP8x23 { mag: 11400835, sign: false });
+    data.append(FP8x23 { mag: 4263073, sign: true });
+    data.append(FP8x23 { mag: 22170402, sign: false });
+    data.append(FP8x23 { mag: 7715608, sign: false });
+    data.append(FP8x23 { mag: 7511781, sign: true });
+    data.append(FP8x23 { mag: 8686402, sign: false });
+    data.append(FP8x23 { mag: 2710329, sign: true });
+    data.append(FP8x23 { mag: 5540998, sign: true });
+    data.append(FP8x23 { mag: 11608300, sign: true });
+    data.append(FP8x23 { mag: 3020404, sign: true });
+    data.append(FP8x23 { mag: 6342478, sign: true });
+    data.append(FP8x23 { mag: 9399735, sign: true });
+    data.append(FP8x23 { mag: 446463, sign: false });
+    data.append(FP8x23 { mag: 13691013, sign: true });
+    data.append(FP8x23 { mag: 11552903, sign: true });
+    data.append(FP8x23 { mag: 1204731, sign: false });
+    data.append(FP8x23 { mag: 1741592, sign: true });
+    data.append(FP8x23 { mag: 13103082, sign: false });
+    data.append(FP8x23 { mag: 3181444, sign: true });
+    data.append(FP8x23 { mag: 256975, sign: true });
+    data.append(FP8x23 { mag: 9440785, sign: true });
+    data.append(FP8x23 { mag: 2112590, sign: false });
+    data.append(FP8x23 { mag: 13404752, sign: false });
+    data.append(FP8x23 { mag: 760699, sign: false });
+    data.append(FP8x23 { mag: 1588793, sign: true });
+    data.append(FP8x23 { mag: 13026604, sign: true });
+    data.append(FP8x23 { mag: 452707, sign: false });
+    data.append(FP8x23 { mag: 7267348, sign: true });
+    data.append(FP8x23 { mag: 14737007, sign: false });
+    data.append(FP8x23 { mag: 8457998, sign: false });
+    data.append(FP8x23 { mag: 2233703, sign: false });
+    data.append(FP8x23 { mag: 3434673, sign: true });
+    data.append(FP8x23 { mag: 4280157, sign: true });
+    data.append(FP8x23 { mag: 2950181, sign: true });
+    data.append(FP8x23 { mag: 1385553, sign: false });
+    data.append(FP8x23 { mag: 17250056, sign: false });
+    data.append(FP8x23 { mag: 12716927, sign: true });
+    data.append(FP8x23 { mag: 2980452, sign: false });
+    data.append(FP8x23 { mag: 13031106, sign: true });
+    data.append(FP8x23 { mag: 4118717, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_4/input_1.cairo b/tests/nodes/layer_normalization_4d_axis_negative_4/input_1.cairo
new file mode 100644
index 000000000..cefc1df2c
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_4/input_1.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_1() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 765036, sign: false });
+    data.append(FP8x23 { mag: 1487908, sign: true });
+    data.append(FP8x23 { mag: 8979452, sign: true });
+    data.append(FP8x23 { mag: 2457758, sign: true });
+    data.append(FP8x23 { mag: 6713378, sign: true });
+    data.append(FP8x23 { mag: 9826923, sign: true });
+    data.append(FP8x23 { mag: 2590671, sign: false });
+    data.append(FP8x23 { mag: 3000698, sign: true });
+    data.append(FP8x23 { mag: 1027684, sign: true });
+    data.append(FP8x23 { mag: 6534988, sign: true });
+    data.append(FP8x23 { mag: 8946545, sign: false });
+    data.append(FP8x23 { mag: 4391800, sign: true });
+    data.append(FP8x23 { mag: 7641860, sign: true });
+    data.append(FP8x23 { mag: 11675021, sign: false });
+    data.append(FP8x23 { mag: 7466702, sign: false });
+    data.append(FP8x23 { mag: 395181, sign: true });
+    data.append(FP8x23 { mag: 365436, sign: true });
+    data.append(FP8x23 { mag: 15999312, sign: true });
+    data.append(FP8x23 { mag: 11565880, sign: false });
+    data.append(FP8x23 { mag: 9282480, sign: false });
+    data.append(FP8x23 { mag: 3785428, sign: true });
+    data.append(FP8x23 { mag: 18869726, sign: true });
+    data.append(FP8x23 { mag: 6981482, sign: true });
+    data.append(FP8x23 { mag: 7335699, sign: false });
+    data.append(FP8x23 { mag: 3393176, sign: true });
+    data.append(FP8x23 { mag: 17918872, sign: true });
+    data.append(FP8x23 { mag: 4778393, sign: true });
+    data.append(FP8x23 { mag: 15076621, sign: true });
+    data.append(FP8x23 { mag: 2537182, sign: false });
+    data.append(FP8x23 { mag: 13904034, sign: false });
+    data.append(FP8x23 { mag: 4360076, sign: true });
+    data.append(FP8x23 { mag: 15481889, sign: false });
+    data.append(FP8x23 { mag: 2741193, sign: false });
+    data.append(FP8x23 { mag: 465479, sign: true });
+    data.append(FP8x23 { mag: 2151935, sign: true });
+    data.append(FP8x23 { mag: 3630430, sign: true });
+    data.append(FP8x23 { mag: 8001601, sign: false });
+    data.append(FP8x23 { mag: 10861003, sign: true });
+    data.append(FP8x23 { mag: 15152404, sign: false });
+    data.append(FP8x23 { mag: 12626251, sign: true });
+    data.append(FP8x23 { mag: 4662175, sign: true });
+    data.append(FP8x23 { mag: 1467433, sign: false });
+    data.append(FP8x23 { mag: 18234838, sign: false });
+    data.append(FP8x23 { mag: 15142594, sign: false });
+    data.append(FP8x23 { mag: 797950, sign: false });
+    data.append(FP8x23 { mag: 6158520, sign: true });
+    data.append(FP8x23 { mag: 1464793, sign: false });
+    data.append(FP8x23 { mag: 9229589, sign: false });
+    data.append(FP8x23 { mag: 2331004, sign: false });
+    data.append(FP8x23 { mag: 4402549, sign: true });
+    data.append(FP8x23 { mag: 5288030, sign: false });
+    data.append(FP8x23 { mag: 8947151, sign: true });
+    data.append(FP8x23 { mag: 13011209, sign: false });
+    data.append(FP8x23 { mag: 14550682, sign: true });
+    data.append(FP8x23 { mag: 19891878, sign: false });
+    data.append(FP8x23 { mag: 8668230, sign: false });
+    data.append(FP8x23 { mag: 6457287, sign: false });
+    data.append(FP8x23 { mag: 2427503, sign: false });
+    data.append(FP8x23 { mag: 12205684, sign: false });
+    data.append(FP8x23 { mag: 2003377, sign: false });
+    data.append(FP8x23 { mag: 11948757, sign: false });
+    data.append(FP8x23 { mag: 1542336, sign: true });
+    data.append(FP8x23 { mag: 762426, sign: false });
+    data.append(FP8x23 { mag: 7311585, sign: true });
+    data.append(FP8x23 { mag: 4824334, sign: true });
+    data.append(FP8x23 { mag: 5502968, sign: true });
+    data.append(FP8x23 { mag: 12092839, sign: true });
+    data.append(FP8x23 { mag: 3938484, sign: false });
+    data.append(FP8x23 { mag: 13236991, sign: false });
+    data.append(FP8x23 { mag: 13323203, sign: false });
+    data.append(FP8x23 { mag: 3383393, sign: true });
+    data.append(FP8x23 { mag: 2710544, sign: true });
+    data.append(FP8x23 { mag: 1101084, sign: true });
+    data.append(FP8x23 { mag: 8167031, sign: true });
+    data.append(FP8x23 { mag: 10908585, sign: false });
+    data.append(FP8x23 { mag: 5727771, sign: true });
+    data.append(FP8x23 { mag: 7391430, sign: true });
+    data.append(FP8x23 { mag: 9532868, sign: true });
+    data.append(FP8x23 { mag: 570993, sign: false });
+    data.append(FP8x23 { mag: 1661402, sign: true });
+    data.append(FP8x23 { mag: 8927581, sign: false });
+    data.append(FP8x23 { mag: 69087, sign: true });
+    data.append(FP8x23 { mag: 13202806, sign: true });
+    data.append(FP8x23 { mag: 4596499, sign: false });
+    data.append(FP8x23 { mag: 6458124, sign: false });
+    data.append(FP8x23 { mag: 16955478, sign: true });
+    data.append(FP8x23 { mag: 12176098, sign: false });
+    data.append(FP8x23 { mag: 9405263, sign: false });
+    data.append(FP8x23 { mag: 7171010, sign: true });
+    data.append(FP8x23 { mag: 2977961, sign: false });
+    data.append(FP8x23 { mag: 5183702, sign: false });
+    data.append(FP8x23 { mag: 15341216, sign: false });
+    data.append(FP8x23 { mag: 18373916, sign: false });
+    data.append(FP8x23 { mag: 3529331, sign: false });
+    data.append(FP8x23 { mag: 21097650, sign: false });
+    data.append(FP8x23 { mag: 3775469, sign: false });
+    data.append(FP8x23 { mag: 6304868, sign: false });
+    data.append(FP8x23 { mag: 3531356, sign: false });
+    data.append(FP8x23 { mag: 2877400, sign: true });
+    data.append(FP8x23 { mag: 12725005, sign: false });
+    data.append(FP8x23 { mag: 2672641, sign: true });
+    data.append(FP8x23 { mag: 2460268, sign: false });
+    data.append(FP8x23 { mag: 9700178, sign: false });
+    data.append(FP8x23 { mag: 1683799, sign: true });
+    data.append(FP8x23 { mag: 2685739, sign: true });
+    data.append(FP8x23 { mag: 2567529, sign: false });
+    data.append(FP8x23 { mag: 12283587, sign: false });
+    data.append(FP8x23 { mag: 903086, sign: true });
+    data.append(FP8x23 { mag: 20664720, sign: true });
+    data.append(FP8x23 { mag: 4231587, sign: false });
+    data.append(FP8x23 { mag: 3279288, sign: false });
+    data.append(FP8x23 { mag: 2866349, sign: true });
+    data.append(FP8x23 { mag: 11227992, sign: true });
+    data.append(FP8x23 { mag: 13129429, sign: true });
+    data.append(FP8x23 { mag: 3240746, sign: false });
+    data.append(FP8x23 { mag: 9516856, sign: true });
+    data.append(FP8x23 { mag: 6088257, sign: true });
+    data.append(FP8x23 { mag: 502779, sign: false });
+    data.append(FP8x23 { mag: 4585135, sign: true });
+    data.append(FP8x23 { mag: 9007322, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_4/input_2.cairo b/tests/nodes/layer_normalization_4d_axis_negative_4/input_2.cairo
new file mode 100644
index 000000000..993d1b2b5
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_4/input_2.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_2() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 1565206, sign: true });
+    data.append(FP8x23 { mag: 8440746, sign: false });
+    data.append(FP8x23 { mag: 73220, sign: true });
+    data.append(FP8x23 { mag: 9789189, sign: false });
+    data.append(FP8x23 { mag: 3941934, sign: false });
+    data.append(FP8x23 { mag: 870617, sign: false });
+    data.append(FP8x23 { mag: 9469178, sign: true });
+    data.append(FP8x23 { mag: 2579358, sign: true });
+    data.append(FP8x23 { mag: 11437672, sign: true });
+    data.append(FP8x23 { mag: 4638952, sign: false });
+    data.append(FP8x23 { mag: 6273082, sign: true });
+    data.append(FP8x23 { mag: 6222525, sign: true });
+    data.append(FP8x23 { mag: 10101133, sign: true });
+    data.append(FP8x23 { mag: 5359626, sign: true });
+    data.append(FP8x23 { mag: 3085420, sign: false });
+    data.append(FP8x23 { mag: 9194004, sign: false });
+    data.append(FP8x23 { mag: 6966236, sign: false });
+    data.append(FP8x23 { mag: 6880860, sign: true });
+    data.append(FP8x23 { mag: 5546158, sign: false });
+    data.append(FP8x23 { mag: 6934687, sign: true });
+    data.append(FP8x23 { mag: 987627, sign: true });
+    data.append(FP8x23 { mag: 4104909, sign: false });
+    data.append(FP8x23 { mag: 10332664, sign: true });
+    data.append(FP8x23 { mag: 6589758, sign: true });
+    data.append(FP8x23 { mag: 13722424, sign: true });
+    data.append(FP8x23 { mag: 9462928, sign: true });
+    data.append(FP8x23 { mag: 13947351, sign: true });
+    data.append(FP8x23 { mag: 5509455, sign: true });
+    data.append(FP8x23 { mag: 644274, sign: true });
+    data.append(FP8x23 { mag: 2030384, sign: true });
+    data.append(FP8x23 { mag: 1089765, sign: true });
+    data.append(FP8x23 { mag: 175051, sign: false });
+    data.append(FP8x23 { mag: 10990995, sign: false });
+    data.append(FP8x23 { mag: 950685, sign: true });
+    data.append(FP8x23 { mag: 6512231, sign: false });
+    data.append(FP8x23 { mag: 980559, sign: true });
+    data.append(FP8x23 { mag: 2639622, sign: true });
+    data.append(FP8x23 { mag: 6566764, sign: true });
+    data.append(FP8x23 { mag: 5196593, sign: true });
+    data.append(FP8x23 { mag: 12299425, sign: true });
+    data.append(FP8x23 { mag: 98068, sign: true });
+    data.append(FP8x23 { mag: 5477706, sign: false });
+    data.append(FP8x23 { mag: 540028, sign: false });
+    data.append(FP8x23 { mag: 6323924, sign: false });
+    data.append(FP8x23 { mag: 4402990, sign: false });
+    data.append(FP8x23 { mag: 4425289, sign: true });
+    data.append(FP8x23 { mag: 1864532, sign: true });
+    data.append(FP8x23 { mag: 3821369, sign: true });
+    data.append(FP8x23 { mag: 4152119, sign: true });
+    data.append(FP8x23 { mag: 13493616, sign: false });
+    data.append(FP8x23 { mag: 16485032, sign: false });
+    data.append(FP8x23 { mag: 47558, sign: true });
+    data.append(FP8x23 { mag: 4175409, sign: false });
+    data.append(FP8x23 { mag: 1769218, sign: true });
+    data.append(FP8x23 { mag: 1835677, sign: true });
+    data.append(FP8x23 { mag: 6190130, sign: false });
+    data.append(FP8x23 { mag: 9290798, sign: true });
+    data.append(FP8x23 { mag: 14518570, sign: false });
+    data.append(FP8x23 { mag: 5531764, sign: false });
+    data.append(FP8x23 { mag: 3781891, sign: true });
+    data.append(FP8x23 { mag: 8213439, sign: true });
+    data.append(FP8x23 { mag: 5323399, sign: false });
+    data.append(FP8x23 { mag: 15125746, sign: true });
+    data.append(FP8x23 { mag: 3776717, sign: false });
+    data.append(FP8x23 { mag: 5903805, sign: false });
+    data.append(FP8x23 { mag: 735915, sign: true });
+    data.append(FP8x23 { mag: 2326781, sign: false });
+    data.append(FP8x23 { mag: 10726848, sign: true });
+    data.append(FP8x23 { mag: 8423845, sign: false });
+    data.append(FP8x23 { mag: 5989648, sign: true });
+    data.append(FP8x23 { mag: 9314061, sign: false });
+    data.append(FP8x23 { mag: 11670752, sign: false });
+    data.append(FP8x23 { mag: 364021, sign: false });
+    data.append(FP8x23 { mag: 1428671, sign: true });
+    data.append(FP8x23 { mag: 2859786, sign: true });
+    data.append(FP8x23 { mag: 367369, sign: false });
+    data.append(FP8x23 { mag: 2107786, sign: true });
+    data.append(FP8x23 { mag: 5419940, sign: true });
+    data.append(FP8x23 { mag: 8748640, sign: false });
+    data.append(FP8x23 { mag: 6783550, sign: true });
+    data.append(FP8x23 { mag: 1174084, sign: false });
+    data.append(FP8x23 { mag: 1244741, sign: false });
+    data.append(FP8x23 { mag: 628290, sign: false });
+    data.append(FP8x23 { mag: 7245872, sign: false });
+    data.append(FP8x23 { mag: 10358153, sign: true });
+    data.append(FP8x23 { mag: 11037845, sign: true });
+    data.append(FP8x23 { mag: 7181495, sign: false });
+    data.append(FP8x23 { mag: 9622438, sign: true });
+    data.append(FP8x23 { mag: 433991, sign: true });
+    data.append(FP8x23 { mag: 1950004, sign: true });
+    data.append(FP8x23 { mag: 172993, sign: false });
+    data.append(FP8x23 { mag: 5089314, sign: true });
+    data.append(FP8x23 { mag: 2494218, sign: true });
+    data.append(FP8x23 { mag: 6786513, sign: true });
+    data.append(FP8x23 { mag: 757032, sign: false });
+    data.append(FP8x23 { mag: 265511, sign: false });
+    data.append(FP8x23 { mag: 3655001, sign: true });
+    data.append(FP8x23 { mag: 1933308, sign: false });
+    data.append(FP8x23 { mag: 13213099, sign: true });
+    data.append(FP8x23 { mag: 4142122, sign: false });
+    data.append(FP8x23 { mag: 1604209, sign: false });
+    data.append(FP8x23 { mag: 11013449, sign: false });
+    data.append(FP8x23 { mag: 9016796, sign: true });
+    data.append(FP8x23 { mag: 12822414, sign: true });
+    data.append(FP8x23 { mag: 5674089, sign: true });
+    data.append(FP8x23 { mag: 7284811, sign: false });
+    data.append(FP8x23 { mag: 5380566, sign: false });
+    data.append(FP8x23 { mag: 1200376, sign: true });
+    data.append(FP8x23 { mag: 4449674, sign: false });
+    data.append(FP8x23 { mag: 2509265, sign: true });
+    data.append(FP8x23 { mag: 11423904, sign: false });
+    data.append(FP8x23 { mag: 182801, sign: true });
+    data.append(FP8x23 { mag: 61327, sign: true });
+    data.append(FP8x23 { mag: 20514282, sign: false });
+    data.append(FP8x23 { mag: 2488749, sign: true });
+    data.append(FP8x23 { mag: 4664432, sign: false });
+    data.append(FP8x23 { mag: 12670565, sign: false });
+    data.append(FP8x23 { mag: 4800221, sign: true });
+    data.append(FP8x23 { mag: 5916248, sign: false });
+    data.append(FP8x23 { mag: 2870212, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_4d_axis_negative_4/output_0.cairo b/tests/nodes/layer_normalization_4d_axis_negative_4/output_0.cairo
new file mode 100644
index 000000000..915c0e6b9
--- /dev/null
+++ b/tests/nodes/layer_normalization_4d_axis_negative_4/output_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn output_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 2424743, sign: true });
+    data.append(FP8x23 { mag: 6230880, sign: false });
+    data.append(FP8x23 { mag: 1347738, sign: true });
+    data.append(FP8x23 { mag: 11835814, sign: false });
+    data.append(FP8x23 { mag: 8560172, sign: true });
+    data.append(FP8x23 { mag: 18353888, sign: true });
+    data.append(FP8x23 { mag: 9644270, sign: true });
+    data.append(FP8x23 { mag: 1531681, sign: true });
+    data.append(FP8x23 { mag: 12454719, sign: true });
+    data.append(FP8x23 { mag: 9308055, sign: false });
+    data.append(FP8x23 { mag: 11949580, sign: true });
+    data.append(FP8x23 { mag: 4039623, sign: true });
+    data.append(FP8x23 { mag: 31716300, sign: true });
+    data.append(FP8x23 { mag: 15707956, sign: true });
+    data.append(FP8x23 { mag: 8486900, sign: false });
+    data.append(FP8x23 { mag: 9430574, sign: false });
+    data.append(FP8x23 { mag: 6804483, sign: false });
+    data.append(FP8x23 { mag: 2207451, sign: true });
+    data.append(FP8x23 { mag: 13474301, sign: false });
+    data.append(FP8x23 { mag: 16478194, sign: true });
+    data.append(FP8x23 { mag: 150738, sign: false });
+    data.append(FP8x23 { mag: 13342368, sign: true });
+    data.append(FP8x23 { mag: 10913451, sign: true });
+    data.append(FP8x23 { mag: 7620089, sign: false });
+    data.append(FP8x23 { mag: 16835534, sign: true });
+    data.append(FP8x23 { mag: 15912140, sign: false });
+    data.append(FP8x23 { mag: 15652847, sign: true });
+    data.append(FP8x23 { mag: 6149347, sign: true });
+    data.append(FP8x23 { mag: 1506302, sign: true });
+    data.append(FP8x23 { mag: 1084043, sign: true });
+    data.append(FP8x23 { mag: 4556684, sign: false });
+    data.append(FP8x23 { mag: 2254544, sign: false });
+    data.append(FP8x23 { mag: 11823707, sign: false });
+    data.append(FP8x23 { mag: 1215629, sign: true });
+    data.append(FP8x23 { mag: 4100511, sign: false });
+    data.append(FP8x23 { mag: 3945443, sign: false });
+    data.append(FP8x23 { mag: 13197695, sign: true });
+    data.append(FP8x23 { mag: 2657681, sign: true });
+    data.append(FP8x23 { mag: 16811968, sign: true });
+    data.append(FP8x23 { mag: 16634120, sign: true });
+    data.append(FP8x23 { mag: 2191530, sign: false });
+    data.append(FP8x23 { mag: 6326118, sign: false });
+    data.append(FP8x23 { mag: 18484424, sign: false });
+    data.append(FP8x23 { mag: 3436880, sign: true });
+    data.append(FP8x23 { mag: 5361301, sign: false });
+    data.append(FP8x23 { mag: 3829618, sign: false });
+    data.append(FP8x23 { mag: 1176182, sign: true });
+    data.append(FP8x23 { mag: 4586050, sign: true });
+    data.append(FP8x23 { mag: 6607650, sign: true });
+    data.append(FP8x23 { mag: 9475522, sign: false });
+    data.append(FP8x23 { mag: 13766757, sign: false });
+    data.append(FP8x23 { mag: 762316, sign: false });
+    data.append(FP8x23 { mag: 1284229, sign: false });
+    data.append(FP8x23 { mag: 129063, sign: false });
+    data.append(FP8x23 { mag: 22939360, sign: false });
+    data.append(FP8x23 { mag: 12797228, sign: false });
+    data.append(FP8x23 { mag: 8114181, sign: true });
+    data.append(FP8x23 { mag: 11090552, sign: false });
+    data.append(FP8x23 { mag: 7201685, sign: false });
+    data.append(FP8x23 { mag: 3322697, sign: true });
+    data.append(FP8x23 { mag: 17097544, sign: true });
+    data.append(FP8x23 { mag: 6065884, sign: false });
+    data.append(FP8x23 { mag: 13238165, sign: true });
+    data.append(FP8x23 { mag: 3890634, sign: false });
+    data.append(FP8x23 { mag: 11693645, sign: false });
+    data.append(FP8x23 { mag: 3705815, sign: false });
+    data.append(FP8x23 { mag: 4143846, sign: true });
+    data.append(FP8x23 { mag: 18670984, sign: true });
+    data.append(FP8x23 { mag: 10667477, sign: true });
+    data.append(FP8x23 { mag: 18529098, sign: true });
+    data.append(FP8x23 { mag: 9000333, sign: false });
+    data.append(FP8x23 { mag: 11380511, sign: false });
+    data.append(FP8x23 { mag: 894273, sign: false });
+    data.append(FP8x23 { mag: 16982722, sign: true });
+    data.append(FP8x23 { mag: 11570541, sign: true });
+    data.append(FP8x23 { mag: 6023858, sign: true });
+    data.append(FP8x23 { mag: 6541442, sign: true });
+    data.append(FP8x23 { mag: 3458935, sign: true });
+    data.append(FP8x23 { mag: 8882136, sign: false });
+    data.append(FP8x23 { mag: 4963218, sign: true });
+    data.append(FP8x23 { mag: 13530222, sign: false });
+    data.append(FP8x23 { mag: 1276796, sign: false });
+    data.append(FP8x23 { mag: 34420188, sign: true });
+    data.append(FP8x23 { mag: 11609153, sign: false });
+    data.append(FP8x23 { mag: 15829801, sign: true });
+    data.append(FP8x23 { mag: 29074996, sign: true });
+    data.append(FP8x23 { mag: 3762691, sign: false });
+    data.append(FP8x23 { mag: 15404224, sign: true });
+    data.append(FP8x23 { mag: 9107420, sign: false });
+    data.append(FP8x23 { mag: 2895097, sign: true });
+    data.append(FP8x23 { mag: 3503790, sign: true });
+    data.append(FP8x23 { mag: 21504250, sign: true });
+    data.append(FP8x23 { mag: 810151, sign: true });
+    data.append(FP8x23 { mag: 12349691, sign: true });
+    data.append(FP8x23 { mag: 27176632, sign: true });
+    data.append(FP8x23 { mag: 949305, sign: false });
+    data.append(FP8x23 { mag: 4704694, sign: true });
+    data.append(FP8x23 { mag: 7530050, sign: false });
+    data.append(FP8x23 { mag: 12245252, sign: true });
+    data.append(FP8x23 { mag: 4252376, sign: false });
+    data.append(FP8x23 { mag: 4476849, sign: false });
+    data.append(FP8x23 { mag: 11722556, sign: false });
+    data.append(FP8x23 { mag: 6701965, sign: false });
+    data.append(FP8x23 { mag: 13039167, sign: true });
+    data.append(FP8x23 { mag: 5275358, sign: true });
+    data.append(FP8x23 { mag: 3438951, sign: false });
+    data.append(FP8x23 { mag: 6515470, sign: false });
+    data.append(FP8x23 { mag: 461279, sign: true });
+    data.append(FP8x23 { mag: 32284742, sign: true });
+    data.append(FP8x23 { mag: 1878248, sign: false });
+    data.append(FP8x23 { mag: 12415928, sign: false });
+    data.append(FP8x23 { mag: 866962, sign: false });
+    data.append(FP8x23 { mag: 5170767, sign: false });
+    data.append(FP8x23 { mag: 24572292, sign: false });
+    data.append(FP8x23 { mag: 1832665, sign: true });
+    data.append(FP8x23 { mag: 15074726, sign: true });
+    data.append(FP8x23 { mag: 21567628, sign: false });
+    data.append(FP8x23 { mag: 4603829, sign: true });
+    data.append(FP8x23 { mag: 12786682, sign: false });
+    data.append(FP8x23 { mag: 1155521, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_default_axis.cairo b/tests/nodes/layer_normalization_default_axis.cairo
new file mode 100644
index 000000000..1d1e1db90
--- /dev/null
+++ b/tests/nodes/layer_normalization_default_axis.cairo
@@ -0,0 +1,28 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{IntegerTrait, i32, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_default_axis() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1, Option::Some(@input_2), Option::None, Option::None, Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_default_axis/input_0.cairo b/tests/nodes/layer_normalization_default_axis/input_0.cairo
new file mode 100644
index 000000000..d8da4e64e
--- /dev/null
+++ b/tests/nodes/layer_normalization_default_axis/input_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 162162, sign: true });
+    data.append(FP16x16 { mag: 29273, sign: true });
+    data.append(FP16x16 { mag: 155457, sign: true });
+    data.append(FP16x16 { mag: 50795, sign: false });
+    data.append(FP16x16 { mag: 17629, sign: true });
+    data.append(FP16x16 { mag: 26642, sign: false });
+    data.append(FP16x16 { mag: 29753, sign: true });
+    data.append(FP16x16 { mag: 76017, sign: true });
+    data.append(FP16x16 { mag: 58145, sign: true });
+    data.append(FP16x16 { mag: 81397, sign: false });
+    data.append(FP16x16 { mag: 56926, sign: true });
+    data.append(FP16x16 { mag: 92380, sign: true });
+    data.append(FP16x16 { mag: 5200, sign: true });
+    data.append(FP16x16 { mag: 24905, sign: false });
+    data.append(FP16x16 { mag: 14892, sign: true });
+    data.append(FP16x16 { mag: 37085, sign: false });
+    data.append(FP16x16 { mag: 120688, sign: false });
+    data.append(FP16x16 { mag: 24518, sign: true });
+    data.append(FP16x16 { mag: 56437, sign: true });
+    data.append(FP16x16 { mag: 68199, sign: true });
+    data.append(FP16x16 { mag: 49008, sign: true });
+    data.append(FP16x16 { mag: 6080, sign: false });
+    data.append(FP16x16 { mag: 72783, sign: true });
+    data.append(FP16x16 { mag: 100706, sign: true });
+    data.append(FP16x16 { mag: 86624, sign: false });
+    data.append(FP16x16 { mag: 72942, sign: false });
+    data.append(FP16x16 { mag: 416, sign: false });
+    data.append(FP16x16 { mag: 60084, sign: false });
+    data.append(FP16x16 { mag: 13763, sign: false });
+    data.append(FP16x16 { mag: 29895, sign: false });
+    data.append(FP16x16 { mag: 70876, sign: false });
+    data.append(FP16x16 { mag: 29792, sign: false });
+    data.append(FP16x16 { mag: 50085, sign: false });
+    data.append(FP16x16 { mag: 14512, sign: false });
+    data.append(FP16x16 { mag: 69956, sign: true });
+    data.append(FP16x16 { mag: 23179, sign: true });
+    data.append(FP16x16 { mag: 34593, sign: false });
+    data.append(FP16x16 { mag: 94827, sign: true });
+    data.append(FP16x16 { mag: 36976, sign: true });
+    data.append(FP16x16 { mag: 66090, sign: true });
+    data.append(FP16x16 { mag: 46310, sign: true });
+    data.append(FP16x16 { mag: 1270, sign: false });
+    data.append(FP16x16 { mag: 10179, sign: false });
+    data.append(FP16x16 { mag: 2421, sign: false });
+    data.append(FP16x16 { mag: 41063, sign: false });
+    data.append(FP16x16 { mag: 10014, sign: true });
+    data.append(FP16x16 { mag: 23971, sign: true });
+    data.append(FP16x16 { mag: 38734, sign: false });
+    data.append(FP16x16 { mag: 252987, sign: false });
+    data.append(FP16x16 { mag: 15276, sign: false });
+    data.append(FP16x16 { mag: 94190, sign: true });
+    data.append(FP16x16 { mag: 152835, sign: true });
+    data.append(FP16x16 { mag: 120387, sign: true });
+    data.append(FP16x16 { mag: 11624, sign: true });
+    data.append(FP16x16 { mag: 83095, sign: false });
+    data.append(FP16x16 { mag: 120401, sign: true });
+    data.append(FP16x16 { mag: 32613, sign: false });
+    data.append(FP16x16 { mag: 125957, sign: true });
+    data.append(FP16x16 { mag: 44002, sign: true });
+    data.append(FP16x16 { mag: 98578, sign: false });
+    data.append(FP16x16 { mag: 40668, sign: true });
+    data.append(FP16x16 { mag: 15027, sign: true });
+    data.append(FP16x16 { mag: 80889, sign: true });
+    data.append(FP16x16 { mag: 46530, sign: true });
+    data.append(FP16x16 { mag: 14759, sign: false });
+    data.append(FP16x16 { mag: 67967, sign: false });
+    data.append(FP16x16 { mag: 83003, sign: false });
+    data.append(FP16x16 { mag: 59413, sign: true });
+    data.append(FP16x16 { mag: 24514, sign: false });
+    data.append(FP16x16 { mag: 33820, sign: true });
+    data.append(FP16x16 { mag: 71782, sign: false });
+    data.append(FP16x16 { mag: 102396, sign: false });
+    data.append(FP16x16 { mag: 118, sign: true });
+    data.append(FP16x16 { mag: 1158, sign: true });
+    data.append(FP16x16 { mag: 60291, sign: true });
+    data.append(FP16x16 { mag: 41730, sign: true });
+    data.append(FP16x16 { mag: 17552, sign: true });
+    data.append(FP16x16 { mag: 27965, sign: true });
+    data.append(FP16x16 { mag: 12189, sign: true });
+    data.append(FP16x16 { mag: 1007, sign: true });
+    data.append(FP16x16 { mag: 75390, sign: false });
+    data.append(FP16x16 { mag: 29536, sign: true });
+    data.append(FP16x16 { mag: 39461, sign: true });
+    data.append(FP16x16 { mag: 36937, sign: false });
+    data.append(FP16x16 { mag: 74171, sign: false });
+    data.append(FP16x16 { mag: 18374, sign: false });
+    data.append(FP16x16 { mag: 25540, sign: false });
+    data.append(FP16x16 { mag: 72170, sign: true });
+    data.append(FP16x16 { mag: 13960, sign: true });
+    data.append(FP16x16 { mag: 25485, sign: false });
+    data.append(FP16x16 { mag: 36529, sign: true });
+    data.append(FP16x16 { mag: 157843, sign: false });
+    data.append(FP16x16 { mag: 18027, sign: false });
+    data.append(FP16x16 { mag: 49602, sign: false });
+    data.append(FP16x16 { mag: 78001, sign: false });
+    data.append(FP16x16 { mag: 12776, sign: false });
+    data.append(FP16x16 { mag: 2507, sign: false });
+    data.append(FP16x16 { mag: 85706, sign: false });
+    data.append(FP16x16 { mag: 76268, sign: true });
+    data.append(FP16x16 { mag: 20694, sign: false });
+    data.append(FP16x16 { mag: 111079, sign: true });
+    data.append(FP16x16 { mag: 118166, sign: false });
+    data.append(FP16x16 { mag: 84064, sign: false });
+    data.append(FP16x16 { mag: 146152, sign: false });
+    data.append(FP16x16 { mag: 99879, sign: false });
+    data.append(FP16x16 { mag: 101373, sign: false });
+    data.append(FP16x16 { mag: 138415, sign: false });
+    data.append(FP16x16 { mag: 34200, sign: true });
+    data.append(FP16x16 { mag: 61986, sign: true });
+    data.append(FP16x16 { mag: 99859, sign: false });
+    data.append(FP16x16 { mag: 35109, sign: true });
+    data.append(FP16x16 { mag: 28029, sign: false });
+    data.append(FP16x16 { mag: 56997, sign: false });
+    data.append(FP16x16 { mag: 19277, sign: false });
+    data.append(FP16x16 { mag: 38195, sign: true });
+    data.append(FP16x16 { mag: 56295, sign: true });
+    data.append(FP16x16 { mag: 25408, sign: false });
+    data.append(FP16x16 { mag: 72993, sign: true });
+    data.append(FP16x16 { mag: 28011, sign: false });
+    data.append(FP16x16 { mag: 63069, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_default_axis/input_1.cairo b/tests/nodes/layer_normalization_default_axis/input_1.cairo
new file mode 100644
index 000000000..f180a8bd1
--- /dev/null
+++ b/tests/nodes/layer_normalization_default_axis/input_1.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 8602, sign: true });
+    data.append(FP16x16 { mag: 134121, sign: true });
+    data.append(FP16x16 { mag: 39230, sign: false });
+    data.append(FP16x16 { mag: 17052, sign: true });
+    data.append(FP16x16 { mag: 24886, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_default_axis/input_2.cairo b/tests/nodes/layer_normalization_default_axis/input_2.cairo
new file mode 100644
index 000000000..540f8c89b
--- /dev/null
+++ b/tests/nodes/layer_normalization_default_axis/input_2.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 35619, sign: true });
+    data.append(FP16x16 { mag: 31160, sign: false });
+    data.append(FP16x16 { mag: 51197, sign: true });
+    data.append(FP16x16 { mag: 150071, sign: true });
+    data.append(FP16x16 { mag: 57770, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_default_axis/output_0.cairo b/tests/nodes/layer_normalization_default_axis/output_0.cairo
new file mode 100644
index 000000000..78cc8f3c2
--- /dev/null
+++ b/tests/nodes/layer_normalization_default_axis/output_0.cairo
@@ -0,0 +1,135 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 25327, sign: true });
+    data.append(FP16x16 { mag: 22863, sign: true });
+    data.append(FP16x16 { mag: 94966, sign: true });
+    data.append(FP16x16 { mag: 173370, sign: true });
+    data.append(FP16x16 { mag: 71281, sign: false });
+    data.append(FP16x16 { mag: 41238, sign: true });
+    data.append(FP16x16 { mag: 74203, sign: false });
+    data.append(FP16x16 { mag: 95139, sign: true });
+    data.append(FP16x16 { mag: 136235, sign: true });
+    data.append(FP16x16 { mag: 97567, sign: false });
+    data.append(FP16x16 { mag: 29760, sign: true });
+    data.append(FP16x16 { mag: 238045, sign: false });
+    data.append(FP16x16 { mag: 28606, sign: true });
+    data.append(FP16x16 { mag: 172365, sign: true });
+    data.append(FP16x16 { mag: 66239, sign: false });
+    data.append(FP16x16 { mag: 39976, sign: true });
+    data.append(FP16x16 { mag: 197413, sign: true });
+    data.append(FP16x16 { mag: 65945, sign: true });
+    data.append(FP16x16 { mag: 135863, sign: true });
+    data.append(FP16x16 { mag: 32841, sign: false });
+    data.append(FP16x16 { mag: 32630, sign: true });
+    data.append(FP16x16 { mag: 33602, sign: true });
+    data.append(FP16x16 { mag: 78882, sign: true });
+    data.append(FP16x16 { mag: 130860, sign: true });
+    data.append(FP16x16 { mag: 99996, sign: false });
+    data.append(FP16x16 { mag: 47418, sign: true });
+    data.append(FP16x16 { mag: 202789, sign: false });
+    data.append(FP16x16 { mag: 15824, sign: true });
+    data.append(FP16x16 { mag: 136570, sign: true });
+    data.append(FP16x16 { mag: 52743, sign: false });
+    data.append(FP16x16 { mag: 44830, sign: true });
+    data.append(FP16x16 { mag: 1418, sign: false });
+    data.append(FP16x16 { mag: 26045, sign: true });
+    data.append(FP16x16 { mag: 148467, sign: true });
+    data.append(FP16x16 { mag: 11987, sign: false });
+    data.append(FP16x16 { mag: 38404, sign: true });
+    data.append(FP16x16 { mag: 189961, sign: true });
+    data.append(FP16x16 { mag: 102957, sign: true });
+    data.append(FP16x16 { mag: 150196, sign: true });
+    data.append(FP16x16 { mag: 41335, sign: false });
+    data.append(FP16x16 { mag: 20875, sign: true });
+    data.append(FP16x16 { mag: 33337, sign: false });
+    data.append(FP16x16 { mag: 39363, sign: true });
+    data.append(FP16x16 { mag: 150495, sign: true });
+    data.append(FP16x16 { mag: 92702, sign: false });
+    data.append(FP16x16 { mag: 30142, sign: true });
+    data.append(FP16x16 { mag: 134994, sign: false });
+    data.append(FP16x16 { mag: 57330, sign: true });
+    data.append(FP16x16 { mag: 183402, sign: true });
+    data.append(FP16x16 { mag: 48127, sign: false });
+    data.append(FP16x16 { mag: 32082, sign: true });
+    data.append(FP16x16 { mag: 178676, sign: false });
+    data.append(FP16x16 { mag: 79394, sign: true });
+    data.append(FP16x16 { mag: 159596, sign: true });
+    data.append(FP16x16 { mag: 99358, sign: false });
+    data.append(FP16x16 { mag: 26881, sign: true });
+    data.append(FP16x16 { mag: 67973, sign: true });
+    data.append(FP16x16 { mag: 93546, sign: true });
+    data.append(FP16x16 { mag: 147691, sign: true });
+    data.append(FP16x16 { mag: 94992, sign: false });
+    data.append(FP16x16 { mag: 33741, sign: true });
+    data.append(FP16x16 { mag: 46849, sign: true });
+    data.append(FP16x16 { mag: 108987, sign: true });
+    data.append(FP16x16 { mag: 143230, sign: true });
+    data.append(FP16x16 { mag: 95371, sign: false });
+    data.append(FP16x16 { mag: 43596, sign: true });
+    data.append(FP16x16 { mag: 129515, sign: true });
+    data.append(FP16x16 { mag: 104770, sign: true });
+    data.append(FP16x16 { mag: 152546, sign: true });
+    data.append(FP16x16 { mag: 35250, sign: false });
+    data.append(FP16x16 { mag: 42942, sign: true });
+    data.append(FP16x16 { mag: 153994, sign: true });
+    data.append(FP16x16 { mag: 66548, sign: true });
+    data.append(FP16x16 { mag: 143091, sign: true });
+    data.append(FP16x16 { mag: 22149, sign: false });
+    data.append(FP16x16 { mag: 22209, sign: true });
+    data.append(FP16x16 { mag: 6651, sign: false });
+    data.append(FP16x16 { mag: 73456, sign: true });
+    data.append(FP16x16 { mag: 159774, sign: true });
+    data.append(FP16x16 { mag: 91978, sign: false });
+    data.append(FP16x16 { mag: 44648, sign: true });
+    data.append(FP16x16 { mag: 175059, sign: false });
+    data.append(FP16x16 { mag: 101164, sign: true });
+    data.append(FP16x16 { mag: 154706, sign: true });
+    data.append(FP16x16 { mag: 83280, sign: false });
+    data.append(FP16x16 { mag: 40617, sign: true });
+    data.append(FP16x16 { mag: 72477, sign: true });
+    data.append(FP16x16 { mag: 123424, sign: true });
+    data.append(FP16x16 { mag: 145228, sign: true });
+    data.append(FP16x16 { mag: 76964, sign: false });
+    data.append(FP16x16 { mag: 23646, sign: true });
+    data.append(FP16x16 { mag: 185699, sign: true });
+    data.append(FP16x16 { mag: 72671, sign: true });
+    data.append(FP16x16 { mag: 149071, sign: true });
+    data.append(FP16x16 { mag: 67251, sign: false });
+    data.append(FP16x16 { mag: 36234, sign: true });
+    data.append(FP16x16 { mag: 48229, sign: false });
+    data.append(FP16x16 { mag: 6980, sign: false });
+    data.append(FP16x16 { mag: 121901, sign: true });
+    data.append(FP16x16 { mag: 63362, sign: false });
+    data.append(FP16x16 { mag: 18857, sign: true });
+    data.append(FP16x16 { mag: 43105, sign: true });
+    data.append(FP16x16 { mag: 44077, sign: true });
+    data.append(FP16x16 { mag: 164722, sign: true });
+    data.append(FP16x16 { mag: 66582, sign: false });
+    data.append(FP16x16 { mag: 41234, sign: true });
+    data.append(FP16x16 { mag: 117951, sign: true });
+    data.append(FP16x16 { mag: 91492, sign: true });
+    data.append(FP16x16 { mag: 126684, sign: true });
+    data.append(FP16x16 { mag: 73548, sign: false });
+    data.append(FP16x16 { mag: 26057, sign: true });
+    data.append(FP16x16 { mag: 47620, sign: true });
+    data.append(FP16x16 { mag: 2425, sign: false });
+    data.append(FP16x16 { mag: 156071, sign: true });
+    data.append(FP16x16 { mag: 28040, sign: false });
+    data.append(FP16x16 { mag: 30148, sign: true });
+    data.append(FP16x16 { mag: 128023, sign: true });
+    data.append(FP16x16 { mag: 90764, sign: true });
+    data.append(FP16x16 { mag: 171299, sign: true });
+    data.append(FP16x16 { mag: 38179, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_test.cairo b/tests/nodes/layer_normalization_test.cairo
new file mode 100644
index 000000000..02235bd8b
--- /dev/null
+++ b/tests/nodes/layer_normalization_test.cairo
@@ -0,0 +1,28 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_layer_normalization_test() {
+    let input_0 = input_0::input_0();
+    let input_1 = input_1::input_1();
+    let input_2 = input_2::input_2();
+    let z_0 = output_0::output_0();
+
+    let (y_0, _, _) = input_0
+        .layer_normalization(
+            @input_1, Option::Some(@input_2), Option::None, Option::None, Option::None
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/layer_normalization_test/input_0.cairo b/tests/nodes/layer_normalization_test/input_0.cairo
new file mode 100644
index 000000000..6a1e2573e
--- /dev/null
+++ b/tests/nodes/layer_normalization_test/input_0.cairo
@@ -0,0 +1,25 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 41143, sign: true });
+    data.append(FP16x16 { mag: 51803, sign: false });
+    data.append(FP16x16 { mag: 113556, sign: false });
+    data.append(FP16x16 { mag: 64774, sign: false });
+    data.append(FP16x16 { mag: 866, sign: false });
+    data.append(FP16x16 { mag: 698, sign: true });
+    data.append(FP16x16 { mag: 106500, sign: false });
+    data.append(FP16x16 { mag: 98929, sign: false });
+    data.append(FP16x16 { mag: 7551, sign: false });
+    data.append(FP16x16 { mag: 30689, sign: true });
+    data.append(FP16x16 { mag: 38325, sign: false });
+    data.append(FP16x16 { mag: 48164, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_test/input_1.cairo b/tests/nodes/layer_normalization_test/input_1.cairo
new file mode 100644
index 000000000..c56fcc649
--- /dev/null
+++ b/tests/nodes/layer_normalization_test/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 49855, sign: false });
+    data.append(FP16x16 { mag: 150787, sign: false });
+    data.append(FP16x16 { mag: 83498, sign: true });
+    data.append(FP16x16 { mag: 30346, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_test/input_2.cairo b/tests/nodes/layer_normalization_test/input_2.cairo
new file mode 100644
index 000000000..9c662c873
--- /dev/null
+++ b/tests/nodes/layer_normalization_test/input_2.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 54864, sign: true });
+    data.append(FP16x16 { mag: 50952, sign: false });
+    data.append(FP16x16 { mag: 8870, sign: true });
+    data.append(FP16x16 { mag: 23216, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/layer_normalization_test/output_0.cairo b/tests/nodes/layer_normalization_test/output_0.cairo
new file mode 100644
index 000000000..c7ddbe92d
--- /dev/null
+++ b/tests/nodes/layer_normalization_test/output_0.cairo
@@ -0,0 +1,25 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 133576, sign: true });
+    data.append(FP16x16 { mag: 63223, sign: false });
+    data.append(FP16x16 { mag: 107763, sign: true });
+    data.append(FP16x16 { mag: 13716, sign: true });
+    data.append(FP16x16 { mag: 103890, sign: true });
+    data.append(FP16x16 { mag: 101915, sign: true });
+    data.append(FP16x16 { mag: 98400, sign: true });
+    data.append(FP16x16 { mag: 4851, sign: false });
+    data.append(FP16x16 { mag: 68295, sign: true });
+    data.append(FP16x16 { mag: 177139, sign: true });
+    data.append(FP16x16 { mag: 69916, sign: true });
+    data.append(FP16x16 { mag: 8677, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/reduce_l2_complex64_axis_0.cairo b/tests/nodes/reduce_l2_complex64_axis_0.cairo
new file mode 100644
index 000000000..bb1391263
--- /dev/null
+++ b/tests/nodes/reduce_l2_complex64_axis_0.cairo
@@ -0,0 +1,24 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::Complex64Tensor;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::Complex64TensorPartialEq;
+
+use orion::numbers::complex_number::complex64::Complex64Print;
+use orion::numbers::{NumberTrait, complex64};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_reduce_l2_complex64_axis_0() {
+    let input_0 = input_0::input_0();
+    let z_0 = output_0::output_0();
+
+    let y_0 = input_0.reduce_l2(0, true);
+
+    assert_eq(y_0, z_0);
+}
+
diff --git a/tests/nodes/reduce_l2_complex64_axis_0/input_0.cairo b/tests/nodes/reduce_l2_complex64_axis_0/input_0.cairo
new file mode 100644
index 000000000..6d7281bfb
--- /dev/null
+++ b/tests/nodes/reduce_l2_complex64_axis_0/input_0.cairo
@@ -0,0 +1,56 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::Complex64Tensor;
+use orion::numbers::{NumberTrait, complex64};
+use orion::numbers::{FixedTrait, FP64x64};
+
+fn input_0() -> Tensor<complex64> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data
+        .append(
+            complex64 {
+                real: FP64x64 { mag: 18446744073709551616, sign: false },
+                img: FP64x64 { mag: 36893488147419103232, sign: false }
+            }
+        );
+    data
+        .append(
+            complex64 {
+                real: FP64x64 { mag: 36893488147419103232, sign: false },
+                img: FP64x64 { mag: 18446744073709551616, sign: true }
+            }
+        );
+    data
+        .append(
+            complex64 {
+                real: FP64x64 { mag: 55340232221128654848, sign: false },
+                img: FP64x64 { mag: 55340232221128654848, sign: true }
+            }
+        );
+    data
+        .append(
+            complex64 {
+                real: FP64x64 { mag: 55340232221128654848, sign: false },
+                img: FP64x64 { mag: 36893488147419103232, sign: true }
+            }
+        );
+    data
+        .append(
+            complex64 {
+                real: FP64x64 { mag: 55340232221128654848, sign: false },
+                img: FP64x64 { mag: 92233720368547758080, sign: false }
+            }
+        );
+    data
+        .append(
+            complex64 {
+                real: FP64x64 { mag: 73786976294838206464, sign: false },
+                img: FP64x64 { mag: 18446744073709551616, sign: true }
+            }
+        );
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/reduce_l2_complex64_axis_0/output_0.cairo b/tests/nodes/reduce_l2_complex64_axis_0/output_0.cairo
new file mode 100644
index 000000000..ee6564432
--- /dev/null
+++ b/tests/nodes/reduce_l2_complex64_axis_0/output_0.cairo
@@ -0,0 +1,35 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::Complex64Tensor;
+use orion::numbers::{NumberTrait, complex64};
+use orion::numbers::{FixedTrait, FP64x64};
+
+fn output_0() -> Tensor<complex64> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data
+        .append(
+            complex64 {
+                real: FP64x64 { mag: 78262906948318920704, sign: false },
+                img: FP64x64 { mag: 0, sign: false }
+            }
+        );
+    data
+        .append(
+            complex64 {
+                real: FP64x64 { mag: 115199879809953955840, sign: false },
+                img: FP64x64 { mag: 0, sign: false }
+            }
+        );
+    data
+        .append(
+            complex64 {
+                real: FP64x64 { mag: 109132409670155108352, sign: false },
+                img: FP64x64 { mag: 0, sign: false }
+            }
+        );
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic.cairo b/tests/nodes/resize_downsample_scales_cubic.cairo
new file mode 100644
index 000000000..29d3b6281
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_downsample_scales_cubic() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::CUBIC),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic/input_0.cairo b/tests/nodes/resize_downsample_scales_cubic/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic/input_1.cairo b/tests/nodes/resize_downsample_scales_cubic/input_1.cairo
new file mode 100644
index 000000000..f861f7954
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 52428, sign: false });
+    data.append(FP16x16 { mag: 52428, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic/output_0.cairo b/tests/nodes/resize_downsample_scales_cubic/output_0.cairo
new file mode 100644
index 000000000..46d7658e5
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic/output_0.cairo
@@ -0,0 +1,24 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 96416, sign: false });
+    data.append(FP16x16 { mag: 182272, sign: false });
+    data.append(FP16x16 { mag: 267552, sign: false });
+    data.append(FP16x16 { mag: 439840, sign: false });
+    data.append(FP16x16 { mag: 525696, sign: false });
+    data.append(FP16x16 { mag: 610976, sign: false });
+    data.append(FP16x16 { mag: 780960, sign: false });
+    data.append(FP16x16 { mag: 866816, sign: false });
+    data.append(FP16x16 { mag: 952096, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside.cairo b/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside.cairo
new file mode 100644
index 000000000..785d81971
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside.cairo
@@ -0,0 +1,40 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+use orion::numbers::{FP16x16, FP16x16Impl, FP32x32, FP32x32Impl, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_downsample_scales_cubic_A_n0p5_exclude_outside() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(FixedTrait::<FP16x16>::new(32768, true)),
+            Option::Some(true),
+            Option::None,
+            Option::None,
+            Option::Some(MODE::CUBIC),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside/input_0.cairo b/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside/input_1.cairo b/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside/input_1.cairo
new file mode 100644
index 000000000..f861f7954
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 52428, sign: false });
+    data.append(FP16x16 { mag: 52428, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside/input_2.cairo b/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside/input_2.cairo
new file mode 100644
index 000000000..775bcc066
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside/input_2.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_2() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(3);
+    data.append(2);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside/output_0.cairo b/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside/output_0.cairo
new file mode 100644
index 000000000..4ce3f227c
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic_A_n0p5_exclude_outside/output_0.cairo
@@ -0,0 +1,24 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 89661, sign: false });
+    data.append(FP16x16 { mag: 174948, sign: false });
+    data.append(FP16x16 { mag: 263018, sign: false });
+    data.append(FP16x16 { mag: 430809, sign: false });
+    data.append(FP16x16 { mag: 516096, sign: false });
+    data.append(FP16x16 { mag: 604165, sign: false });
+    data.append(FP16x16 { mag: 783087, sign: false });
+    data.append(FP16x16 { mag: 868374, sign: false });
+    data.append(FP16x16 { mag: 956443, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic_align_corners.cairo b/tests/nodes/resize_downsample_scales_cubic_align_corners.cairo
new file mode 100644
index 000000000..200326825
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic_align_corners.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_downsample_scales_cubic_align_corners() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::CUBIC),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic_align_corners/input_0.cairo b/tests/nodes/resize_downsample_scales_cubic_align_corners/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic_align_corners/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic_align_corners/input_1.cairo b/tests/nodes/resize_downsample_scales_cubic_align_corners/input_1.cairo
new file mode 100644
index 000000000..f861f7954
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic_align_corners/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 52428, sign: false });
+    data.append(FP16x16 { mag: 52428, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic_align_corners/output_0.cairo b/tests/nodes/resize_downsample_scales_cubic_align_corners/output_0.cairo
new file mode 100644
index 000000000..713b36edd
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic_align_corners/output_0.cairo
@@ -0,0 +1,24 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 156971, sign: false });
+    data.append(FP16x16 { mag: 248406, sign: false });
+    data.append(FP16x16 { mag: 431277, sign: false });
+    data.append(FP16x16 { mag: 522712, sign: false });
+    data.append(FP16x16 { mag: 614147, sign: false });
+    data.append(FP16x16 { mag: 797018, sign: false });
+    data.append(FP16x16 { mag: 888453, sign: false });
+    data.append(FP16x16 { mag: 979888, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic_antialias.cairo b/tests/nodes/resize_downsample_scales_cubic_antialias.cairo
new file mode 100644
index 000000000..230171410
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic_antialias.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_downsample_scales_cubic_antialias() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::Some(1),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::CUBIC),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic_antialias/input_0.cairo b/tests/nodes/resize_downsample_scales_cubic_antialias/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic_antialias/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic_antialias/input_1.cairo b/tests/nodes/resize_downsample_scales_cubic_antialias/input_1.cairo
new file mode 100644
index 000000000..401a2622c
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic_antialias/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_cubic_antialias/output_0.cairo b/tests/nodes/resize_downsample_scales_cubic_antialias/output_0.cairo
new file mode 100644
index 000000000..d2cb459b1
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_cubic_antialias/output_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 165024, sign: false });
+    data.append(FP16x16 { mag: 280879, sign: false });
+    data.append(FP16x16 { mag: 628446, sign: false });
+    data.append(FP16x16 { mag: 744301, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_linear_align_corners.cairo b/tests/nodes/resize_downsample_scales_linear_align_corners.cairo
new file mode 100644
index 000000000..c4b4d21a0
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_linear_align_corners.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_downsample_scales_linear_align_corners() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::LINEAR),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_downsample_scales_linear_align_corners/input_0.cairo b/tests/nodes/resize_downsample_scales_linear_align_corners/input_0.cairo
new file mode 100644
index 000000000..90a00e3c1
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_linear_align_corners/input_0.cairo
@@ -0,0 +1,23 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_linear_align_corners/input_1.cairo b/tests/nodes/resize_downsample_scales_linear_align_corners/input_1.cairo
new file mode 100644
index 000000000..401a2622c
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_linear_align_corners/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_linear_align_corners/output_0.cairo b/tests/nodes/resize_downsample_scales_linear_align_corners/output_0.cairo
new file mode 100644
index 000000000..fdcbd59d8
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_linear_align_corners/output_0.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 205970, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_linear_antialias.cairo b/tests/nodes/resize_downsample_scales_linear_antialias.cairo
new file mode 100644
index 000000000..be072a81d
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_linear_antialias.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_downsample_scales_linear_antialias() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::Some(1),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::LINEAR),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_downsample_scales_linear_antialias/input_0.cairo b/tests/nodes/resize_downsample_scales_linear_antialias/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_linear_antialias/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_linear_antialias/input_1.cairo b/tests/nodes/resize_downsample_scales_linear_antialias/input_1.cairo
new file mode 100644
index 000000000..401a2622c
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_linear_antialias/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_linear_antialias/output_0.cairo b/tests/nodes/resize_downsample_scales_linear_antialias/output_0.cairo
new file mode 100644
index 000000000..a5fa2d1e6
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_linear_antialias/output_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 188416, sign: false });
+    data.append(FP16x16 { mag: 294912, sign: false });
+    data.append(FP16x16 { mag: 614400, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_linear_half_pixel_symmetric.cairo b/tests/nodes/resize_downsample_scales_linear_half_pixel_symmetric.cairo
new file mode 100644
index 000000000..3c5394cb2
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_linear_half_pixel_symmetric.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_downsample_scales_linear_half_pixel_symmetric() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(TRANSFORMATION_MODE::HALF_PIXEL_SYMMETRIC),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::LINEAR),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_downsample_scales_linear_half_pixel_symmetric/input_0.cairo b/tests/nodes/resize_downsample_scales_linear_half_pixel_symmetric/input_0.cairo
new file mode 100644
index 000000000..79947f581
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_linear_half_pixel_symmetric/input_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_linear_half_pixel_symmetric/input_1.cairo b/tests/nodes/resize_downsample_scales_linear_half_pixel_symmetric/input_1.cairo
new file mode 100644
index 000000000..4ec2dc2c5
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_linear_half_pixel_symmetric/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_linear_half_pixel_symmetric/output_0.cairo b/tests/nodes/resize_downsample_scales_linear_half_pixel_symmetric/output_0.cairo
new file mode 100644
index 000000000..865747871
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_linear_half_pixel_symmetric/output_0.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 109226, sign: false });
+    data.append(FP16x16 { mag: 218453, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_nearest.cairo b/tests/nodes/resize_downsample_scales_nearest.cairo
new file mode 100644
index 000000000..8a1a089f4
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_nearest.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_downsample_scales_nearest() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::NEAREST),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_downsample_scales_nearest/input_0.cairo b/tests/nodes/resize_downsample_scales_nearest/input_0.cairo
new file mode 100644
index 000000000..90a00e3c1
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_nearest/input_0.cairo
@@ -0,0 +1,23 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_nearest/input_1.cairo b/tests/nodes/resize_downsample_scales_nearest/input_1.cairo
new file mode 100644
index 000000000..401a2622c
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_nearest/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_scales_nearest/output_0.cairo b/tests/nodes/resize_downsample_scales_nearest/output_0.cairo
new file mode 100644
index 000000000..ba0aaa9bf
--- /dev/null
+++ b/tests/nodes/resize_downsample_scales_nearest/output_0.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_cubic.cairo b/tests/nodes/resize_downsample_sizes_cubic.cairo
new file mode 100644
index 000000000..ac560cf4c
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_cubic.cairo
@@ -0,0 +1,40 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_downsample_sizes_cubic() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::CUBIC),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_downsample_sizes_cubic/input_0.cairo b/tests/nodes/resize_downsample_sizes_cubic/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_cubic/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_cubic/input_1.cairo b/tests/nodes/resize_downsample_sizes_cubic/input_1.cairo
new file mode 100644
index 000000000..9eab8d083
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_cubic/input_1.cairo
@@ -0,0 +1,15 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
+    data.append(3);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_cubic/output_0.cairo b/tests/nodes/resize_downsample_sizes_cubic/output_0.cairo
new file mode 100644
index 000000000..14e3cf8c5
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_cubic/output_0.cairo
@@ -0,0 +1,24 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 106875, sign: false });
+    data.append(FP16x16 { mag: 196911, sign: false });
+    data.append(FP16x16 { mag: 286947, sign: false });
+    data.append(FP16x16 { mag: 467019, sign: false });
+    data.append(FP16x16 { mag: 557056, sign: false });
+    data.append(FP16x16 { mag: 647092, sign: false });
+    data.append(FP16x16 { mag: 827164, sign: false });
+    data.append(FP16x16 { mag: 917200, sign: false });
+    data.append(FP16x16 { mag: 1007236, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_cubic_antialias.cairo b/tests/nodes/resize_downsample_sizes_cubic_antialias.cairo
new file mode 100644
index 000000000..6f31c3678
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_cubic_antialias.cairo
@@ -0,0 +1,41 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_downsample_sizes_cubic_antialias() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::Some(1),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::CUBIC),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_downsample_sizes_cubic_antialias/input_0.cairo b/tests/nodes/resize_downsample_sizes_cubic_antialias/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_cubic_antialias/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_cubic_antialias/input_1.cairo b/tests/nodes/resize_downsample_sizes_cubic_antialias/input_1.cairo
new file mode 100644
index 000000000..9eab8d083
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_cubic_antialias/input_1.cairo
@@ -0,0 +1,15 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
+    data.append(3);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_cubic_antialias/output_0.cairo b/tests/nodes/resize_downsample_sizes_cubic_antialias/output_0.cairo
new file mode 100644
index 000000000..3e02030e8
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_cubic_antialias/output_0.cairo
@@ -0,0 +1,24 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 116327, sign: false });
+    data.append(FP16x16 { mag: 204472, sign: false });
+    data.append(FP16x16 { mag: 292618, sign: false });
+    data.append(FP16x16 { mag: 468910, sign: false });
+    data.append(FP16x16 { mag: 557056, sign: false });
+    data.append(FP16x16 { mag: 645201, sign: false });
+    data.append(FP16x16 { mag: 821493, sign: false });
+    data.append(FP16x16 { mag: 909639, sign: false });
+    data.append(FP16x16 { mag: 997785, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_linear_pytorch_half_pixel.cairo b/tests/nodes/resize_downsample_sizes_linear_pytorch_half_pixel.cairo
new file mode 100644
index 000000000..7064afbcf
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_linear_pytorch_half_pixel.cairo
@@ -0,0 +1,41 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_downsample_sizes_linear_pytorch_half_pixel() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::None,
+            Option::None,
+            Option::Some(TRANSFORMATION_MODE::PYTORCH_HALF_PIXEL),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::LINEAR),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_downsample_sizes_linear_pytorch_half_pixel/input_0.cairo b/tests/nodes/resize_downsample_sizes_linear_pytorch_half_pixel/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_linear_pytorch_half_pixel/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_linear_pytorch_half_pixel/input_1.cairo b/tests/nodes/resize_downsample_sizes_linear_pytorch_half_pixel/input_1.cairo
new file mode 100644
index 000000000..9513937ed
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_linear_pytorch_half_pixel/input_1.cairo
@@ -0,0 +1,15 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
+    data.append(3);
+    data.append(1);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_linear_pytorch_half_pixel/output_0.cairo b/tests/nodes/resize_downsample_sizes_linear_pytorch_half_pixel/output_0.cairo
new file mode 100644
index 000000000..6443746ef
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_linear_pytorch_half_pixel/output_0.cairo
@@ -0,0 +1,18 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(3);
+    shape.append(1);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 109226, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 808277, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_nearest.cairo b/tests/nodes/resize_downsample_sizes_nearest.cairo
new file mode 100644
index 000000000..0c512b8a4
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_nearest.cairo
@@ -0,0 +1,43 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::U32TensorPartialEq;
+
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_downsample_sizes_nearest() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::NEAREST),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_downsample_sizes_nearest/input_0.cairo b/tests/nodes/resize_downsample_sizes_nearest/input_0.cairo
new file mode 100644
index 000000000..90a00e3c1
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_nearest/input_0.cairo
@@ -0,0 +1,23 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_nearest/input_1.cairo b/tests/nodes/resize_downsample_sizes_nearest/input_1.cairo
new file mode 100644
index 000000000..ccf02e251
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_nearest/input_1.cairo
@@ -0,0 +1,15 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
+    data.append(1);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_nearest/output_0.cairo b/tests/nodes/resize_downsample_sizes_nearest/output_0.cairo
new file mode 100644
index 000000000..eecfc2472
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_nearest/output_0.cairo
@@ -0,0 +1,18 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(1);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_nearest_not_larger.cairo b/tests/nodes/resize_downsample_sizes_nearest_not_larger.cairo
new file mode 100644
index 000000000..4c097083e
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_nearest_not_larger.cairo
@@ -0,0 +1,44 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_downsample_sizes_nearest_not_larger() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let axes = Option::Some(input_2::input_2().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::None,
+            axes,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(KEEP_ASPECT_RATIO_POLICY::NOT_LARGER),
+            Option::Some(MODE::NEAREST),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_downsample_sizes_nearest_not_larger/input_0.cairo b/tests/nodes/resize_downsample_sizes_nearest_not_larger/input_0.cairo
new file mode 100644
index 000000000..90a00e3c1
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_nearest_not_larger/input_0.cairo
@@ -0,0 +1,23 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_nearest_not_larger/input_1.cairo b/tests/nodes/resize_downsample_sizes_nearest_not_larger/input_1.cairo
new file mode 100644
index 000000000..7b523e95b
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_nearest_not_larger/input_1.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_nearest_not_larger/input_2.cairo b/tests/nodes/resize_downsample_sizes_nearest_not_larger/input_2.cairo
new file mode 100644
index 000000000..6388a13b0
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_nearest_not_larger/input_2.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_2() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(2);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_nearest_not_larger/output_0.cairo b/tests/nodes/resize_downsample_sizes_nearest_not_larger/output_0.cairo
new file mode 100644
index 000000000..ba0aaa9bf
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_nearest_not_larger/output_0.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_nearest_not_smaller.cairo b/tests/nodes/resize_downsample_sizes_nearest_not_smaller.cairo
new file mode 100644
index 000000000..523472ef3
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_nearest_not_smaller.cairo
@@ -0,0 +1,43 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_downsample_sizes_nearest_not_smaller() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let axes = Option::Some(input_2::input_2().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::None,
+            axes,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(KEEP_ASPECT_RATIO_POLICY::NOT_SMALLER),
+            Option::Some(MODE::NEAREST),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_downsample_sizes_nearest_not_smaller/input_0.cairo b/tests/nodes/resize_downsample_sizes_nearest_not_smaller/input_0.cairo
new file mode 100644
index 000000000..90a00e3c1
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_nearest_not_smaller/input_0.cairo
@@ -0,0 +1,23 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_nearest_not_smaller/input_1.cairo b/tests/nodes/resize_downsample_sizes_nearest_not_smaller/input_1.cairo
new file mode 100644
index 000000000..7b523e95b
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_nearest_not_smaller/input_1.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_nearest_not_smaller/input_2.cairo b/tests/nodes/resize_downsample_sizes_nearest_not_smaller/input_2.cairo
new file mode 100644
index 000000000..6388a13b0
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_nearest_not_smaller/input_2.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_2() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(2);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_downsample_sizes_nearest_not_smaller/output_0.cairo b/tests/nodes/resize_downsample_sizes_nearest_not_smaller/output_0.cairo
new file mode 100644
index 000000000..18da97817
--- /dev/null
+++ b/tests/nodes/resize_downsample_sizes_nearest_not_smaller/output_0.cairo
@@ -0,0 +1,21 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize.cairo b/tests/nodes/resize_tf_crop_and_resize.cairo
new file mode 100644
index 000000000..7ffd9a5b0
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize.cairo
@@ -0,0 +1,43 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_tf_crop_and_resize() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let roi = Option::Some(input_2::input_2());
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            roi,
+            Option::None,
+            sizes,
+            Option::None,
+            Option::None,
+            Option::Some(TRANSFORMATION_MODE::TF_CROP_AND_RESIZE),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::LINEAR),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize/input_0.cairo b/tests/nodes/resize_tf_crop_and_resize/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize/input_1.cairo b/tests/nodes/resize_tf_crop_and_resize/input_1.cairo
new file mode 100644
index 000000000..9eab8d083
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize/input_1.cairo
@@ -0,0 +1,15 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
+    data.append(3);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize/input_2.cairo b/tests/nodes/resize_tf_crop_and_resize/input_2.cairo
new file mode 100644
index 000000000..c6d98970c
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize/input_2.cairo
@@ -0,0 +1,20 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 0, sign: false });
+    data.append(FP16x16 { mag: 0, sign: false });
+    data.append(FP16x16 { mag: 26214, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    data.append(FP16x16 { mag: 52428, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize/output_0.cairo b/tests/nodes/resize_tf_crop_and_resize/output_0.cairo
new file mode 100644
index 000000000..46668e78c
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize/output_0.cairo
@@ -0,0 +1,24 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 498073, sign: false });
+    data.append(FP16x16 { mag: 517734, sign: false });
+    data.append(FP16x16 { mag: 537395, sign: false });
+    data.append(FP16x16 { mag: 576716, sign: false });
+    data.append(FP16x16 { mag: 596377, sign: false });
+    data.append(FP16x16 { mag: 616038, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 675020, sign: false });
+    data.append(FP16x16 { mag: 694681, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_axes_2_3.cairo b/tests/nodes/resize_tf_crop_and_resize_axes_2_3.cairo
new file mode 100644
index 000000000..8d30b2993
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_axes_2_3.cairo
@@ -0,0 +1,45 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod input_3;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_tf_crop_and_resize_axes_2_3() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let roi = Option::Some(input_2::input_2());
+    let axes = Option::Some(input_3::input_3().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            roi,
+            Option::None,
+            sizes,
+            Option::None,
+            axes,
+            Option::Some(TRANSFORMATION_MODE::TF_CROP_AND_RESIZE),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::LINEAR),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_axes_2_3/input_0.cairo b/tests/nodes/resize_tf_crop_and_resize_axes_2_3/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_axes_2_3/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_axes_2_3/input_1.cairo b/tests/nodes/resize_tf_crop_and_resize_axes_2_3/input_1.cairo
new file mode 100644
index 000000000..3e04171f5
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_axes_2_3/input_1.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(3);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_axes_2_3/input_2.cairo b/tests/nodes/resize_tf_crop_and_resize_axes_2_3/input_2.cairo
new file mode 100644
index 000000000..7ec99b507
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_axes_2_3/input_2.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 26214, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    data.append(FP16x16 { mag: 52428, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_axes_2_3/input_3.cairo b/tests/nodes/resize_tf_crop_and_resize_axes_2_3/input_3.cairo
new file mode 100644
index 000000000..6fc07eeff
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_axes_2_3/input_3.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_3() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(2);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_axes_2_3/output_0.cairo b/tests/nodes/resize_tf_crop_and_resize_axes_2_3/output_0.cairo
new file mode 100644
index 000000000..46668e78c
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_axes_2_3/output_0.cairo
@@ -0,0 +1,24 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 498073, sign: false });
+    data.append(FP16x16 { mag: 517734, sign: false });
+    data.append(FP16x16 { mag: 537395, sign: false });
+    data.append(FP16x16 { mag: 576716, sign: false });
+    data.append(FP16x16 { mag: 596377, sign: false });
+    data.append(FP16x16 { mag: 616038, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 675020, sign: false });
+    data.append(FP16x16 { mag: 694681, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_axes_3_2.cairo b/tests/nodes/resize_tf_crop_and_resize_axes_3_2.cairo
new file mode 100644
index 000000000..f0e59f4e5
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_axes_3_2.cairo
@@ -0,0 +1,45 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod input_3;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_tf_crop_and_resize_axes_3_2() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let roi = Option::Some(input_2::input_2());
+    let axes = Option::Some(input_3::input_3().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            roi,
+            Option::None,
+            sizes,
+            Option::None,
+            axes,
+            Option::Some(TRANSFORMATION_MODE::TF_CROP_AND_RESIZE),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::LINEAR),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_axes_3_2/input_0.cairo b/tests/nodes/resize_tf_crop_and_resize_axes_3_2/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_axes_3_2/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_axes_3_2/input_1.cairo b/tests/nodes/resize_tf_crop_and_resize_axes_3_2/input_1.cairo
new file mode 100644
index 000000000..3e04171f5
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_axes_3_2/input_1.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(3);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_axes_3_2/input_2.cairo b/tests/nodes/resize_tf_crop_and_resize_axes_3_2/input_2.cairo
new file mode 100644
index 000000000..4369ef099
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_axes_3_2/input_2.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 39321, sign: false });
+    data.append(FP16x16 { mag: 26214, sign: false });
+    data.append(FP16x16 { mag: 52428, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_axes_3_2/input_3.cairo b/tests/nodes/resize_tf_crop_and_resize_axes_3_2/input_3.cairo
new file mode 100644
index 000000000..804c8f5c6
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_axes_3_2/input_3.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_3() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(3);
+    data.append(2);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_axes_3_2/output_0.cairo b/tests/nodes/resize_tf_crop_and_resize_axes_3_2/output_0.cairo
new file mode 100644
index 000000000..46668e78c
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_axes_3_2/output_0.cairo
@@ -0,0 +1,24 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 498073, sign: false });
+    data.append(FP16x16 { mag: 517734, sign: false });
+    data.append(FP16x16 { mag: 537395, sign: false });
+    data.append(FP16x16 { mag: 576716, sign: false });
+    data.append(FP16x16 { mag: 596377, sign: false });
+    data.append(FP16x16 { mag: 616038, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 675020, sign: false });
+    data.append(FP16x16 { mag: 694681, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_extrapolation_value.cairo b/tests/nodes/resize_tf_crop_and_resize_extrapolation_value.cairo
new file mode 100644
index 000000000..b3fb2e6d0
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_extrapolation_value.cairo
@@ -0,0 +1,44 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+use orion::numbers::{FP16x16, FP16x16Impl, FP32x32, FP32x32Impl, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_tf_crop_and_resize_extrapolation_value() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let roi = Option::Some(input_2::input_2());
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            roi,
+            Option::None,
+            sizes,
+            Option::None,
+            Option::None,
+            Option::Some(TRANSFORMATION_MODE::TF_CROP_AND_RESIZE),
+            Option::None,
+            Option::None,
+            Option::Some(FixedTrait::<FP16x16>::new(655360, false)),
+            Option::None,
+            Option::Some(MODE::LINEAR),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_extrapolation_value/input_0.cairo b/tests/nodes/resize_tf_crop_and_resize_extrapolation_value/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_extrapolation_value/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_extrapolation_value/input_1.cairo b/tests/nodes/resize_tf_crop_and_resize_extrapolation_value/input_1.cairo
new file mode 100644
index 000000000..9eab8d083
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_extrapolation_value/input_1.cairo
@@ -0,0 +1,15 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
+    data.append(3);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_extrapolation_value/input_2.cairo b/tests/nodes/resize_tf_crop_and_resize_extrapolation_value/input_2.cairo
new file mode 100644
index 000000000..26627c0b1
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_extrapolation_value/input_2.cairo
@@ -0,0 +1,20 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_2() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 0, sign: false });
+    data.append(FP16x16 { mag: 0, sign: false });
+    data.append(FP16x16 { mag: 26214, sign: false });
+    data.append(FP16x16 { mag: 39321, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 78643, sign: false });
+    data.append(FP16x16 { mag: 111411, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_tf_crop_and_resize_extrapolation_value/output_0.cairo b/tests/nodes/resize_tf_crop_and_resize_extrapolation_value/output_0.cairo
new file mode 100644
index 000000000..80acb5c8f
--- /dev/null
+++ b/tests/nodes/resize_tf_crop_and_resize_extrapolation_value/output_0.cairo
@@ -0,0 +1,24 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(3);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 498073, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 812646, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic.cairo b/tests/nodes/resize_upsample_scales_cubic.cairo
new file mode 100644
index 000000000..6d7b1da71
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_scales_cubic() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::CUBIC),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic/input_0.cairo b/tests/nodes/resize_upsample_scales_cubic/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic/input_1.cairo b/tests/nodes/resize_upsample_scales_cubic/input_1.cairo
new file mode 100644
index 000000000..75bd50d8a
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic/output_0.cairo b/tests/nodes/resize_upsample_scales_cubic/output_0.cairo
new file mode 100644
index 000000000..8d8b8d5b4
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic/output_0.cairo
@@ -0,0 +1,79 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(8);
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 30976, sign: false });
+    data.append(FP16x16 { mag: 50432, sign: false });
+    data.append(FP16x16 { mag: 81664, sign: false });
+    data.append(FP16x16 { mag: 122880, sign: false });
+    data.append(FP16x16 { mag: 149504, sign: false });
+    data.append(FP16x16 { mag: 190720, sign: false });
+    data.append(FP16x16 { mag: 221952, sign: false });
+    data.append(FP16x16 { mag: 241408, sign: false });
+    data.append(FP16x16 { mag: 108800, sign: false });
+    data.append(FP16x16 { mag: 128256, sign: false });
+    data.append(FP16x16 { mag: 159488, sign: false });
+    data.append(FP16x16 { mag: 200704, sign: false });
+    data.append(FP16x16 { mag: 227328, sign: false });
+    data.append(FP16x16 { mag: 268544, sign: false });
+    data.append(FP16x16 { mag: 299776, sign: false });
+    data.append(FP16x16 { mag: 319232, sign: false });
+    data.append(FP16x16 { mag: 233728, sign: false });
+    data.append(FP16x16 { mag: 253184, sign: false });
+    data.append(FP16x16 { mag: 284416, sign: false });
+    data.append(FP16x16 { mag: 325632, sign: false });
+    data.append(FP16x16 { mag: 352256, sign: false });
+    data.append(FP16x16 { mag: 393472, sign: false });
+    data.append(FP16x16 { mag: 424704, sign: false });
+    data.append(FP16x16 { mag: 444160, sign: false });
+    data.append(FP16x16 { mag: 398592, sign: false });
+    data.append(FP16x16 { mag: 418048, sign: false });
+    data.append(FP16x16 { mag: 449280, sign: false });
+    data.append(FP16x16 { mag: 490496, sign: false });
+    data.append(FP16x16 { mag: 517120, sign: false });
+    data.append(FP16x16 { mag: 558336, sign: false });
+    data.append(FP16x16 { mag: 589568, sign: false });
+    data.append(FP16x16 { mag: 609024, sign: false });
+    data.append(FP16x16 { mag: 505088, sign: false });
+    data.append(FP16x16 { mag: 524544, sign: false });
+    data.append(FP16x16 { mag: 555776, sign: false });
+    data.append(FP16x16 { mag: 596992, sign: false });
+    data.append(FP16x16 { mag: 623616, sign: false });
+    data.append(FP16x16 { mag: 664832, sign: false });
+    data.append(FP16x16 { mag: 696064, sign: false });
+    data.append(FP16x16 { mag: 715520, sign: false });
+    data.append(FP16x16 { mag: 669952, sign: false });
+    data.append(FP16x16 { mag: 689408, sign: false });
+    data.append(FP16x16 { mag: 720640, sign: false });
+    data.append(FP16x16 { mag: 761856, sign: false });
+    data.append(FP16x16 { mag: 788480, sign: false });
+    data.append(FP16x16 { mag: 829696, sign: false });
+    data.append(FP16x16 { mag: 860928, sign: false });
+    data.append(FP16x16 { mag: 880384, sign: false });
+    data.append(FP16x16 { mag: 794880, sign: false });
+    data.append(FP16x16 { mag: 814336, sign: false });
+    data.append(FP16x16 { mag: 845568, sign: false });
+    data.append(FP16x16 { mag: 886784, sign: false });
+    data.append(FP16x16 { mag: 913408, sign: false });
+    data.append(FP16x16 { mag: 954624, sign: false });
+    data.append(FP16x16 { mag: 985856, sign: false });
+    data.append(FP16x16 { mag: 1005312, sign: false });
+    data.append(FP16x16 { mag: 872704, sign: false });
+    data.append(FP16x16 { mag: 892160, sign: false });
+    data.append(FP16x16 { mag: 923392, sign: false });
+    data.append(FP16x16 { mag: 964608, sign: false });
+    data.append(FP16x16 { mag: 991232, sign: false });
+    data.append(FP16x16 { mag: 1032448, sign: false });
+    data.append(FP16x16 { mag: 1063680, sign: false });
+    data.append(FP16x16 { mag: 1083136, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic_A_n0p5_exclude_outside.cairo b/tests/nodes/resize_upsample_scales_cubic_A_n0p5_exclude_outside.cairo
new file mode 100644
index 000000000..1a7e89dd4
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic_A_n0p5_exclude_outside.cairo
@@ -0,0 +1,40 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+use orion::numbers::{FP16x16, FP16x16Impl, FP32x32, FP32x32Impl, FixedTrait};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_scales_cubic_A_n0p5_exclude_outside() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(FixedTrait::<FP16x16>::new(32768, true)),
+            Option::Some(true),
+            Option::None,
+            Option::None,
+            Option::Some(MODE::CUBIC),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic_A_n0p5_exclude_outside/input_0.cairo b/tests/nodes/resize_upsample_scales_cubic_A_n0p5_exclude_outside/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic_A_n0p5_exclude_outside/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic_A_n0p5_exclude_outside/input_1.cairo b/tests/nodes/resize_upsample_scales_cubic_A_n0p5_exclude_outside/input_1.cairo
new file mode 100644
index 000000000..75bd50d8a
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic_A_n0p5_exclude_outside/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic_A_n0p5_exclude_outside/output_0.cairo b/tests/nodes/resize_upsample_scales_cubic_A_n0p5_exclude_outside/output_0.cairo
new file mode 100644
index 000000000..70c95fe02
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic_A_n0p5_exclude_outside/output_0.cairo
@@ -0,0 +1,79 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(8);
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 36623, sign: false });
+    data.append(FP16x16 { mag: 53408, sign: false });
+    data.append(FP16x16 { mag: 88931, sign: false });
+    data.append(FP16x16 { mag: 124325, sign: false });
+    data.append(FP16x16 { mag: 157093, sign: false });
+    data.append(FP16x16 { mag: 192488, sign: false });
+    data.append(FP16x16 { mag: 228011, sign: false });
+    data.append(FP16x16 { mag: 244796, sign: false });
+    data.append(FP16x16 { mag: 103762, sign: false });
+    data.append(FP16x16 { mag: 120547, sign: false });
+    data.append(FP16x16 { mag: 156071, sign: false });
+    data.append(FP16x16 { mag: 191465, sign: false });
+    data.append(FP16x16 { mag: 224233, sign: false });
+    data.append(FP16x16 { mag: 259628, sign: false });
+    data.append(FP16x16 { mag: 295151, sign: false });
+    data.append(FP16x16 { mag: 311936, sign: false });
+    data.append(FP16x16 { mag: 245855, sign: false });
+    data.append(FP16x16 { mag: 262640, sign: false });
+    data.append(FP16x16 { mag: 298163, sign: false });
+    data.append(FP16x16 { mag: 333558, sign: false });
+    data.append(FP16x16 { mag: 366326, sign: false });
+    data.append(FP16x16 { mag: 401720, sign: false });
+    data.append(FP16x16 { mag: 437243, sign: false });
+    data.append(FP16x16 { mag: 454028, sign: false });
+    data.append(FP16x16 { mag: 387433, sign: false });
+    data.append(FP16x16 { mag: 404218, sign: false });
+    data.append(FP16x16 { mag: 439741, sign: false });
+    data.append(FP16x16 { mag: 475136, sign: false });
+    data.append(FP16x16 { mag: 507904, sign: false });
+    data.append(FP16x16 { mag: 543298, sign: false });
+    data.append(FP16x16 { mag: 578821, sign: false });
+    data.append(FP16x16 { mag: 595606, sign: false });
+    data.append(FP16x16 { mag: 518505, sign: false });
+    data.append(FP16x16 { mag: 535290, sign: false });
+    data.append(FP16x16 { mag: 570813, sign: false });
+    data.append(FP16x16 { mag: 606208, sign: false });
+    data.append(FP16x16 { mag: 638976, sign: false });
+    data.append(FP16x16 { mag: 674370, sign: false });
+    data.append(FP16x16 { mag: 709893, sign: false });
+    data.append(FP16x16 { mag: 726678, sign: false });
+    data.append(FP16x16 { mag: 660083, sign: false });
+    data.append(FP16x16 { mag: 676868, sign: false });
+    data.append(FP16x16 { mag: 712391, sign: false });
+    data.append(FP16x16 { mag: 747785, sign: false });
+    data.append(FP16x16 { mag: 780553, sign: false });
+    data.append(FP16x16 { mag: 815948, sign: false });
+    data.append(FP16x16 { mag: 851471, sign: false });
+    data.append(FP16x16 { mag: 868256, sign: false });
+    data.append(FP16x16 { mag: 802175, sign: false });
+    data.append(FP16x16 { mag: 818960, sign: false });
+    data.append(FP16x16 { mag: 854484, sign: false });
+    data.append(FP16x16 { mag: 889878, sign: false });
+    data.append(FP16x16 { mag: 922646, sign: false });
+    data.append(FP16x16 { mag: 958040, sign: false });
+    data.append(FP16x16 { mag: 993564, sign: false });
+    data.append(FP16x16 { mag: 1010349, sign: false });
+    data.append(FP16x16 { mag: 869315, sign: false });
+    data.append(FP16x16 { mag: 886100, sign: false });
+    data.append(FP16x16 { mag: 921623, sign: false });
+    data.append(FP16x16 { mag: 957018, sign: false });
+    data.append(FP16x16 { mag: 989786, sign: false });
+    data.append(FP16x16 { mag: 1025180, sign: false });
+    data.append(FP16x16 { mag: 1060704, sign: false });
+    data.append(FP16x16 { mag: 1077489, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic_align_corners.cairo b/tests/nodes/resize_upsample_scales_cubic_align_corners.cairo
new file mode 100644
index 000000000..77c9befd1
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic_align_corners.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_scales_cubic_align_corners() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::CUBIC),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic_align_corners/input_0.cairo b/tests/nodes/resize_upsample_scales_cubic_align_corners/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic_align_corners/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic_align_corners/input_1.cairo b/tests/nodes/resize_upsample_scales_cubic_align_corners/input_1.cairo
new file mode 100644
index 000000000..75bd50d8a
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic_align_corners/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic_align_corners/output_0.cairo b/tests/nodes/resize_upsample_scales_cubic_align_corners/output_0.cairo
new file mode 100644
index 000000000..87f61ce58
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic_align_corners/output_0.cairo
@@ -0,0 +1,79 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(8);
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 87890, sign: false });
+    data.append(FP16x16 { mag: 117983, sign: false });
+    data.append(FP16x16 { mag: 152662, sign: false });
+    data.append(FP16x16 { mag: 175017, sign: false });
+    data.append(FP16x16 { mag: 209696, sign: false });
+    data.append(FP16x16 { mag: 239789, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 154955, sign: false });
+    data.append(FP16x16 { mag: 177310, sign: false });
+    data.append(FP16x16 { mag: 207403, sign: false });
+    data.append(FP16x16 { mag: 242081, sign: false });
+    data.append(FP16x16 { mag: 264436, sign: false });
+    data.append(FP16x16 { mag: 299115, sign: false });
+    data.append(FP16x16 { mag: 329208, sign: false });
+    data.append(FP16x16 { mag: 351563, sign: false });
+    data.append(FP16x16 { mag: 275327, sign: false });
+    data.append(FP16x16 { mag: 297682, sign: false });
+    data.append(FP16x16 { mag: 327775, sign: false });
+    data.append(FP16x16 { mag: 362454, sign: false });
+    data.append(FP16x16 { mag: 384809, sign: false });
+    data.append(FP16x16 { mag: 419487, sign: false });
+    data.append(FP16x16 { mag: 449580, sign: false });
+    data.append(FP16x16 { mag: 471935, sign: false });
+    data.append(FP16x16 { mag: 414042, sign: false });
+    data.append(FP16x16 { mag: 436397, sign: false });
+    data.append(FP16x16 { mag: 466490, sign: false });
+    data.append(FP16x16 { mag: 501168, sign: false });
+    data.append(FP16x16 { mag: 523523, sign: false });
+    data.append(FP16x16 { mag: 558202, sign: false });
+    data.append(FP16x16 { mag: 588295, sign: false });
+    data.append(FP16x16 { mag: 610650, sign: false });
+    data.append(FP16x16 { mag: 503461, sign: false });
+    data.append(FP16x16 { mag: 525816, sign: false });
+    data.append(FP16x16 { mag: 555909, sign: false });
+    data.append(FP16x16 { mag: 590588, sign: false });
+    data.append(FP16x16 { mag: 612943, sign: false });
+    data.append(FP16x16 { mag: 647621, sign: false });
+    data.append(FP16x16 { mag: 677714, sign: false });
+    data.append(FP16x16 { mag: 700069, sign: false });
+    data.append(FP16x16 { mag: 642176, sign: false });
+    data.append(FP16x16 { mag: 664531, sign: false });
+    data.append(FP16x16 { mag: 694624, sign: false });
+    data.append(FP16x16 { mag: 729302, sign: false });
+    data.append(FP16x16 { mag: 751657, sign: false });
+    data.append(FP16x16 { mag: 786336, sign: false });
+    data.append(FP16x16 { mag: 816429, sign: false });
+    data.append(FP16x16 { mag: 838784, sign: false });
+    data.append(FP16x16 { mag: 762548, sign: false });
+    data.append(FP16x16 { mag: 784903, sign: false });
+    data.append(FP16x16 { mag: 814996, sign: false });
+    data.append(FP16x16 { mag: 849675, sign: false });
+    data.append(FP16x16 { mag: 872030, sign: false });
+    data.append(FP16x16 { mag: 906708, sign: false });
+    data.append(FP16x16 { mag: 936801, sign: false });
+    data.append(FP16x16 { mag: 959156, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 874322, sign: false });
+    data.append(FP16x16 { mag: 904415, sign: false });
+    data.append(FP16x16 { mag: 939094, sign: false });
+    data.append(FP16x16 { mag: 961449, sign: false });
+    data.append(FP16x16 { mag: 996128, sign: false });
+    data.append(FP16x16 { mag: 1026221, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic_asymmetric.cairo b/tests/nodes/resize_upsample_scales_cubic_asymmetric.cairo
new file mode 100644
index 000000000..3b07d61e8
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic_asymmetric.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_scales_cubic_asymmetric() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(TRANSFORMATION_MODE::ASYMMETRIC),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::CUBIC),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic_asymmetric/input_0.cairo b/tests/nodes/resize_upsample_scales_cubic_asymmetric/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic_asymmetric/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic_asymmetric/input_1.cairo b/tests/nodes/resize_upsample_scales_cubic_asymmetric/input_1.cairo
new file mode 100644
index 000000000..75bd50d8a
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic_asymmetric/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_cubic_asymmetric/output_0.cairo b/tests/nodes/resize_upsample_scales_cubic_asymmetric/output_0.cairo
new file mode 100644
index 000000000..61321f6df
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_cubic_asymmetric/output_0.cairo
@@ -0,0 +1,79 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(8);
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 92160, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 163840, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 235520, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 268288, sign: false });
+    data.append(FP16x16 { mag: 172032, sign: false });
+    data.append(FP16x16 { mag: 198656, sign: false });
+    data.append(FP16x16 { mag: 237568, sign: false });
+    data.append(FP16x16 { mag: 270336, sign: false });
+    data.append(FP16x16 { mag: 303104, sign: false });
+    data.append(FP16x16 { mag: 342016, sign: false });
+    data.append(FP16x16 { mag: 368640, sign: false });
+    data.append(FP16x16 { mag: 374784, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 354304, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 425984, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 497664, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 530432, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 485376, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 557056, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 628736, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 661504, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 616448, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 688128, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 759808, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 792576, sign: false });
+    data.append(FP16x16 { mag: 745472, sign: false });
+    data.append(FP16x16 { mag: 772096, sign: false });
+    data.append(FP16x16 { mag: 811008, sign: false });
+    data.append(FP16x16 { mag: 843776, sign: false });
+    data.append(FP16x16 { mag: 876544, sign: false });
+    data.append(FP16x16 { mag: 915456, sign: false });
+    data.append(FP16x16 { mag: 942080, sign: false });
+    data.append(FP16x16 { mag: 948224, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 878592, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 950272, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1021952, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1054720, sign: false });
+    data.append(FP16x16 { mag: 876544, sign: false });
+    data.append(FP16x16 { mag: 903168, sign: false });
+    data.append(FP16x16 { mag: 942080, sign: false });
+    data.append(FP16x16 { mag: 974848, sign: false });
+    data.append(FP16x16 { mag: 1007616, sign: false });
+    data.append(FP16x16 { mag: 1046528, sign: false });
+    data.append(FP16x16 { mag: 1073152, sign: false });
+    data.append(FP16x16 { mag: 1079296, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_linear.cairo b/tests/nodes/resize_upsample_scales_linear.cairo
new file mode 100644
index 000000000..650cbe7d1
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_linear.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_scales_linear() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::LINEAR),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_scales_linear/input_0.cairo b/tests/nodes/resize_upsample_scales_linear/input_0.cairo
new file mode 100644
index 000000000..84a850bc4
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_linear/input_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_linear/input_1.cairo b/tests/nodes/resize_upsample_scales_linear/input_1.cairo
new file mode 100644
index 000000000..75bd50d8a
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_linear/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_linear/output_0.cairo b/tests/nodes/resize_upsample_scales_linear/output_0.cairo
new file mode 100644
index 000000000..67f6375ca
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_linear/output_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 81920, sign: false });
+    data.append(FP16x16 { mag: 114688, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 98304, sign: false });
+    data.append(FP16x16 { mag: 114688, sign: false });
+    data.append(FP16x16 { mag: 147456, sign: false });
+    data.append(FP16x16 { mag: 163840, sign: false });
+    data.append(FP16x16 { mag: 163840, sign: false });
+    data.append(FP16x16 { mag: 180224, sign: false });
+    data.append(FP16x16 { mag: 212992, sign: false });
+    data.append(FP16x16 { mag: 229376, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 212992, sign: false });
+    data.append(FP16x16 { mag: 245760, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_linear_align_corners.cairo b/tests/nodes/resize_upsample_scales_linear_align_corners.cairo
new file mode 100644
index 000000000..d58d1e15c
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_linear_align_corners.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_scales_linear_align_corners() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::LINEAR),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_scales_linear_align_corners/input_0.cairo b/tests/nodes/resize_upsample_scales_linear_align_corners/input_0.cairo
new file mode 100644
index 000000000..84a850bc4
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_linear_align_corners/input_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_linear_align_corners/input_1.cairo b/tests/nodes/resize_upsample_scales_linear_align_corners/input_1.cairo
new file mode 100644
index 000000000..75bd50d8a
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_linear_align_corners/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_linear_align_corners/output_0.cairo b/tests/nodes/resize_upsample_scales_linear_align_corners/output_0.cairo
new file mode 100644
index 000000000..9a136353c
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_linear_align_corners/output_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 87381, sign: false });
+    data.append(FP16x16 { mag: 109226, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 109226, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 152917, sign: false });
+    data.append(FP16x16 { mag: 174762, sign: false });
+    data.append(FP16x16 { mag: 152917, sign: false });
+    data.append(FP16x16 { mag: 174762, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 218453, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 218453, sign: false });
+    data.append(FP16x16 { mag: 240298, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_linear_half_pixel_symmetric.cairo b/tests/nodes/resize_upsample_scales_linear_half_pixel_symmetric.cairo
new file mode 100644
index 000000000..aa07b6dd3
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_linear_half_pixel_symmetric.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::operators::tensor::FP16x16Tensor;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_scales_linear_half_pixel_symmetric() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(TRANSFORMATION_MODE::HALF_PIXEL_SYMMETRIC),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::LINEAR),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_scales_linear_half_pixel_symmetric/input_0.cairo b/tests/nodes/resize_upsample_scales_linear_half_pixel_symmetric/input_0.cairo
new file mode 100644
index 000000000..84a850bc4
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_linear_half_pixel_symmetric/input_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_linear_half_pixel_symmetric/input_1.cairo b/tests/nodes/resize_upsample_scales_linear_half_pixel_symmetric/input_1.cairo
new file mode 100644
index 000000000..ef375c64b
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_linear_half_pixel_symmetric/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 150732, sign: false });
+    data.append(FP16x16 { mag: 192675, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_linear_half_pixel_symmetric/output_0.cairo b/tests/nodes/resize_upsample_scales_linear_half_pixel_symmetric/output_0.cairo
new file mode 100644
index 000000000..4ab735561
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_linear_half_pixel_symmetric/output_0.cairo
@@ -0,0 +1,35 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(5);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 76012, sign: false });
+    data.append(FP16x16 { mag: 98304, sign: false });
+    data.append(FP16x16 { mag: 120595, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 102578, sign: false });
+    data.append(FP16x16 { mag: 113054, sign: false });
+    data.append(FP16x16 { mag: 135346, sign: false });
+    data.append(FP16x16 { mag: 157637, sign: false });
+    data.append(FP16x16 { mag: 168114, sign: false });
+    data.append(FP16x16 { mag: 159565, sign: false });
+    data.append(FP16x16 { mag: 170042, sign: false });
+    data.append(FP16x16 { mag: 192333, sign: false });
+    data.append(FP16x16 { mag: 214625, sign: false });
+    data.append(FP16x16 { mag: 225101, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 207084, sign: false });
+    data.append(FP16x16 { mag: 229376, sign: false });
+    data.append(FP16x16 { mag: 251667, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_nearest.cairo b/tests/nodes/resize_upsample_scales_nearest.cairo
new file mode 100644
index 000000000..f4a6c422f
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_nearest.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_scales_nearest() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(TRANSFORMATION_MODE::HALF_PIXEL_SYMMETRIC),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::NEAREST),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_scales_nearest/input_0.cairo b/tests/nodes/resize_upsample_scales_nearest/input_0.cairo
new file mode 100644
index 000000000..84a850bc4
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_nearest/input_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_nearest/input_1.cairo b/tests/nodes/resize_upsample_scales_nearest/input_1.cairo
new file mode 100644
index 000000000..2f880ced5
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_nearest/input_1.cairo
@@ -0,0 +1,16 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_nearest/output_0.cairo b/tests/nodes/resize_upsample_scales_nearest/output_0.cairo
new file mode 100644
index 000000000..61e0d0daf
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_nearest/output_0.cairo
@@ -0,0 +1,39 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(6);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_nearest_axes_2_3.cairo b/tests/nodes/resize_upsample_scales_nearest_axes_2_3.cairo
new file mode 100644
index 000000000..b492b7128
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_nearest_axes_2_3.cairo
@@ -0,0 +1,41 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_scales_nearest_axes_2_3() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let axes = Option::Some(input_2::input_2().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            axes,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::NEAREST),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_scales_nearest_axes_2_3/input_0.cairo b/tests/nodes/resize_upsample_scales_nearest_axes_2_3/input_0.cairo
new file mode 100644
index 000000000..84a850bc4
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_nearest_axes_2_3/input_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_nearest_axes_2_3/input_1.cairo b/tests/nodes/resize_upsample_scales_nearest_axes_2_3/input_1.cairo
new file mode 100644
index 000000000..01532dbdb
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_nearest_axes_2_3/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_nearest_axes_2_3/input_2.cairo b/tests/nodes/resize_upsample_scales_nearest_axes_2_3/input_2.cairo
new file mode 100644
index 000000000..6388a13b0
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_nearest_axes_2_3/input_2.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_2() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(2);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_nearest_axes_2_3/output_0.cairo b/tests/nodes/resize_upsample_scales_nearest_axes_2_3/output_0.cairo
new file mode 100644
index 000000000..61e0d0daf
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_nearest_axes_2_3/output_0.cairo
@@ -0,0 +1,39 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(6);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_nearest_axes_3_2.cairo b/tests/nodes/resize_upsample_scales_nearest_axes_3_2.cairo
new file mode 100644
index 000000000..51b857b25
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_nearest_axes_3_2.cairo
@@ -0,0 +1,41 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_scales_nearest_axes_3_2() {
+    let data = input_0::input_0();
+    let mut scales = Option::Some(input_1::input_1().data);
+    let axes = Option::Some(input_2::input_2().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            scales,
+            Option::None,
+            Option::None,
+            axes,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::NEAREST),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_scales_nearest_axes_3_2/input_0.cairo b/tests/nodes/resize_upsample_scales_nearest_axes_3_2/input_0.cairo
new file mode 100644
index 000000000..84a850bc4
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_nearest_axes_3_2/input_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_nearest_axes_3_2/input_1.cairo b/tests/nodes/resize_upsample_scales_nearest_axes_3_2/input_1.cairo
new file mode 100644
index 000000000..f6a98ccbd
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_nearest_axes_3_2/input_1.cairo
@@ -0,0 +1,14 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_1() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_nearest_axes_3_2/input_2.cairo b/tests/nodes/resize_upsample_scales_nearest_axes_3_2/input_2.cairo
new file mode 100644
index 000000000..775bcc066
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_nearest_axes_3_2/input_2.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_2() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(3);
+    data.append(2);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_scales_nearest_axes_3_2/output_0.cairo b/tests/nodes/resize_upsample_scales_nearest_axes_3_2/output_0.cairo
new file mode 100644
index 000000000..61e0d0daf
--- /dev/null
+++ b/tests/nodes/resize_upsample_scales_nearest_axes_3_2/output_0.cairo
@@ -0,0 +1,39 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(6);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_cubic.cairo b/tests/nodes/resize_upsample_sizes_cubic.cairo
new file mode 100644
index 000000000..cfeee00ba
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_cubic.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_sizes_cubic() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::CUBIC),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_sizes_cubic/input_0.cairo b/tests/nodes/resize_upsample_sizes_cubic/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_cubic/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_cubic/input_1.cairo b/tests/nodes/resize_upsample_sizes_cubic/input_1.cairo
new file mode 100644
index 000000000..ac41c83c4
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_cubic/input_1.cairo
@@ -0,0 +1,15 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
+    data.append(9);
+    data.append(10);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_cubic/output_0.cairo b/tests/nodes/resize_upsample_sizes_cubic/output_0.cairo
new file mode 100644
index 000000000..7363ef635
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_cubic/output_0.cairo
@@ -0,0 +1,105 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(9);
+    shape.append(10);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 29824, sign: false });
+    data.append(FP16x16 { mag: 41981, sign: false });
+    data.append(FP16x16 { mag: 63673, sign: false });
+    data.append(FP16x16 { mag: 93230, sign: false });
+    data.append(FP16x16 { mag: 124998, sign: false });
+    data.append(FP16x16 { mag: 145708, sign: false });
+    data.append(FP16x16 { mag: 177476, sign: false });
+    data.append(FP16x16 { mag: 207033, sign: false });
+    data.append(FP16x16 { mag: 228725, sign: false });
+    data.append(FP16x16 { mag: 240882, sign: false });
+    data.append(FP16x16 { mag: 91382, sign: false });
+    data.append(FP16x16 { mag: 103538, sign: false });
+    data.append(FP16x16 { mag: 125231, sign: false });
+    data.append(FP16x16 { mag: 154788, sign: false });
+    data.append(FP16x16 { mag: 186556, sign: false });
+    data.append(FP16x16 { mag: 207266, sign: false });
+    data.append(FP16x16 { mag: 239034, sign: false });
+    data.append(FP16x16 { mag: 268591, sign: false });
+    data.append(FP16x16 { mag: 290283, sign: false });
+    data.append(FP16x16 { mag: 302440, sign: false });
+    data.append(FP16x16 { mag: 193416, sign: false });
+    data.append(FP16x16 { mag: 205573, sign: false });
+    data.append(FP16x16 { mag: 227266, sign: false });
+    data.append(FP16x16 { mag: 256822, sign: false });
+    data.append(FP16x16 { mag: 288591, sign: false });
+    data.append(FP16x16 { mag: 309300, sign: false });
+    data.append(FP16x16 { mag: 341069, sign: false });
+    data.append(FP16x16 { mag: 370626, sign: false });
+    data.append(FP16x16 { mag: 392318, sign: false });
+    data.append(FP16x16 { mag: 404475, sign: false });
+    data.append(FP16x16 { mag: 341131, sign: false });
+    data.append(FP16x16 { mag: 353288, sign: false });
+    data.append(FP16x16 { mag: 374980, sign: false });
+    data.append(FP16x16 { mag: 404537, sign: false });
+    data.append(FP16x16 { mag: 436305, sign: false });
+    data.append(FP16x16 { mag: 457015, sign: false });
+    data.append(FP16x16 { mag: 488783, sign: false });
+    data.append(FP16x16 { mag: 518340, sign: false });
+    data.append(FP16x16 { mag: 540033, sign: false });
+    data.append(FP16x16 { mag: 552190, sign: false });
+    data.append(FP16x16 { mag: 451526, sign: false });
+    data.append(FP16x16 { mag: 463683, sign: false });
+    data.append(FP16x16 { mag: 485376, sign: false });
+    data.append(FP16x16 { mag: 514932, sign: false });
+    data.append(FP16x16 { mag: 546701, sign: false });
+    data.append(FP16x16 { mag: 567410, sign: false });
+    data.append(FP16x16 { mag: 599179, sign: false });
+    data.append(FP16x16 { mag: 628736, sign: false });
+    data.append(FP16x16 { mag: 650428, sign: false });
+    data.append(FP16x16 { mag: 662585, sign: false });
+    data.append(FP16x16 { mag: 561922, sign: false });
+    data.append(FP16x16 { mag: 574078, sign: false });
+    data.append(FP16x16 { mag: 595771, sign: false });
+    data.append(FP16x16 { mag: 625328, sign: false });
+    data.append(FP16x16 { mag: 657096, sign: false });
+    data.append(FP16x16 { mag: 677806, sign: false });
+    data.append(FP16x16 { mag: 709574, sign: false });
+    data.append(FP16x16 { mag: 739131, sign: false });
+    data.append(FP16x16 { mag: 760823, sign: false });
+    data.append(FP16x16 { mag: 772980, sign: false });
+    data.append(FP16x16 { mag: 709636, sign: false });
+    data.append(FP16x16 { mag: 721793, sign: false });
+    data.append(FP16x16 { mag: 743485, sign: false });
+    data.append(FP16x16 { mag: 773042, sign: false });
+    data.append(FP16x16 { mag: 804811, sign: false });
+    data.append(FP16x16 { mag: 825520, sign: false });
+    data.append(FP16x16 { mag: 857289, sign: false });
+    data.append(FP16x16 { mag: 886845, sign: false });
+    data.append(FP16x16 { mag: 908538, sign: false });
+    data.append(FP16x16 { mag: 920695, sign: false });
+    data.append(FP16x16 { mag: 811671, sign: false });
+    data.append(FP16x16 { mag: 823828, sign: false });
+    data.append(FP16x16 { mag: 845520, sign: false });
+    data.append(FP16x16 { mag: 875077, sign: false });
+    data.append(FP16x16 { mag: 906845, sign: false });
+    data.append(FP16x16 { mag: 927555, sign: false });
+    data.append(FP16x16 { mag: 959323, sign: false });
+    data.append(FP16x16 { mag: 988880, sign: false });
+    data.append(FP16x16 { mag: 1010573, sign: false });
+    data.append(FP16x16 { mag: 1022729, sign: false });
+    data.append(FP16x16 { mag: 873229, sign: false });
+    data.append(FP16x16 { mag: 885386, sign: false });
+    data.append(FP16x16 { mag: 907078, sign: false });
+    data.append(FP16x16 { mag: 936635, sign: false });
+    data.append(FP16x16 { mag: 968403, sign: false });
+    data.append(FP16x16 { mag: 989113, sign: false });
+    data.append(FP16x16 { mag: 1020881, sign: false });
+    data.append(FP16x16 { mag: 1050438, sign: false });
+    data.append(FP16x16 { mag: 1072131, sign: false });
+    data.append(FP16x16 { mag: 1084287, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest.cairo b/tests/nodes/resize_upsample_sizes_nearest.cairo
new file mode 100644
index 000000000..2c766a209
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest.cairo
@@ -0,0 +1,39 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_sizes_nearest() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::NEAREST),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest/input_0.cairo b/tests/nodes/resize_upsample_sizes_nearest/input_0.cairo
new file mode 100644
index 000000000..84a850bc4
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest/input_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest/input_1.cairo b/tests/nodes/resize_upsample_sizes_nearest/input_1.cairo
new file mode 100644
index 000000000..816910620
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest/input_1.cairo
@@ -0,0 +1,15 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
+    data.append(7);
+    data.append(8);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest/output_0.cairo b/tests/nodes/resize_upsample_sizes_nearest/output_0.cairo
new file mode 100644
index 000000000..592bfbcd8
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest/output_0.cairo
@@ -0,0 +1,71 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(7);
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_axes_2_3.cairo b/tests/nodes/resize_upsample_sizes_nearest_axes_2_3.cairo
new file mode 100644
index 000000000..8074ecf36
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_axes_2_3.cairo
@@ -0,0 +1,41 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_sizes_nearest_axes_2_3() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let axes = Option::Some(input_2::input_2().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::None,
+            axes,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::NEAREST),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_axes_2_3/input_0.cairo b/tests/nodes/resize_upsample_sizes_nearest_axes_2_3/input_0.cairo
new file mode 100644
index 000000000..84a850bc4
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_axes_2_3/input_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_axes_2_3/input_1.cairo b/tests/nodes/resize_upsample_sizes_nearest_axes_2_3/input_1.cairo
new file mode 100644
index 000000000..5fd88f168
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_axes_2_3/input_1.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(7);
+    data.append(8);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_axes_2_3/input_2.cairo b/tests/nodes/resize_upsample_sizes_nearest_axes_2_3/input_2.cairo
new file mode 100644
index 000000000..6388a13b0
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_axes_2_3/input_2.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_2() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(2);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_axes_2_3/output_0.cairo b/tests/nodes/resize_upsample_sizes_nearest_axes_2_3/output_0.cairo
new file mode 100644
index 000000000..592bfbcd8
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_axes_2_3/output_0.cairo
@@ -0,0 +1,71 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(7);
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_axes_3_2.cairo b/tests/nodes/resize_upsample_sizes_nearest_axes_3_2.cairo
new file mode 100644
index 000000000..8edc4daa2
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_axes_3_2.cairo
@@ -0,0 +1,45 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_sizes_nearest_axes_3_2() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let axes = Option::Some(input_2::input_2().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::None,
+            axes,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::NEAREST),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_axes_3_2/input_0.cairo b/tests/nodes/resize_upsample_sizes_nearest_axes_3_2/input_0.cairo
new file mode 100644
index 000000000..84a850bc4
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_axes_3_2/input_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_axes_3_2/input_1.cairo b/tests/nodes/resize_upsample_sizes_nearest_axes_3_2/input_1.cairo
new file mode 100644
index 000000000..a1e135ae0
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_axes_3_2/input_1.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(8);
+    data.append(7);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_axes_3_2/input_2.cairo b/tests/nodes/resize_upsample_sizes_nearest_axes_3_2/input_2.cairo
new file mode 100644
index 000000000..775bcc066
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_axes_3_2/input_2.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_2() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(3);
+    data.append(2);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_axes_3_2/output_0.cairo b/tests/nodes/resize_upsample_sizes_nearest_axes_3_2/output_0.cairo
new file mode 100644
index 000000000..592bfbcd8
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_axes_3_2/output_0.cairo
@@ -0,0 +1,71 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(7);
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_ceil_half_pixel.cairo b/tests/nodes/resize_upsample_sizes_nearest_ceil_half_pixel.cairo
new file mode 100644
index 000000000..51a9f5216
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_ceil_half_pixel.cairo
@@ -0,0 +1,43 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32TensorPartialEq;
+
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_sizes_nearest_ceil_half_pixel() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::None,
+            Option::None,
+            Option::Some(TRANSFORMATION_MODE::HALF_PIXEL),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::NEAREST),
+            Option::Some(NEAREST_MODE::CEIL),
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_ceil_half_pixel/input_0.cairo b/tests/nodes/resize_upsample_sizes_nearest_ceil_half_pixel/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_ceil_half_pixel/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_ceil_half_pixel/input_1.cairo b/tests/nodes/resize_upsample_sizes_nearest_ceil_half_pixel/input_1.cairo
new file mode 100644
index 000000000..1b0cce2c6
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_ceil_half_pixel/input_1.cairo
@@ -0,0 +1,15 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
+    data.append(8);
+    data.append(8);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_ceil_half_pixel/output_0.cairo b/tests/nodes/resize_upsample_sizes_nearest_ceil_half_pixel/output_0.cairo
new file mode 100644
index 000000000..9c8571539
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_ceil_half_pixel/output_0.cairo
@@ -0,0 +1,79 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(8);
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_floor_align_corners.cairo b/tests/nodes/resize_upsample_sizes_nearest_floor_align_corners.cairo
new file mode 100644
index 000000000..172beaa84
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_floor_align_corners.cairo
@@ -0,0 +1,41 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_sizes_nearest_floor_align_corners() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::None,
+            Option::None,
+            Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::NEAREST),
+            Option::Some(NEAREST_MODE::FLOOR),
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_floor_align_corners/input_0.cairo b/tests/nodes/resize_upsample_sizes_nearest_floor_align_corners/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_floor_align_corners/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_floor_align_corners/input_1.cairo b/tests/nodes/resize_upsample_sizes_nearest_floor_align_corners/input_1.cairo
new file mode 100644
index 000000000..1b0cce2c6
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_floor_align_corners/input_1.cairo
@@ -0,0 +1,15 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
+    data.append(8);
+    data.append(8);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_floor_align_corners/output_0.cairo b/tests/nodes/resize_upsample_sizes_nearest_floor_align_corners/output_0.cairo
new file mode 100644
index 000000000..bcfaad1bd
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_floor_align_corners/output_0.cairo
@@ -0,0 +1,79 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(8);
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_not_larger.cairo b/tests/nodes/resize_upsample_sizes_nearest_not_larger.cairo
new file mode 100644
index 000000000..4e5e6221b
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_not_larger.cairo
@@ -0,0 +1,45 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_sizes_nearest_not_larger() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let axes = Option::Some(input_2::input_2().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::None,
+            axes,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(KEEP_ASPECT_RATIO_POLICY::NOT_LARGER),
+            Option::Some(MODE::NEAREST),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_not_larger/input_0.cairo b/tests/nodes/resize_upsample_sizes_nearest_not_larger/input_0.cairo
new file mode 100644
index 000000000..84a850bc4
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_not_larger/input_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_not_larger/input_1.cairo b/tests/nodes/resize_upsample_sizes_nearest_not_larger/input_1.cairo
new file mode 100644
index 000000000..5fd88f168
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_not_larger/input_1.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(7);
+    data.append(8);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_not_larger/input_2.cairo b/tests/nodes/resize_upsample_sizes_nearest_not_larger/input_2.cairo
new file mode 100644
index 000000000..6388a13b0
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_not_larger/input_2.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_2() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(2);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_not_larger/output_0.cairo b/tests/nodes/resize_upsample_sizes_nearest_not_larger/output_0.cairo
new file mode 100644
index 000000000..4bbc98a19
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_not_larger/output_0.cairo
@@ -0,0 +1,64 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(7);
+    shape.append(7);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_not_smaller.cairo b/tests/nodes/resize_upsample_sizes_nearest_not_smaller.cairo
new file mode 100644
index 000000000..25563fa16
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_not_smaller.cairo
@@ -0,0 +1,45 @@
+mod input_0;
+mod input_1;
+mod input_2;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::operators::tensor::U32Tensor;
+
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_sizes_nearest_not_smaller() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let axes = Option::Some(input_2::input_2().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::None,
+            axes,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(KEEP_ASPECT_RATIO_POLICY::NOT_SMALLER),
+            Option::Some(MODE::NEAREST),
+            Option::None,
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_not_smaller/input_0.cairo b/tests/nodes/resize_upsample_sizes_nearest_not_smaller/input_0.cairo
new file mode 100644
index 000000000..84a850bc4
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_not_smaller/input_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_not_smaller/input_1.cairo b/tests/nodes/resize_upsample_sizes_nearest_not_smaller/input_1.cairo
new file mode 100644
index 000000000..5fd88f168
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_not_smaller/input_1.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(7);
+    data.append(8);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_not_smaller/input_2.cairo b/tests/nodes/resize_upsample_sizes_nearest_not_smaller/input_2.cairo
new file mode 100644
index 000000000..6388a13b0
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_not_smaller/input_2.cairo
@@ -0,0 +1,13 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_2() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(2);
+    data.append(3);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_not_smaller/output_0.cairo b/tests/nodes/resize_upsample_sizes_nearest_not_smaller/output_0.cairo
new file mode 100644
index 000000000..aeac198d9
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_not_smaller/output_0.cairo
@@ -0,0 +1,79 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(8);
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric.cairo b/tests/nodes/resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric.cairo
new file mode 100644
index 000000000..27cc03ec8
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric.cairo
@@ -0,0 +1,41 @@
+mod input_0;
+mod input_1;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::tensor::math::resize::{
+    MODE, NEAREST_MODE, KEEP_ASPECT_RATIO_POLICY, TRANSFORMATION_MODE
+};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric() {
+    let data = input_0::input_0();
+    let mut sizes = Option::Some(input_1::input_1().data);
+    let z_0 = output_0::output_0();
+
+    let y_0 = data
+        .resize(
+            Option::None,
+            Option::None,
+            sizes,
+            Option::None,
+            Option::None,
+            Option::Some(TRANSFORMATION_MODE::ASYMMETRIC),
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::None,
+            Option::Some(MODE::NEAREST),
+            Option::Some(NEAREST_MODE::ROUND_PREFER_CEIL),
+        );
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric/input_0.cairo b/tests/nodes/resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric/input_0.cairo
new file mode 100644
index 000000000..cd17b9adf
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric/input_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(4);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric/input_1.cairo b/tests/nodes/resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric/input_1.cairo
new file mode 100644
index 000000000..1b0cce2c6
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric/input_1.cairo
@@ -0,0 +1,15 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_1() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
+    data.append(8);
+    data.append(8);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric/output_0.cairo b/tests/nodes/resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric/output_0.cairo
new file mode 100644
index 000000000..9c8571539
--- /dev/null
+++ b/tests/nodes/resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric/output_0.cairo
@@ -0,0 +1,79 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+    shape.append(1);
+    shape.append(8);
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 327680, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 393216, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 458752, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 524288, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 589824, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 655360, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 720896, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 851968, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 983040, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    data.append(FP16x16 { mag: 1048576, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/sequence_at_fp16x16_negative.cairo b/tests/nodes/sequence_at_fp16x16_negative.cairo
index d65c1e56f..12bd5507c 100644
--- a/tests/nodes/sequence_at_fp16x16_negative.cairo
+++ b/tests/nodes/sequence_at_fp16x16_negative.cairo
@@ -10,6 +10,8 @@ use orion::operators::tensor::I32Tensor;
 use orion::utils::{assert_eq, assert_seq_eq};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I32TensorPartialEq;
+use orion::operators::sequence::FP16x16Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_at_fp16x16_negative() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_at(input_0, input_1);
+    let y = SequenceTrait::sequence_at(input_0, input_1);
 
     assert_eq(y, z);
 }
diff --git a/tests/nodes/sequence_at_fp16x16_positive.cairo b/tests/nodes/sequence_at_fp16x16_positive.cairo
index be329f13a..c4f35cac4 100644
--- a/tests/nodes/sequence_at_fp16x16_positive.cairo
+++ b/tests/nodes/sequence_at_fp16x16_positive.cairo
@@ -10,6 +10,8 @@ use orion::operators::tensor::I32Tensor;
 use orion::utils::{assert_eq, assert_seq_eq};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I32TensorPartialEq;
+use orion::operators::sequence::FP16x16Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_at_fp16x16_positive() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_at(input_0, input_1);
+    let y = SequenceTrait::sequence_at(input_0, input_1);
 
     assert_eq(y, z);
 }
diff --git a/tests/nodes/sequence_at_fp8x23_negative.cairo b/tests/nodes/sequence_at_fp8x23_negative.cairo
index e222aad40..4a04b333c 100644
--- a/tests/nodes/sequence_at_fp8x23_negative.cairo
+++ b/tests/nodes/sequence_at_fp8x23_negative.cairo
@@ -10,6 +10,8 @@ use orion::utils::{assert_eq, assert_seq_eq};
 use orion::operators::tensor::FP8x23TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I32TensorPartialEq;
+use orion::operators::sequence::FP8x23Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_at_fp8x23_negative() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_at(input_0, input_1);
+    let y = SequenceTrait::sequence_at(input_0, input_1);
 
     assert_eq(y, z);
 }
diff --git a/tests/nodes/sequence_at_fp8x23_positive.cairo b/tests/nodes/sequence_at_fp8x23_positive.cairo
index f1b4b627e..543711645 100644
--- a/tests/nodes/sequence_at_fp8x23_positive.cairo
+++ b/tests/nodes/sequence_at_fp8x23_positive.cairo
@@ -10,6 +10,8 @@ use orion::utils::{assert_eq, assert_seq_eq};
 use orion::operators::tensor::FP8x23TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I32TensorPartialEq;
+use orion::operators::sequence::FP8x23Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_at_fp8x23_positive() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_at(input_0, input_1);
+    let y = SequenceTrait::sequence_at(input_0, input_1);
 
     assert_eq(y, z);
 }
diff --git a/tests/nodes/sequence_at_i32_negative.cairo b/tests/nodes/sequence_at_i32_negative.cairo
index 6ba6b2594..d45079d6a 100644
--- a/tests/nodes/sequence_at_i32_negative.cairo
+++ b/tests/nodes/sequence_at_i32_negative.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::I32Tensor;
 use orion::utils::{assert_eq, assert_seq_eq};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I32TensorPartialEq;
+use orion::operators::sequence::I32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -16,7 +18,7 @@ fn test_sequence_at_i32_negative() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_at(input_0, input_1);
+    let y = SequenceTrait::sequence_at(input_0, input_1);
 
     assert_eq(y, z);
 }
diff --git a/tests/nodes/sequence_at_i32_positive.cairo b/tests/nodes/sequence_at_i32_positive.cairo
index 36647a780..c25e289b7 100644
--- a/tests/nodes/sequence_at_i32_positive.cairo
+++ b/tests/nodes/sequence_at_i32_positive.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::I32Tensor;
 use orion::utils::{assert_eq, assert_seq_eq};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I32TensorPartialEq;
+use orion::operators::sequence::I32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -16,7 +18,7 @@ fn test_sequence_at_i32_positive() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_at(input_0, input_1);
+    let y = SequenceTrait::sequence_at(input_0, input_1);
 
     assert_eq(y, z);
 }
diff --git a/tests/nodes/sequence_at_i8_negative.cairo b/tests/nodes/sequence_at_i8_negative.cairo
index 1e176feda..33007f491 100644
--- a/tests/nodes/sequence_at_i8_negative.cairo
+++ b/tests/nodes/sequence_at_i8_negative.cairo
@@ -10,6 +10,8 @@ use orion::utils::{assert_eq, assert_seq_eq};
 use orion::operators::tensor::I8Tensor;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I8TensorPartialEq;
+use orion::operators::sequence::I8Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_at_i8_negative() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_at(input_0, input_1);
+    let y = SequenceTrait::sequence_at(input_0, input_1);
 
     assert_eq(y, z);
 }
diff --git a/tests/nodes/sequence_at_i8_positive.cairo b/tests/nodes/sequence_at_i8_positive.cairo
index b45bbe178..4cd397fff 100644
--- a/tests/nodes/sequence_at_i8_positive.cairo
+++ b/tests/nodes/sequence_at_i8_positive.cairo
@@ -10,6 +10,8 @@ use orion::utils::{assert_eq, assert_seq_eq};
 use orion::operators::tensor::I8Tensor;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I8TensorPartialEq;
+use orion::operators::sequence::I8Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_at_i8_positive() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_at(input_0, input_1);
+    let y = SequenceTrait::sequence_at(input_0, input_1);
 
     assert_eq(y, z);
 }
diff --git a/tests/nodes/sequence_at_u32_negative.cairo b/tests/nodes/sequence_at_u32_negative.cairo
index ae5a20d90..dc22c2cbc 100644
--- a/tests/nodes/sequence_at_u32_negative.cairo
+++ b/tests/nodes/sequence_at_u32_negative.cairo
@@ -10,6 +10,8 @@ use orion::utils::{assert_eq, assert_seq_eq};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::U32Tensor;
 use orion::operators::tensor::I32TensorPartialEq;
+use orion::operators::sequence::U32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_at_u32_negative() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_at(input_0, input_1);
+    let y = SequenceTrait::sequence_at(input_0, input_1);
 
     assert_eq(y, z);
 }
diff --git a/tests/nodes/sequence_at_u32_positive.cairo b/tests/nodes/sequence_at_u32_positive.cairo
index 6e3922cd1..b55489cb7 100644
--- a/tests/nodes/sequence_at_u32_positive.cairo
+++ b/tests/nodes/sequence_at_u32_positive.cairo
@@ -10,6 +10,8 @@ use orion::utils::{assert_eq, assert_seq_eq};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::U32Tensor;
 use orion::operators::tensor::I32TensorPartialEq;
+use orion::operators::sequence::U32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_at_u32_positive() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_at(input_0, input_1);
+    let y = SequenceTrait::sequence_at(input_0, input_1);
 
     assert_eq(y, z);
 }
diff --git a/tests/nodes/sequence_construct_fp16x16.cairo b/tests/nodes/sequence_construct_fp16x16.cairo
index 13f4197d8..c6d2238fb 100644
--- a/tests/nodes/sequence_construct_fp16x16.cairo
+++ b/tests/nodes/sequence_construct_fp16x16.cairo
@@ -6,7 +6,8 @@ use orion::operators::tensor::FP16x16TensorPartialEq;
 use orion::operators::tensor::{TensorTrait, Tensor};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
-use orion::operators::tensor::FP16x16Tensor;
+use orion::operators::sequence::SequenceTrait;
+use orion::operators::sequence::FP16x16Sequence;
 
 #[test]
 #[available_gas(2000000000)]
@@ -14,7 +15,7 @@ fn test_sequence_construct_fp16x16() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_construct(input_0);
+    let y = SequenceTrait::sequence_construct(input_0);
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_construct_fp16x16/input_0.cairo b/tests/nodes/sequence_construct_fp16x16/input_0.cairo
index 046dc143d..3c02bd6e7 100644
--- a/tests/nodes/sequence_construct_fp16x16/input_0.cairo
+++ b/tests/nodes/sequence_construct_fp16x16/input_0.cairo
@@ -7,68 +7,47 @@ fn input_0() -> Array<Tensor<FP16x16>> {
     let mut sequence = ArrayTrait::new();
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(3);
     shape.append(1);
-
-    let mut data = ArrayTrait::new();
-    data.append(FP16x16 { mag: 131072, sign: true });
-    data.append(FP16x16 { mag: 131072, sign: false });
-    data.append(FP16x16 { mag: 262144, sign: false });
-
-    sequence.append(TensorTrait::new(shape.span(), data.span()));
-
-    let mut shape = ArrayTrait::<usize>::new();
-    shape.append(3);
     shape.append(1);
 
     let mut data = ArrayTrait::new();
-    data.append(FP16x16 { mag: 393216, sign: true });
     data.append(FP16x16 { mag: 327680, sign: false });
-    data.append(FP16x16 { mag: 65536, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(3);
+    shape.append(1);
     shape.append(1);
 
     let mut data = ArrayTrait::new();
-    data.append(FP16x16 { mag: 65536, sign: true });
-    data.append(FP16x16 { mag: 131072, sign: false });
-    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(3);
+    shape.append(1);
     shape.append(1);
 
     let mut data = ArrayTrait::new();
-    data.append(FP16x16 { mag: 262144, sign: true });
-    data.append(FP16x16 { mag: 0, sign: false });
-    data.append(FP16x16 { mag: 196608, sign: true });
+    data.append(FP16x16 { mag: 327680, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(3);
+    shape.append(1);
     shape.append(1);
 
     let mut data = ArrayTrait::new();
-    data.append(FP16x16 { mag: 65536, sign: false });
-    data.append(FP16x16 { mag: 262144, sign: false });
-    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 0, sign: false });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(3);
+    shape.append(1);
     shape.append(1);
 
     let mut data = ArrayTrait::new();
-    data.append(FP16x16 { mag: 327680, sign: false });
-    data.append(FP16x16 { mag: 196608, sign: false });
-    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 0, sign: false });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
diff --git a/tests/nodes/sequence_construct_fp16x16/output_0.cairo b/tests/nodes/sequence_construct_fp16x16/output_0.cairo
index 37e724d77..0fe9053ce 100644
--- a/tests/nodes/sequence_construct_fp16x16/output_0.cairo
+++ b/tests/nodes/sequence_construct_fp16x16/output_0.cairo
@@ -7,68 +7,47 @@ fn output_0() -> Array<Tensor<FP16x16>> {
     let mut sequence = ArrayTrait::new();
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(3);
     shape.append(1);
-
-    let mut data = ArrayTrait::new();
-    data.append(FP16x16 { mag: 131072, sign: true });
-    data.append(FP16x16 { mag: 131072, sign: false });
-    data.append(FP16x16 { mag: 262144, sign: false });
-
-    sequence.append(TensorTrait::new(shape.span(), data.span()));
-
-    let mut shape = ArrayTrait::<usize>::new();
-    shape.append(3);
     shape.append(1);
 
     let mut data = ArrayTrait::new();
-    data.append(FP16x16 { mag: 393216, sign: true });
     data.append(FP16x16 { mag: 327680, sign: false });
-    data.append(FP16x16 { mag: 65536, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(3);
+    shape.append(1);
     shape.append(1);
 
     let mut data = ArrayTrait::new();
-    data.append(FP16x16 { mag: 65536, sign: true });
-    data.append(FP16x16 { mag: 131072, sign: false });
-    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 196608, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(3);
+    shape.append(1);
     shape.append(1);
 
     let mut data = ArrayTrait::new();
-    data.append(FP16x16 { mag: 262144, sign: true });
-    data.append(FP16x16 { mag: 0, sign: false });
-    data.append(FP16x16 { mag: 196608, sign: true });
+    data.append(FP16x16 { mag: 327680, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(3);
+    shape.append(1);
     shape.append(1);
 
     let mut data = ArrayTrait::new();
-    data.append(FP16x16 { mag: 65536, sign: false });
-    data.append(FP16x16 { mag: 262144, sign: false });
-    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 0, sign: false });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(3);
+    shape.append(1);
     shape.append(1);
 
     let mut data = ArrayTrait::new();
-    data.append(FP16x16 { mag: 327680, sign: false });
-    data.append(FP16x16 { mag: 196608, sign: false });
-    data.append(FP16x16 { mag: 65536, sign: false });
+    data.append(FP16x16 { mag: 0, sign: false });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
diff --git a/tests/nodes/sequence_construct_fp8x23.cairo b/tests/nodes/sequence_construct_fp8x23.cairo
index 216ade001..fa08e589f 100644
--- a/tests/nodes/sequence_construct_fp8x23.cairo
+++ b/tests/nodes/sequence_construct_fp8x23.cairo
@@ -7,6 +7,8 @@ use orion::operators::tensor::FP8x23Tensor;
 use orion::operators::tensor::{TensorTrait, Tensor};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::FP8x23Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -14,7 +16,7 @@ fn test_sequence_construct_fp8x23() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_construct(input_0);
+    let y = SequenceTrait::sequence_construct(input_0);
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_construct_fp8x23/input_0.cairo b/tests/nodes/sequence_construct_fp8x23/input_0.cairo
index 88c11706e..2c8b74ec1 100644
--- a/tests/nodes/sequence_construct_fp8x23/input_0.cairo
+++ b/tests/nodes/sequence_construct_fp8x23/input_0.cairo
@@ -12,7 +12,7 @@ fn input_0() -> Array<Tensor<FP8x23>> {
 
     let mut data = ArrayTrait::new();
     data.append(FP8x23 { mag: 41943040, sign: true });
-    data.append(FP8x23 { mag: 16777216, sign: false });
+    data.append(FP8x23 { mag: 8388608, sign: false });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
@@ -21,28 +21,8 @@ fn input_0() -> Array<Tensor<FP8x23>> {
     shape.append(2);
 
     let mut data = ArrayTrait::new();
-    data.append(FP8x23 { mag: 25165824, sign: false });
-    data.append(FP8x23 { mag: 33554432, sign: false });
-
-    sequence.append(TensorTrait::new(shape.span(), data.span()));
-
-    let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(2);
-
-    let mut data = ArrayTrait::new();
-    data.append(FP8x23 { mag: 8388608, sign: true });
-    data.append(FP8x23 { mag: 25165824, sign: false });
-
-    sequence.append(TensorTrait::new(shape.span(), data.span()));
-
-    let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(2);
-
-    let mut data = ArrayTrait::new();
-    data.append(FP8x23 { mag: 16777216, sign: false });
-    data.append(FP8x23 { mag: 25165824, sign: false });
+    data.append(FP8x23 { mag: 33554432, sign: true });
+    data.append(FP8x23 { mag: 41943040, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
@@ -51,8 +31,8 @@ fn input_0() -> Array<Tensor<FP8x23>> {
     shape.append(2);
 
     let mut data = ArrayTrait::new();
-    data.append(FP8x23 { mag: 33554432, sign: true });
-    data.append(FP8x23 { mag: 41943040, sign: false });
+    data.append(FP8x23 { mag: 33554432, sign: false });
+    data.append(FP8x23 { mag: 25165824, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
@@ -61,8 +41,8 @@ fn input_0() -> Array<Tensor<FP8x23>> {
     shape.append(2);
 
     let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 0, sign: false });
     data.append(FP8x23 { mag: 41943040, sign: true });
-    data.append(FP8x23 { mag: 41943040, sign: false });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
@@ -71,8 +51,8 @@ fn input_0() -> Array<Tensor<FP8x23>> {
     shape.append(2);
 
     let mut data = ArrayTrait::new();
-    data.append(FP8x23 { mag: 8388608, sign: true });
-    data.append(FP8x23 { mag: 33554432, sign: false });
+    data.append(FP8x23 { mag: 8388608, sign: false });
+    data.append(FP8x23 { mag: 33554432, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
@@ -81,8 +61,8 @@ fn input_0() -> Array<Tensor<FP8x23>> {
     shape.append(2);
 
     let mut data = ArrayTrait::new();
-    data.append(FP8x23 { mag: 25165824, sign: true });
-    data.append(FP8x23 { mag: 16777216, sign: true });
+    data.append(FP8x23 { mag: 25165824, sign: false });
+    data.append(FP8x23 { mag: 50331648, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
diff --git a/tests/nodes/sequence_construct_fp8x23/output_0.cairo b/tests/nodes/sequence_construct_fp8x23/output_0.cairo
index 237ba4d9e..75264e3c8 100644
--- a/tests/nodes/sequence_construct_fp8x23/output_0.cairo
+++ b/tests/nodes/sequence_construct_fp8x23/output_0.cairo
@@ -12,7 +12,7 @@ fn output_0() -> Array<Tensor<FP8x23>> {
 
     let mut data = ArrayTrait::new();
     data.append(FP8x23 { mag: 41943040, sign: true });
-    data.append(FP8x23 { mag: 16777216, sign: false });
+    data.append(FP8x23 { mag: 8388608, sign: false });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
@@ -21,28 +21,8 @@ fn output_0() -> Array<Tensor<FP8x23>> {
     shape.append(2);
 
     let mut data = ArrayTrait::new();
-    data.append(FP8x23 { mag: 25165824, sign: false });
-    data.append(FP8x23 { mag: 33554432, sign: false });
-
-    sequence.append(TensorTrait::new(shape.span(), data.span()));
-
-    let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(2);
-
-    let mut data = ArrayTrait::new();
-    data.append(FP8x23 { mag: 8388608, sign: true });
-    data.append(FP8x23 { mag: 25165824, sign: false });
-
-    sequence.append(TensorTrait::new(shape.span(), data.span()));
-
-    let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(2);
-
-    let mut data = ArrayTrait::new();
-    data.append(FP8x23 { mag: 16777216, sign: false });
-    data.append(FP8x23 { mag: 25165824, sign: false });
+    data.append(FP8x23 { mag: 33554432, sign: true });
+    data.append(FP8x23 { mag: 41943040, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
@@ -51,8 +31,8 @@ fn output_0() -> Array<Tensor<FP8x23>> {
     shape.append(2);
 
     let mut data = ArrayTrait::new();
-    data.append(FP8x23 { mag: 33554432, sign: true });
-    data.append(FP8x23 { mag: 41943040, sign: false });
+    data.append(FP8x23 { mag: 33554432, sign: false });
+    data.append(FP8x23 { mag: 25165824, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
@@ -61,8 +41,8 @@ fn output_0() -> Array<Tensor<FP8x23>> {
     shape.append(2);
 
     let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 0, sign: false });
     data.append(FP8x23 { mag: 41943040, sign: true });
-    data.append(FP8x23 { mag: 41943040, sign: false });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
@@ -71,8 +51,8 @@ fn output_0() -> Array<Tensor<FP8x23>> {
     shape.append(2);
 
     let mut data = ArrayTrait::new();
-    data.append(FP8x23 { mag: 8388608, sign: true });
-    data.append(FP8x23 { mag: 33554432, sign: false });
+    data.append(FP8x23 { mag: 8388608, sign: false });
+    data.append(FP8x23 { mag: 33554432, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
@@ -81,8 +61,8 @@ fn output_0() -> Array<Tensor<FP8x23>> {
     shape.append(2);
 
     let mut data = ArrayTrait::new();
-    data.append(FP8x23 { mag: 25165824, sign: true });
-    data.append(FP8x23 { mag: 16777216, sign: true });
+    data.append(FP8x23 { mag: 25165824, sign: false });
+    data.append(FP8x23 { mag: 50331648, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
diff --git a/tests/nodes/sequence_construct_i32.cairo b/tests/nodes/sequence_construct_i32.cairo
index db083f990..f59b4df1b 100644
--- a/tests/nodes/sequence_construct_i32.cairo
+++ b/tests/nodes/sequence_construct_i32.cairo
@@ -7,6 +7,8 @@ use orion::operators::tensor::{TensorTrait, Tensor};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::operators::tensor::I32TensorPartialEq;
+use orion::operators::sequence::I32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -14,7 +16,7 @@ fn test_sequence_construct_i32() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_construct(input_0);
+    let y = SequenceTrait::sequence_construct(input_0);
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_construct_i32/input_0.cairo b/tests/nodes/sequence_construct_i32/input_0.cairo
index 1c994283d..e06dc295c 100644
--- a/tests/nodes/sequence_construct_i32/input_0.cairo
+++ b/tests/nodes/sequence_construct_i32/input_0.cairo
@@ -7,22 +7,42 @@ fn input_0() -> Array<Tensor<i32>> {
     let mut sequence = ArrayTrait::new();
 
     let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
     shape.append(1);
+
+    let mut data = ArrayTrait::new();
+    data.append(i32 { mag: 5, sign: false });
+    data.append(i32 { mag: 5, sign: true });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
     shape.append(2);
+    shape.append(1);
 
     let mut data = ArrayTrait::new();
-    data.append(i32 { mag: 2, sign: false });
-    data.append(i32 { mag: 6, sign: true });
+    data.append(i32 { mag: 2, sign: true });
+    data.append(i32 { mag: 5, sign: false });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
     shape.append(1);
+
+    let mut data = ArrayTrait::new();
+    data.append(i32 { mag: 2, sign: false });
+    data.append(i32 { mag: 3, sign: false });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
     shape.append(2);
+    shape.append(1);
 
     let mut data = ArrayTrait::new();
-    data.append(i32 { mag: 5, sign: false });
-    data.append(i32 { mag: 6, sign: true });
+    data.append(i32 { mag: 0, sign: false });
+    data.append(i32 { mag: 5, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
diff --git a/tests/nodes/sequence_construct_i32/output_0.cairo b/tests/nodes/sequence_construct_i32/output_0.cairo
index f8cab4f6f..d0b8d356c 100644
--- a/tests/nodes/sequence_construct_i32/output_0.cairo
+++ b/tests/nodes/sequence_construct_i32/output_0.cairo
@@ -7,22 +7,42 @@ fn output_0() -> Array<Tensor<i32>> {
     let mut sequence = ArrayTrait::new();
 
     let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
     shape.append(1);
+
+    let mut data = ArrayTrait::new();
+    data.append(i32 { mag: 5, sign: false });
+    data.append(i32 { mag: 5, sign: true });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
     shape.append(2);
+    shape.append(1);
 
     let mut data = ArrayTrait::new();
-    data.append(i32 { mag: 2, sign: false });
-    data.append(i32 { mag: 6, sign: true });
+    data.append(i32 { mag: 2, sign: true });
+    data.append(i32 { mag: 5, sign: false });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
     shape.append(1);
+
+    let mut data = ArrayTrait::new();
+    data.append(i32 { mag: 2, sign: false });
+    data.append(i32 { mag: 3, sign: false });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
     shape.append(2);
+    shape.append(1);
 
     let mut data = ArrayTrait::new();
-    data.append(i32 { mag: 5, sign: false });
-    data.append(i32 { mag: 6, sign: true });
+    data.append(i32 { mag: 0, sign: false });
+    data.append(i32 { mag: 5, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
diff --git a/tests/nodes/sequence_construct_i8.cairo b/tests/nodes/sequence_construct_i8.cairo
index dbdd49abf..4d4a3f5ac 100644
--- a/tests/nodes/sequence_construct_i8.cairo
+++ b/tests/nodes/sequence_construct_i8.cairo
@@ -6,7 +6,8 @@ use orion::operators::tensor::I8TensorPartialEq;
 use orion::operators::tensor::{TensorTrait, Tensor};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
-use orion::operators::tensor::I8Tensor;
+use orion::operators::sequence::SequenceTrait;
+use orion::operators::sequence::I8Sequence;
 
 #[test]
 #[available_gas(2000000000)]
@@ -14,7 +15,7 @@ fn test_sequence_construct_i8() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_construct(input_0);
+    let y = SequenceTrait::sequence_construct(input_0);
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_construct_i8/input_0.cairo b/tests/nodes/sequence_construct_i8/input_0.cairo
index c2c82d57f..715483b5b 100644
--- a/tests/nodes/sequence_construct_i8/input_0.cairo
+++ b/tests/nodes/sequence_construct_i8/input_0.cairo
@@ -7,35 +7,42 @@ fn input_0() -> Array<Tensor<i8>> {
     let mut sequence = ArrayTrait::new();
 
     let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
     shape.append(1);
-    shape.append(3);
 
     let mut data = ArrayTrait::new();
-    data.append(i8 { mag: 1, sign: false });
-    data.append(i8 { mag: 5, sign: true });
-    data.append(i8 { mag: 5, sign: false });
+    data.append(i8 { mag: 4, sign: false });
+    data.append(i8 { mag: 6, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
     shape.append(1);
-    shape.append(3);
 
     let mut data = ArrayTrait::new();
-    data.append(i8 { mag: 5, sign: false });
+    data.append(i8 { mag: 0, sign: false });
     data.append(i8 { mag: 4, sign: false });
-    data.append(i8 { mag: 6, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
     shape.append(1);
-    shape.append(3);
 
     let mut data = ArrayTrait::new();
-    data.append(i8 { mag: 5, sign: false });
-    data.append(i8 { mag: 3, sign: true });
-    data.append(i8 { mag: 4, sign: false });
+    data.append(i8 { mag: 3, sign: false });
+    data.append(i8 { mag: 1, sign: true });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(1);
+
+    let mut data = ArrayTrait::new();
+    data.append(i8 { mag: 2, sign: true });
+    data.append(i8 { mag: 3, sign: false });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
diff --git a/tests/nodes/sequence_construct_i8/output_0.cairo b/tests/nodes/sequence_construct_i8/output_0.cairo
index 70d9197d0..268530d13 100644
--- a/tests/nodes/sequence_construct_i8/output_0.cairo
+++ b/tests/nodes/sequence_construct_i8/output_0.cairo
@@ -7,35 +7,42 @@ fn output_0() -> Array<Tensor<i8>> {
     let mut sequence = ArrayTrait::new();
 
     let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
     shape.append(1);
-    shape.append(3);
 
     let mut data = ArrayTrait::new();
-    data.append(i8 { mag: 1, sign: false });
-    data.append(i8 { mag: 5, sign: true });
-    data.append(i8 { mag: 5, sign: false });
+    data.append(i8 { mag: 4, sign: false });
+    data.append(i8 { mag: 6, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
     shape.append(1);
-    shape.append(3);
 
     let mut data = ArrayTrait::new();
-    data.append(i8 { mag: 5, sign: false });
+    data.append(i8 { mag: 0, sign: false });
     data.append(i8 { mag: 4, sign: false });
-    data.append(i8 { mag: 6, sign: true });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
     shape.append(1);
-    shape.append(3);
 
     let mut data = ArrayTrait::new();
-    data.append(i8 { mag: 5, sign: false });
-    data.append(i8 { mag: 3, sign: true });
-    data.append(i8 { mag: 4, sign: false });
+    data.append(i8 { mag: 3, sign: false });
+    data.append(i8 { mag: 1, sign: true });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(1);
+
+    let mut data = ArrayTrait::new();
+    data.append(i8 { mag: 2, sign: true });
+    data.append(i8 { mag: 3, sign: false });
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
diff --git a/tests/nodes/sequence_construct_u32.cairo b/tests/nodes/sequence_construct_u32.cairo
index e7683ad8a..a6a783f51 100644
--- a/tests/nodes/sequence_construct_u32.cairo
+++ b/tests/nodes/sequence_construct_u32.cairo
@@ -7,6 +7,8 @@ use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::U32Tensor;
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::sequence::SequenceTrait;
+use orion::operators::sequence::U32Sequence;
 
 #[test]
 #[available_gas(2000000000)]
@@ -14,7 +16,7 @@ fn test_sequence_construct_u32() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_construct(input_0);
+    let y = SequenceTrait::sequence_construct(input_0);
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_construct_u32/input_0.cairo b/tests/nodes/sequence_construct_u32/input_0.cairo
index 506c377e7..a24fb6345 100644
--- a/tests/nodes/sequence_construct_u32/input_0.cairo
+++ b/tests/nodes/sequence_construct_u32/input_0.cairo
@@ -6,83 +6,104 @@ fn input_0() -> Array<Tensor<u32>> {
     let mut sequence = ArrayTrait::new();
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
+    shape.append(3);
+    shape.append(3);
 
     let mut data = ArrayTrait::new();
-    data.append(4);
+    data.append(1);
+    data.append(3);
+    data.append(3);
+    data.append(5);
+    data.append(2);
+    data.append(2);
+    data.append(1);
+    data.append(0);
+    data.append(5);
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
+    shape.append(3);
+    shape.append(3);
 
     let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
     data.append(4);
-
-    sequence.append(TensorTrait::new(shape.span(), data.span()));
-
-    let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
-
-    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(4);
+    data.append(4);
+    data.append(2);
+    data.append(3);
     data.append(5);
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
+    shape.append(3);
+    shape.append(3);
 
     let mut data = ArrayTrait::new();
     data.append(3);
-
-    sequence.append(TensorTrait::new(shape.span(), data.span()));
-
-    let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
-
-    let mut data = ArrayTrait::new();
+    data.append(3);
+    data.append(4);
     data.append(0);
-
-    sequence.append(TensorTrait::new(shape.span(), data.span()));
-
-    let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
-
-    let mut data = ArrayTrait::new();
+    data.append(1);
     data.append(0);
+    data.append(2);
+    data.append(1);
+    data.append(3);
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
+    shape.append(3);
+    shape.append(3);
 
     let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(5);
+    data.append(1);
+    data.append(2);
+    data.append(5);
+    data.append(0);
+    data.append(3);
+    data.append(4);
     data.append(3);
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
+    shape.append(3);
+    shape.append(3);
 
     let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(5);
+    data.append(2);
+    data.append(1);
+    data.append(3);
+    data.append(2);
+    data.append(4);
+    data.append(1);
     data.append(4);
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
+    shape.append(3);
+    shape.append(3);
 
     let mut data = ArrayTrait::new();
+    data.append(5);
+    data.append(4);
+    data.append(5);
     data.append(1);
+    data.append(5);
+    data.append(0);
+    data.append(4);
+    data.append(2);
+    data.append(2);
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
diff --git a/tests/nodes/sequence_construct_u32/output_0.cairo b/tests/nodes/sequence_construct_u32/output_0.cairo
index 110abad73..59872afbd 100644
--- a/tests/nodes/sequence_construct_u32/output_0.cairo
+++ b/tests/nodes/sequence_construct_u32/output_0.cairo
@@ -6,83 +6,104 @@ fn output_0() -> Array<Tensor<u32>> {
     let mut sequence = ArrayTrait::new();
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
+    shape.append(3);
+    shape.append(3);
 
     let mut data = ArrayTrait::new();
-    data.append(4);
+    data.append(1);
+    data.append(3);
+    data.append(3);
+    data.append(5);
+    data.append(2);
+    data.append(2);
+    data.append(1);
+    data.append(0);
+    data.append(5);
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
+    shape.append(3);
+    shape.append(3);
 
     let mut data = ArrayTrait::new();
+    data.append(1);
+    data.append(1);
     data.append(4);
-
-    sequence.append(TensorTrait::new(shape.span(), data.span()));
-
-    let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
-
-    let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(4);
+    data.append(4);
+    data.append(2);
+    data.append(3);
     data.append(5);
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
+    shape.append(3);
+    shape.append(3);
 
     let mut data = ArrayTrait::new();
     data.append(3);
-
-    sequence.append(TensorTrait::new(shape.span(), data.span()));
-
-    let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
-
-    let mut data = ArrayTrait::new();
+    data.append(3);
+    data.append(4);
     data.append(0);
-
-    sequence.append(TensorTrait::new(shape.span(), data.span()));
-
-    let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
-
-    let mut data = ArrayTrait::new();
+    data.append(1);
     data.append(0);
+    data.append(2);
+    data.append(1);
+    data.append(3);
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
+    shape.append(3);
+    shape.append(3);
 
     let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(5);
+    data.append(1);
+    data.append(2);
+    data.append(5);
+    data.append(0);
+    data.append(3);
+    data.append(4);
     data.append(3);
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
+    shape.append(3);
+    shape.append(3);
 
     let mut data = ArrayTrait::new();
+    data.append(0);
+    data.append(5);
+    data.append(2);
+    data.append(1);
+    data.append(3);
+    data.append(2);
+    data.append(4);
+    data.append(1);
     data.append(4);
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
     let mut shape = ArrayTrait::<usize>::new();
-    shape.append(1);
-    shape.append(1);
+    shape.append(3);
+    shape.append(3);
 
     let mut data = ArrayTrait::new();
+    data.append(5);
+    data.append(4);
+    data.append(5);
     data.append(1);
+    data.append(5);
+    data.append(0);
+    data.append(4);
+    data.append(2);
+    data.append(2);
 
     sequence.append(TensorTrait::new(shape.span(), data.span()));
 
diff --git a/tests/nodes/sequence_empty_fp16x16.cairo b/tests/nodes/sequence_empty_fp16x16.cairo
index 3686c1893..b9348bb4f 100644
--- a/tests/nodes/sequence_empty_fp16x16.cairo
+++ b/tests/nodes/sequence_empty_fp16x16.cairo
@@ -1,10 +1,10 @@
 mod output_0;
 
 
+use orion::operators::sequence::FP16x16Sequence;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::FP16x16TensorPartialEq;
-use orion::operators::tensor::FP16x16Tensor;
-use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::sequence::SequenceTrait;
 use orion::utils::{assert_eq, assert_seq_eq};
 
 #[test]
@@ -12,7 +12,7 @@ use orion::utils::{assert_eq, assert_seq_eq};
 fn test_sequence_empty_fp16x16() {
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_empty();
+    let y = SequenceTrait::sequence_empty();
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_empty_fp8x23.cairo b/tests/nodes/sequence_empty_fp8x23.cairo
index ee4bf214b..6c7248e7f 100644
--- a/tests/nodes/sequence_empty_fp8x23.cairo
+++ b/tests/nodes/sequence_empty_fp8x23.cairo
@@ -3,16 +3,16 @@ mod output_0;
 
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::FP8x23TensorPartialEq;
-use orion::operators::tensor::{TensorTrait, Tensor};
-use orion::operators::tensor::FP8x23Tensor;
+use orion::operators::sequence::FP8x23Sequence;
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
 fn test_sequence_empty_fp8x23() {
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_empty();
+    let y = SequenceTrait::sequence_empty();
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_empty_i32.cairo b/tests/nodes/sequence_empty_i32.cairo
index 9029b4c26..798bc794a 100644
--- a/tests/nodes/sequence_empty_i32.cairo
+++ b/tests/nodes/sequence_empty_i32.cairo
@@ -1,18 +1,20 @@
 mod output_0;
 
 
+use orion::operators::sequence::SequenceTrait;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I32TensorPartialEq;
 use orion::operators::tensor::{TensorTrait, Tensor};
 use orion::operators::tensor::I32Tensor;
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::I32Sequence;
 
 #[test]
 #[available_gas(2000000000)]
 fn test_sequence_empty_i32() {
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_empty();
+    let y = SequenceTrait::sequence_empty();
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_empty_i8.cairo b/tests/nodes/sequence_empty_i8.cairo
index 4074b2ef7..07e9f8a57 100644
--- a/tests/nodes/sequence_empty_i8.cairo
+++ b/tests/nodes/sequence_empty_i8.cairo
@@ -1,10 +1,11 @@
 mod output_0;
 
 
+use orion::operators::sequence::I8Sequence;
+use orion::operators::sequence::SequenceTrait;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::{TensorTrait, Tensor};
 use orion::utils::{assert_eq, assert_seq_eq};
-use orion::operators::tensor::I8Tensor;
 use orion::operators::tensor::I8TensorPartialEq;
 
 #[test]
@@ -12,7 +13,7 @@ use orion::operators::tensor::I8TensorPartialEq;
 fn test_sequence_empty_i8() {
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_empty();
+    let y = SequenceTrait::sequence_empty();
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_empty_u32.cairo b/tests/nodes/sequence_empty_u32.cairo
index 836c97ff1..ecd7cefef 100644
--- a/tests/nodes/sequence_empty_u32.cairo
+++ b/tests/nodes/sequence_empty_u32.cairo
@@ -6,13 +6,15 @@ use orion::operators::tensor::{TensorTrait, Tensor};
 use orion::operators::tensor::U32Tensor;
 use orion::utils::{assert_eq, assert_seq_eq};
 use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::sequence::SequenceTrait;
+use orion::operators::sequence::U32Sequence;
 
 #[test]
 #[available_gas(2000000000)]
 fn test_sequence_empty_u32() {
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_empty();
+    let y = SequenceTrait::sequence_empty();
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_fp16x16_empty.cairo b/tests/nodes/sequence_erase_fp16x16_empty.cairo
index 23f42b7b5..3c6492874 100644
--- a/tests/nodes/sequence_erase_fp16x16_empty.cairo
+++ b/tests/nodes/sequence_erase_fp16x16_empty.cairo
@@ -7,6 +7,8 @@ use orion::operators::tensor::{TensorTrait, Tensor};
 use orion::operators::tensor::FP16x16TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::FP16x16Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -14,7 +16,7 @@ fn test_sequence_erase_fp16x16_empty() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::None(()));
+    let y = SequenceTrait::sequence_erase(input_0, Option::None(()));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_fp16x16_negative.cairo b/tests/nodes/sequence_erase_fp16x16_negative.cairo
index 183643c99..f8deff4bb 100644
--- a/tests/nodes/sequence_erase_fp16x16_negative.cairo
+++ b/tests/nodes/sequence_erase_fp16x16_negative.cairo
@@ -10,6 +10,8 @@ use orion::operators::tensor::FP16x16TensorPartialEq;
 use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::FP16x16Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_erase_fp16x16_negative() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::Some(input_1));
+    let y = SequenceTrait::sequence_erase(input_0, Option::Some(input_1));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_fp16x16_positive.cairo b/tests/nodes/sequence_erase_fp16x16_positive.cairo
index dc17a00b3..dbc41c494 100644
--- a/tests/nodes/sequence_erase_fp16x16_positive.cairo
+++ b/tests/nodes/sequence_erase_fp16x16_positive.cairo
@@ -10,6 +10,8 @@ use orion::operators::tensor::FP16x16TensorPartialEq;
 use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::FP16x16Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_erase_fp16x16_positive() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::Some(input_1));
+    let y = SequenceTrait::sequence_erase(input_0, Option::Some(input_1));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_fp8x23_empty.cairo b/tests/nodes/sequence_erase_fp8x23_empty.cairo
index 86dbf1cdc..bcaabf9ad 100644
--- a/tests/nodes/sequence_erase_fp8x23_empty.cairo
+++ b/tests/nodes/sequence_erase_fp8x23_empty.cairo
@@ -7,6 +7,8 @@ use orion::operators::tensor::FP8x23TensorPartialEq;
 use orion::operators::tensor::{TensorTrait, Tensor};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::FP8x23Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -14,7 +16,7 @@ fn test_sequence_erase_fp8x23_empty() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::None(()));
+    let y = SequenceTrait::sequence_erase(input_0, Option::None(()));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_fp8x23_negative.cairo b/tests/nodes/sequence_erase_fp8x23_negative.cairo
index 0a427225b..408c8e897 100644
--- a/tests/nodes/sequence_erase_fp8x23_negative.cairo
+++ b/tests/nodes/sequence_erase_fp8x23_negative.cairo
@@ -10,6 +10,8 @@ use orion::operators::tensor::{TensorTrait, Tensor};
 use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::FP8x23Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_erase_fp8x23_negative() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::Some(input_1));
+    let y = SequenceTrait::sequence_erase(input_0, Option::Some(input_1));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_fp8x23_positive.cairo b/tests/nodes/sequence_erase_fp8x23_positive.cairo
index ed1ca666d..ce5f2e1a9 100644
--- a/tests/nodes/sequence_erase_fp8x23_positive.cairo
+++ b/tests/nodes/sequence_erase_fp8x23_positive.cairo
@@ -10,6 +10,8 @@ use orion::operators::tensor::{TensorTrait, Tensor};
 use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::FP8x23Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_erase_fp8x23_positive() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::Some(input_1));
+    let y = SequenceTrait::sequence_erase(input_0, Option::Some(input_1));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_i32_empty.cairo b/tests/nodes/sequence_erase_i32_empty.cairo
index 800a27af0..bd7f7da59 100644
--- a/tests/nodes/sequence_erase_i32_empty.cairo
+++ b/tests/nodes/sequence_erase_i32_empty.cairo
@@ -7,6 +7,8 @@ use orion::operators::tensor::{TensorTrait, Tensor};
 use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::I32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -14,7 +16,7 @@ fn test_sequence_erase_i32_empty() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::None(()));
+    let y = SequenceTrait::sequence_erase(input_0, Option::None(()));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_i32_negative.cairo b/tests/nodes/sequence_erase_i32_negative.cairo
index 4bff09eb8..8d839c32d 100644
--- a/tests/nodes/sequence_erase_i32_negative.cairo
+++ b/tests/nodes/sequence_erase_i32_negative.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::{TensorTrait, Tensor};
 use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::I32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -16,7 +18,7 @@ fn test_sequence_erase_i32_negative() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::Some(input_1));
+    let y = SequenceTrait::sequence_erase(input_0, Option::Some(input_1));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_i32_positive.cairo b/tests/nodes/sequence_erase_i32_positive.cairo
index aee5ef58b..928acffcb 100644
--- a/tests/nodes/sequence_erase_i32_positive.cairo
+++ b/tests/nodes/sequence_erase_i32_positive.cairo
@@ -8,6 +8,8 @@ use orion::operators::tensor::{TensorTrait, Tensor};
 use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::I32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -16,7 +18,7 @@ fn test_sequence_erase_i32_positive() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::Some(input_1));
+    let y = SequenceTrait::sequence_erase(input_0, Option::Some(input_1));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_i8_empty.cairo b/tests/nodes/sequence_erase_i8_empty.cairo
index 1ad6d9f1b..e7a55fb4d 100644
--- a/tests/nodes/sequence_erase_i8_empty.cairo
+++ b/tests/nodes/sequence_erase_i8_empty.cairo
@@ -7,6 +7,8 @@ use orion::operators::tensor::{TensorTrait, Tensor};
 use orion::operators::tensor::I8Tensor;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::I8Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -14,7 +16,7 @@ fn test_sequence_erase_i8_empty() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::None(()));
+    let y = SequenceTrait::sequence_erase(input_0, Option::None(()));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_i8_negative.cairo b/tests/nodes/sequence_erase_i8_negative.cairo
index 0caad87fa..a41daf502 100644
--- a/tests/nodes/sequence_erase_i8_negative.cairo
+++ b/tests/nodes/sequence_erase_i8_negative.cairo
@@ -10,6 +10,8 @@ use orion::operators::tensor::I8Tensor;
 use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::I8Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_erase_i8_negative() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::Some(input_1));
+    let y = SequenceTrait::sequence_erase(input_0, Option::Some(input_1));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_i8_positive.cairo b/tests/nodes/sequence_erase_i8_positive.cairo
index 52d1a62ee..d0c7dfb04 100644
--- a/tests/nodes/sequence_erase_i8_positive.cairo
+++ b/tests/nodes/sequence_erase_i8_positive.cairo
@@ -10,6 +10,8 @@ use orion::operators::tensor::I8Tensor;
 use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::I8Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_erase_i8_positive() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::Some(input_1));
+    let y = SequenceTrait::sequence_erase(input_0, Option::Some(input_1));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_u32_empty.cairo b/tests/nodes/sequence_erase_u32_empty.cairo
index 915fdfd41..8065bda49 100644
--- a/tests/nodes/sequence_erase_u32_empty.cairo
+++ b/tests/nodes/sequence_erase_u32_empty.cairo
@@ -7,6 +7,8 @@ use orion::operators::tensor::U32TensorPartialEq;
 use orion::operators::tensor::{TensorTrait, Tensor};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::U32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -14,7 +16,7 @@ fn test_sequence_erase_u32_empty() {
     let input_0 = input_0::input_0();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::None(()));
+    let y = SequenceTrait::sequence_erase(input_0, Option::None(()));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_u32_negative.cairo b/tests/nodes/sequence_erase_u32_negative.cairo
index 673724468..feab9c1ee 100644
--- a/tests/nodes/sequence_erase_u32_negative.cairo
+++ b/tests/nodes/sequence_erase_u32_negative.cairo
@@ -10,6 +10,8 @@ use orion::operators::tensor::{TensorTrait, Tensor};
 use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::U32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_erase_u32_negative() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::Some(input_1));
+    let y = SequenceTrait::sequence_erase(input_0, Option::Some(input_1));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_erase_u32_positive.cairo b/tests/nodes/sequence_erase_u32_positive.cairo
index d5dc5f353..a742989c2 100644
--- a/tests/nodes/sequence_erase_u32_positive.cairo
+++ b/tests/nodes/sequence_erase_u32_positive.cairo
@@ -10,6 +10,8 @@ use orion::operators::tensor::{TensorTrait, Tensor};
 use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::sequence::U32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
@@ -18,7 +20,7 @@ fn test_sequence_erase_u32_positive() {
     let input_1 = input_1::input_1();
     let z = output_0::output_0();
 
-    let y = TensorTrait::sequence_erase(input_0, Option::Some(input_1));
+    let y = SequenceTrait::sequence_erase(input_0, Option::Some(input_1));
 
     assert_seq_eq(y, z);
 }
diff --git a/tests/nodes/sequence_insert_fp16x16.cairo b/tests/nodes/sequence_insert_fp16x16.cairo
index ca636990f..9f9fe2754 100644
--- a/tests/nodes/sequence_insert_fp16x16.cairo
+++ b/tests/nodes/sequence_insert_fp16x16.cairo
@@ -11,6 +11,8 @@ use orion::operators::tensor::FP16x16TensorPartialEq;
 use orion::operators::tensor::FP16x16Tensor;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::sequence::FP16x16Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/sequence_insert_fp8x23.cairo b/tests/nodes/sequence_insert_fp8x23.cairo
index be838402d..e18208548 100644
--- a/tests/nodes/sequence_insert_fp8x23.cairo
+++ b/tests/nodes/sequence_insert_fp8x23.cairo
@@ -11,6 +11,8 @@ use orion::operators::tensor::FP8x23TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::{TensorTrait, Tensor};
 use orion::operators::tensor::FP8x23Tensor;
+use orion::operators::sequence::FP8x23Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/sequence_insert_i32.cairo b/tests/nodes/sequence_insert_i32.cairo
index 0eac00d7b..f23a2ea7c 100644
--- a/tests/nodes/sequence_insert_i32.cairo
+++ b/tests/nodes/sequence_insert_i32.cairo
@@ -9,6 +9,8 @@ use orion::utils::{assert_eq, assert_seq_eq};
 use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::sequence::I32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/sequence_insert_i8.cairo b/tests/nodes/sequence_insert_i8.cairo
index 0d25a649b..604c39326 100644
--- a/tests/nodes/sequence_insert_i8.cairo
+++ b/tests/nodes/sequence_insert_i8.cairo
@@ -11,6 +11,8 @@ use orion::operators::tensor::I32TensorPartialEq;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I8TensorPartialEq;
 use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::sequence::I8Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/sequence_insert_u32.cairo b/tests/nodes/sequence_insert_u32.cairo
index 0a8a55f18..8791e81d6 100644
--- a/tests/nodes/sequence_insert_u32.cairo
+++ b/tests/nodes/sequence_insert_u32.cairo
@@ -11,6 +11,8 @@ use orion::operators::tensor::U32Tensor;
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::{TensorTrait, Tensor};
 use orion::operators::tensor::U32TensorPartialEq;
+use orion::operators::sequence::U32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/sequence_length_fp16x16.cairo b/tests/nodes/sequence_length_fp16x16.cairo
index 5d39a1df6..b43f3494b 100644
--- a/tests/nodes/sequence_length_fp16x16.cairo
+++ b/tests/nodes/sequence_length_fp16x16.cairo
@@ -9,6 +9,8 @@ use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::FP16x16Tensor;
 use orion::operators::tensor::U32Tensor;
 use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::operators::sequence::FP16x16Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/sequence_length_fp16x16_broadcast.cairo b/tests/nodes/sequence_length_fp16x16_broadcast.cairo
index 0b7bba50b..f8c59e131 100644
--- a/tests/nodes/sequence_length_fp16x16_broadcast.cairo
+++ b/tests/nodes/sequence_length_fp16x16_broadcast.cairo
@@ -9,6 +9,8 @@ use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::FP16x16Tensor;
 use orion::operators::tensor::U32Tensor;
 use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::operators::sequence::FP16x16Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/sequence_length_fp8x23.cairo b/tests/nodes/sequence_length_fp8x23.cairo
index 64c3291c9..cbe83fbc7 100644
--- a/tests/nodes/sequence_length_fp8x23.cairo
+++ b/tests/nodes/sequence_length_fp8x23.cairo
@@ -9,6 +9,8 @@ use orion::operators::tensor::U32TensorPartialEq;
 use orion::utils::{assert_eq, assert_seq_eq};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::U32Tensor;
+use orion::operators::sequence::FP8x23Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/sequence_length_fp8x23_broadcast.cairo b/tests/nodes/sequence_length_fp8x23_broadcast.cairo
index f61131b65..4c78e99bb 100644
--- a/tests/nodes/sequence_length_fp8x23_broadcast.cairo
+++ b/tests/nodes/sequence_length_fp8x23_broadcast.cairo
@@ -9,6 +9,8 @@ use orion::operators::tensor::U32TensorPartialEq;
 use orion::utils::{assert_eq, assert_seq_eq};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::U32Tensor;
+use orion::operators::sequence::FP8x23Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/sequence_length_i32.cairo b/tests/nodes/sequence_length_i32.cairo
index 412e23dbd..566c70e9a 100644
--- a/tests/nodes/sequence_length_i32.cairo
+++ b/tests/nodes/sequence_length_i32.cairo
@@ -9,6 +9,8 @@ use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I32Tensor;
 use orion::operators::tensor::I32TensorPartialEq;
 use orion::operators::tensor::U32Tensor;
+use orion::operators::sequence::I32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/sequence_length_i32_broadcast.cairo b/tests/nodes/sequence_length_i32_broadcast.cairo
index d773749eb..89ae3fad8 100644
--- a/tests/nodes/sequence_length_i32_broadcast.cairo
+++ b/tests/nodes/sequence_length_i32_broadcast.cairo
@@ -9,6 +9,8 @@ use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::I32Tensor;
 use orion::operators::tensor::I32TensorPartialEq;
 use orion::operators::tensor::U32Tensor;
+use orion::operators::sequence::I32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/sequence_length_i8.cairo b/tests/nodes/sequence_length_i8.cairo
index 3aba0cfb4..3de36247b 100644
--- a/tests/nodes/sequence_length_i8.cairo
+++ b/tests/nodes/sequence_length_i8.cairo
@@ -9,6 +9,8 @@ use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::U32Tensor;
 use orion::operators::tensor::I8TensorPartialEq;
 use orion::operators::tensor::I8Tensor;
+use orion::operators::sequence::I8Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/sequence_length_i8_broadcast.cairo b/tests/nodes/sequence_length_i8_broadcast.cairo
index f8af9b5cf..03b2a9d90 100644
--- a/tests/nodes/sequence_length_i8_broadcast.cairo
+++ b/tests/nodes/sequence_length_i8_broadcast.cairo
@@ -9,6 +9,8 @@ use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::U32Tensor;
 use orion::operators::tensor::I8TensorPartialEq;
 use orion::operators::tensor::I8Tensor;
+use orion::operators::sequence::I8Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/sequence_length_u32.cairo b/tests/nodes/sequence_length_u32.cairo
index edf9dced9..caf367c71 100644
--- a/tests/nodes/sequence_length_u32.cairo
+++ b/tests/nodes/sequence_length_u32.cairo
@@ -7,6 +7,8 @@ use orion::operators::tensor::U32TensorPartialEq;
 use orion::utils::{assert_eq, assert_seq_eq};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::U32Tensor;
+use orion::operators::sequence::U32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/sequence_length_u32_broadcast.cairo b/tests/nodes/sequence_length_u32_broadcast.cairo
index b5432cfaf..67b26c272 100644
--- a/tests/nodes/sequence_length_u32_broadcast.cairo
+++ b/tests/nodes/sequence_length_u32_broadcast.cairo
@@ -7,6 +7,8 @@ use orion::operators::tensor::U32TensorPartialEq;
 use orion::utils::{assert_eq, assert_seq_eq};
 use core::array::{ArrayTrait, SpanTrait};
 use orion::operators::tensor::U32Tensor;
+use orion::operators::sequence::U32Sequence;
+use orion::operators::sequence::SequenceTrait;
 
 #[test]
 #[available_gas(2000000000)]
diff --git a/tests/nodes/softmax_zero_fp16x16.cairo b/tests/nodes/softmax_zero_fp16x16.cairo
new file mode 100644
index 000000000..41e229944
--- /dev/null
+++ b/tests/nodes/softmax_zero_fp16x16.cairo
@@ -0,0 +1,20 @@
+mod input_0;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::numbers::FixedTrait;
+use orion::operators::nn::FP16x16NN;
+use orion::operators::nn::NNTrait;
+use orion::operators::tensor::FP16x16TensorPartialEq;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_softmax_zero_fp16x16() {
+    let input_0 = input_0::input_0();
+    let z_0 = output_0::output_0();
+
+    let y_0 = NNTrait::softmax_zero(@input_0, 1);
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/softmax_zero_fp16x16/input_0.cairo b/tests/nodes/softmax_zero_fp16x16/input_0.cairo
new file mode 100644
index 000000000..fb0512473
--- /dev/null
+++ b/tests/nodes/softmax_zero_fp16x16/input_0.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 2278, sign: true });
+    data.append(FP16x16 { mag: 76483, sign: false });
+    data.append(FP16x16 { mag: 23998, sign: false });
+    data.append(FP16x16 { mag: 808, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/softmax_zero_fp16x16/output_0.cairo b/tests/nodes/softmax_zero_fp16x16/output_0.cairo
new file mode 100644
index 000000000..f72d7363c
--- /dev/null
+++ b/tests/nodes/softmax_zero_fp16x16/output_0.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 15148, sign: false });
+    data.append(FP16x16 { mag: 50387, sign: false });
+    data.append(FP16x16 { mag: 38896, sign: false });
+    data.append(FP16x16 { mag: 26639, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/softmax_zero_fp8x23.cairo b/tests/nodes/softmax_zero_fp8x23.cairo
new file mode 100644
index 000000000..88e2eb3a1
--- /dev/null
+++ b/tests/nodes/softmax_zero_fp8x23.cairo
@@ -0,0 +1,20 @@
+mod input_0;
+mod output_0;
+
+
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::numbers::FixedTrait;
+use orion::operators::tensor::FP8x23TensorPartialEq;
+use orion::operators::nn::FP8x23NN;
+use orion::operators::nn::NNTrait;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_softmax_zero_fp8x23() {
+    let input_0 = input_0::input_0();
+    let z_0 = output_0::output_0();
+
+    let y_0 = NNTrait::softmax_zero(@input_0, 1);
+
+    assert_eq(y_0, z_0);
+}
diff --git a/tests/nodes/softmax_zero_fp8x23/input_0.cairo b/tests/nodes/softmax_zero_fp8x23/input_0.cairo
new file mode 100644
index 000000000..c74e57ae0
--- /dev/null
+++ b/tests/nodes/softmax_zero_fp8x23/input_0.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn input_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 21210813, sign: true });
+    data.append(FP8x23 { mag: 18026313, sign: true });
+    data.append(FP8x23 { mag: 11180685, sign: false });
+    data.append(FP8x23 { mag: 9192264, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/softmax_zero_fp8x23/output_0.cairo b/tests/nodes/softmax_zero_fp8x23/output_0.cairo
new file mode 100644
index 000000000..5a73df92d
--- /dev/null
+++ b/tests/nodes/softmax_zero_fp8x23/output_0.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP8x23Tensor;
+use orion::numbers::{FixedTrait, FP8x23};
+
+fn output_0() -> Tensor<FP8x23> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP8x23 { mag: 3407604, sign: false });
+    data.append(FP8x23 { mag: 4981003, sign: false });
+    data.append(FP8x23 { mag: 4689094, sign: false });
+    data.append(FP8x23 { mag: 3699513, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_fp16x16_1d_equal_parts.cairo b/tests/nodes/split_fp16x16_1d_equal_parts.cairo
new file mode 100644
index 000000000..03b36c507
--- /dev/null
+++ b/tests/nodes/split_fp16x16_1d_equal_parts.cairo
@@ -0,0 +1,20 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_split_fp16x16_1d_equal_parts() {
+    let input_0 = input_0::input_0();
+    let z = output_0::output_0();
+
+    let y = input_0.split(0, Option::Some(3), Option::None(()));
+
+    assert_seq_eq(y, z);
+}
diff --git a/tests/nodes/split_fp16x16_1d_equal_parts/input_0.cairo b/tests/nodes/split_fp16x16_1d_equal_parts/input_0.cairo
new file mode 100644
index 000000000..c5859b6f8
--- /dev/null
+++ b/tests/nodes/split_fp16x16_1d_equal_parts/input_0.cairo
@@ -0,0 +1,18 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(6);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 1966080, sign: false });
+    data.append(FP16x16 { mag: 8257536, sign: false });
+    data.append(FP16x16 { mag: 7471104, sign: true });
+    data.append(FP16x16 { mag: 4849664, sign: true });
+    data.append(FP16x16 { mag: 3407872, sign: false });
+    data.append(FP16x16 { mag: 3014656, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_fp16x16_1d_equal_parts/output_0.cairo b/tests/nodes/split_fp16x16_1d_equal_parts/output_0.cairo
new file mode 100644
index 000000000..43cf3eb64
--- /dev/null
+++ b/tests/nodes/split_fp16x16_1d_equal_parts/output_0.cairo
@@ -0,0 +1,37 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Array<Tensor<FP16x16>> {
+    let mut sequence = ArrayTrait::new();
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 1966080, sign: false });
+    data.append(FP16x16 { mag: 8257536, sign: false });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 7471104, sign: true });
+    data.append(FP16x16 { mag: 4849664, sign: true });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 3407872, sign: false });
+    data.append(FP16x16 { mag: 3014656, sign: false });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    sequence
+}
diff --git a/tests/nodes/split_fp16x16_1d_uneven.cairo b/tests/nodes/split_fp16x16_1d_uneven.cairo
new file mode 100644
index 000000000..e0221f924
--- /dev/null
+++ b/tests/nodes/split_fp16x16_1d_uneven.cairo
@@ -0,0 +1,20 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_split_fp16x16_1d_uneven() {
+    let input_0 = input_0::input_0();
+    let z = output_0::output_0();
+
+    let y = input_0.split(0, Option::Some(4), Option::None(()));
+
+    assert_seq_eq(y, z);
+}
diff --git a/tests/nodes/split_fp16x16_1d_uneven/input_0.cairo b/tests/nodes/split_fp16x16_1d_uneven/input_0.cairo
new file mode 100644
index 000000000..8426e2fe9
--- /dev/null
+++ b/tests/nodes/split_fp16x16_1d_uneven/input_0.cairo
@@ -0,0 +1,19 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(7);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 5701632, sign: true });
+    data.append(FP16x16 { mag: 6946816, sign: false });
+    data.append(FP16x16 { mag: 2883584, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+    data.append(FP16x16 { mag: 2883584, sign: true });
+    data.append(FP16x16 { mag: 6160384, sign: false });
+    data.append(FP16x16 { mag: 8257536, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_fp16x16_1d_uneven/output_0.cairo b/tests/nodes/split_fp16x16_1d_uneven/output_0.cairo
new file mode 100644
index 000000000..b5c8384ab
--- /dev/null
+++ b/tests/nodes/split_fp16x16_1d_uneven/output_0.cairo
@@ -0,0 +1,45 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Array<Tensor<FP16x16>> {
+    let mut sequence = ArrayTrait::new();
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 5701632, sign: true });
+    data.append(FP16x16 { mag: 6946816, sign: false });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 2883584, sign: false });
+    data.append(FP16x16 { mag: 131072, sign: false });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 2883584, sign: true });
+    data.append(FP16x16 { mag: 6160384, sign: false });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 8257536, sign: true });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    sequence
+}
diff --git a/tests/nodes/split_fp16x16_1d_variable_parts.cairo b/tests/nodes/split_fp16x16_1d_variable_parts.cairo
new file mode 100644
index 000000000..888e8ef2e
--- /dev/null
+++ b/tests/nodes/split_fp16x16_1d_variable_parts.cairo
@@ -0,0 +1,28 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_split_fp16x16_1d_variable_parts() {
+    let input_0 = input_0::input_0();
+    let z = output_0::output_0();
+
+    let y = input_0
+        .split(
+            0,
+            Option::None(()),
+            Option::Some(
+                TensorTrait::<u32>::new(shape: array![2].span(), data: array![2, 4].span(),)
+            )
+        );
+
+    assert_seq_eq(y, z);
+}
diff --git a/tests/nodes/split_fp16x16_1d_variable_parts/input_0.cairo b/tests/nodes/split_fp16x16_1d_variable_parts/input_0.cairo
new file mode 100644
index 000000000..c5859b6f8
--- /dev/null
+++ b/tests/nodes/split_fp16x16_1d_variable_parts/input_0.cairo
@@ -0,0 +1,18 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(6);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 1966080, sign: false });
+    data.append(FP16x16 { mag: 8257536, sign: false });
+    data.append(FP16x16 { mag: 7471104, sign: true });
+    data.append(FP16x16 { mag: 4849664, sign: true });
+    data.append(FP16x16 { mag: 3407872, sign: false });
+    data.append(FP16x16 { mag: 3014656, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_fp16x16_1d_variable_parts/output_0.cairo b/tests/nodes/split_fp16x16_1d_variable_parts/output_0.cairo
new file mode 100644
index 000000000..49474c9ff
--- /dev/null
+++ b/tests/nodes/split_fp16x16_1d_variable_parts/output_0.cairo
@@ -0,0 +1,30 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Array<Tensor<FP16x16>> {
+    let mut sequence = ArrayTrait::new();
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 1966080, sign: false });
+    data.append(FP16x16 { mag: 8257536, sign: false });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 7471104, sign: true });
+    data.append(FP16x16 { mag: 4849664, sign: true });
+    data.append(FP16x16 { mag: 3407872, sign: false });
+    data.append(FP16x16 { mag: 3014656, sign: false });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    sequence
+}
diff --git a/tests/nodes/split_fp16x16_2d_equal_parts.cairo b/tests/nodes/split_fp16x16_2d_equal_parts.cairo
new file mode 100644
index 000000000..a1c441b60
--- /dev/null
+++ b/tests/nodes/split_fp16x16_2d_equal_parts.cairo
@@ -0,0 +1,20 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_split_fp16x16_2d_equal_parts() {
+    let input_0 = input_0::input_0();
+    let z = output_0::output_0();
+
+    let y = input_0.split(1, Option::Some(2), Option::None(()));
+
+    assert_seq_eq(y, z);
+}
diff --git a/tests/nodes/split_fp16x16_2d_equal_parts/input_0.cairo b/tests/nodes/split_fp16x16_2d_equal_parts/input_0.cairo
new file mode 100644
index 000000000..f9e723397
--- /dev/null
+++ b/tests/nodes/split_fp16x16_2d_equal_parts/input_0.cairo
@@ -0,0 +1,25 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(6);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 7274496, sign: true });
+    data.append(FP16x16 { mag: 1507328, sign: true });
+    data.append(FP16x16 { mag: 3604480, sign: false });
+    data.append(FP16x16 { mag: 7864320, sign: true });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 6422528, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: true });
+    data.append(FP16x16 { mag: 3538944, sign: true });
+    data.append(FP16x16 { mag: 1835008, sign: false });
+    data.append(FP16x16 { mag: 3407872, sign: false });
+    data.append(FP16x16 { mag: 3145728, sign: false });
+    data.append(FP16x16 { mag: 8257536, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_fp16x16_2d_equal_parts/output_0.cairo b/tests/nodes/split_fp16x16_2d_equal_parts/output_0.cairo
new file mode 100644
index 000000000..cc8491e5d
--- /dev/null
+++ b/tests/nodes/split_fp16x16_2d_equal_parts/output_0.cairo
@@ -0,0 +1,38 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Array<Tensor<FP16x16>> {
+    let mut sequence = ArrayTrait::new();
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 7274496, sign: true });
+    data.append(FP16x16 { mag: 1507328, sign: true });
+    data.append(FP16x16 { mag: 3604480, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: true });
+    data.append(FP16x16 { mag: 3538944, sign: true });
+    data.append(FP16x16 { mag: 1835008, sign: false });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 7864320, sign: true });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 6422528, sign: false });
+    data.append(FP16x16 { mag: 3407872, sign: false });
+    data.append(FP16x16 { mag: 3145728, sign: false });
+    data.append(FP16x16 { mag: 8257536, sign: true });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    sequence
+}
diff --git a/tests/nodes/split_fp16x16_2d_uneven.cairo b/tests/nodes/split_fp16x16_2d_uneven.cairo
new file mode 100644
index 000000000..fbf5c12d7
--- /dev/null
+++ b/tests/nodes/split_fp16x16_2d_uneven.cairo
@@ -0,0 +1,20 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_split_fp16x16_2d_uneven() {
+    let input_0 = input_0::input_0();
+    let z = output_0::output_0();
+
+    let y = input_0.split(1, Option::Some(3), Option::None(()));
+
+    assert_seq_eq(y, z);
+}
diff --git a/tests/nodes/split_fp16x16_2d_uneven/input_0.cairo b/tests/nodes/split_fp16x16_2d_uneven/input_0.cairo
new file mode 100644
index 000000000..341bfc1af
--- /dev/null
+++ b/tests/nodes/split_fp16x16_2d_uneven/input_0.cairo
@@ -0,0 +1,29 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 4980736, sign: false });
+    data.append(FP16x16 { mag: 6750208, sign: true });
+    data.append(FP16x16 { mag: 6488064, sign: true });
+    data.append(FP16x16 { mag: 2490368, sign: false });
+    data.append(FP16x16 { mag: 1245184, sign: true });
+    data.append(FP16x16 { mag: 1310720, sign: false });
+    data.append(FP16x16 { mag: 2686976, sign: true });
+    data.append(FP16x16 { mag: 3801088, sign: false });
+    data.append(FP16x16 { mag: 4849664, sign: false });
+    data.append(FP16x16 { mag: 3538944, sign: true });
+    data.append(FP16x16 { mag: 7077888, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: true });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 8192000, sign: false });
+    data.append(FP16x16 { mag: 1441792, sign: true });
+    data.append(FP16x16 { mag: 6553600, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_fp16x16_2d_uneven/output_0.cairo b/tests/nodes/split_fp16x16_2d_uneven/output_0.cairo
new file mode 100644
index 000000000..fb660f51c
--- /dev/null
+++ b/tests/nodes/split_fp16x16_2d_uneven/output_0.cairo
@@ -0,0 +1,50 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Array<Tensor<FP16x16>> {
+    let mut sequence = ArrayTrait::new();
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 4980736, sign: false });
+    data.append(FP16x16 { mag: 6750208, sign: true });
+    data.append(FP16x16 { mag: 6488064, sign: true });
+    data.append(FP16x16 { mag: 4849664, sign: false });
+    data.append(FP16x16 { mag: 3538944, sign: true });
+    data.append(FP16x16 { mag: 7077888, sign: false });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 2490368, sign: false });
+    data.append(FP16x16 { mag: 1245184, sign: true });
+    data.append(FP16x16 { mag: 1310720, sign: false });
+    data.append(FP16x16 { mag: 262144, sign: true });
+    data.append(FP16x16 { mag: 262144, sign: false });
+    data.append(FP16x16 { mag: 8192000, sign: false });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 2686976, sign: true });
+    data.append(FP16x16 { mag: 3801088, sign: false });
+    data.append(FP16x16 { mag: 1441792, sign: true });
+    data.append(FP16x16 { mag: 6553600, sign: true });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    sequence
+}
diff --git a/tests/nodes/split_fp16x16_2d_variable_parts.cairo b/tests/nodes/split_fp16x16_2d_variable_parts.cairo
new file mode 100644
index 000000000..ab6090f5c
--- /dev/null
+++ b/tests/nodes/split_fp16x16_2d_variable_parts.cairo
@@ -0,0 +1,28 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_split_fp16x16_2d_variable_parts() {
+    let input_0 = input_0::input_0();
+    let z = output_0::output_0();
+
+    let y = input_0
+        .split(
+            1,
+            Option::None(()),
+            Option::Some(
+                TensorTrait::<u32>::new(shape: array![2].span(), data: array![2, 4].span(),)
+            )
+        );
+
+    assert_seq_eq(y, z);
+}
diff --git a/tests/nodes/split_fp16x16_2d_variable_parts/input_0.cairo b/tests/nodes/split_fp16x16_2d_variable_parts/input_0.cairo
new file mode 100644
index 000000000..f9e723397
--- /dev/null
+++ b/tests/nodes/split_fp16x16_2d_variable_parts/input_0.cairo
@@ -0,0 +1,25 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(6);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 7274496, sign: true });
+    data.append(FP16x16 { mag: 1507328, sign: true });
+    data.append(FP16x16 { mag: 3604480, sign: false });
+    data.append(FP16x16 { mag: 7864320, sign: true });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 6422528, sign: false });
+    data.append(FP16x16 { mag: 917504, sign: true });
+    data.append(FP16x16 { mag: 3538944, sign: true });
+    data.append(FP16x16 { mag: 1835008, sign: false });
+    data.append(FP16x16 { mag: 3407872, sign: false });
+    data.append(FP16x16 { mag: 3145728, sign: false });
+    data.append(FP16x16 { mag: 8257536, sign: true });
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_fp16x16_2d_variable_parts/output_0.cairo b/tests/nodes/split_fp16x16_2d_variable_parts/output_0.cairo
new file mode 100644
index 000000000..3c7c5282f
--- /dev/null
+++ b/tests/nodes/split_fp16x16_2d_variable_parts/output_0.cairo
@@ -0,0 +1,38 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Array<Tensor<FP16x16>> {
+    let mut sequence = ArrayTrait::new();
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 7274496, sign: true });
+    data.append(FP16x16 { mag: 1507328, sign: true });
+    data.append(FP16x16 { mag: 917504, sign: true });
+    data.append(FP16x16 { mag: 3538944, sign: true });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 3604480, sign: false });
+    data.append(FP16x16 { mag: 7864320, sign: true });
+    data.append(FP16x16 { mag: 786432, sign: false });
+    data.append(FP16x16 { mag: 6422528, sign: false });
+    data.append(FP16x16 { mag: 1835008, sign: false });
+    data.append(FP16x16 { mag: 3407872, sign: false });
+    data.append(FP16x16 { mag: 3145728, sign: false });
+    data.append(FP16x16 { mag: 8257536, sign: true });
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    sequence
+}
diff --git a/tests/nodes/split_fp16x16_zero_size.cairo b/tests/nodes/split_fp16x16_zero_size.cairo
new file mode 100644
index 000000000..a0749c925
--- /dev/null
+++ b/tests/nodes/split_fp16x16_zero_size.cairo
@@ -0,0 +1,28 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use orion::operators::tensor::FP16x16Tensor;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+#[test]
+#[available_gas(2000000000)]
+fn test_split_fp16x16_zero_size() {
+    let input_0 = input_0::input_0();
+    let z = output_0::output_0();
+
+    let y = input_0
+        .split(
+            0,
+            Option::None(()),
+            Option::Some(
+                TensorTrait::<u32>::new(shape: array![3].span(), data: array![0, 0, 0].span(),)
+            )
+        );
+
+    assert_seq_eq(y, z);
+}
diff --git a/tests/nodes/split_fp16x16_zero_size/input_0.cairo b/tests/nodes/split_fp16x16_zero_size/input_0.cairo
new file mode 100644
index 000000000..99f4ba73b
--- /dev/null
+++ b/tests/nodes/split_fp16x16_zero_size/input_0.cairo
@@ -0,0 +1,12 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn input_0() -> Tensor<FP16x16> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(0);
+
+    let mut data = ArrayTrait::new();
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_fp16x16_zero_size/output_0.cairo b/tests/nodes/split_fp16x16_zero_size/output_0.cairo
new file mode 100644
index 000000000..09f338786
--- /dev/null
+++ b/tests/nodes/split_fp16x16_zero_size/output_0.cairo
@@ -0,0 +1,31 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn output_0() -> Array<Tensor<FP16x16>> {
+    let mut sequence = ArrayTrait::new();
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(0);
+
+    let mut data = ArrayTrait::new();
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(0);
+
+    let mut data = ArrayTrait::new();
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(0);
+
+    let mut data = ArrayTrait::new();
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    sequence
+}
diff --git a/tests/nodes/split_u32_1d_equal_parts.cairo b/tests/nodes/split_u32_1d_equal_parts.cairo
new file mode 100644
index 000000000..d4530bac9
--- /dev/null
+++ b/tests/nodes/split_u32_1d_equal_parts.cairo
@@ -0,0 +1,20 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_split_u32_1d_equal_parts() {
+    let input_0 = input_0::input_0();
+    let z = output_0::output_0();
+
+    let y = input_0.split(0, Option::Some(3), Option::None(()));
+
+    assert_seq_eq(y, z);
+}
diff --git a/tests/nodes/split_u32_1d_equal_parts/input_0.cairo b/tests/nodes/split_u32_1d_equal_parts/input_0.cairo
new file mode 100644
index 000000000..7cb4b0bef
--- /dev/null
+++ b/tests/nodes/split_u32_1d_equal_parts/input_0.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(6);
+
+    let mut data = ArrayTrait::new();
+    data.append(191);
+    data.append(83);
+    data.append(144);
+    data.append(69);
+    data.append(77);
+    data.append(34);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_u32_1d_equal_parts/output_0.cairo b/tests/nodes/split_u32_1d_equal_parts/output_0.cairo
new file mode 100644
index 000000000..65c19f5ff
--- /dev/null
+++ b/tests/nodes/split_u32_1d_equal_parts/output_0.cairo
@@ -0,0 +1,36 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn output_0() -> Array<Tensor<u32>> {
+    let mut sequence = ArrayTrait::new();
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(191);
+    data.append(83);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(144);
+    data.append(69);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(77);
+    data.append(34);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    sequence
+}
diff --git a/tests/nodes/split_u32_1d_uneven.cairo b/tests/nodes/split_u32_1d_uneven.cairo
new file mode 100644
index 000000000..a4180af74
--- /dev/null
+++ b/tests/nodes/split_u32_1d_uneven.cairo
@@ -0,0 +1,20 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_split_u32_1d_uneven() {
+    let input_0 = input_0::input_0();
+    let z = output_0::output_0();
+
+    let y = input_0.split(0, Option::Some(4), Option::None(()));
+
+    assert_seq_eq(y, z);
+}
diff --git a/tests/nodes/split_u32_1d_uneven/input_0.cairo b/tests/nodes/split_u32_1d_uneven/input_0.cairo
new file mode 100644
index 000000000..9ef03f0fe
--- /dev/null
+++ b/tests/nodes/split_u32_1d_uneven/input_0.cairo
@@ -0,0 +1,18 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(7);
+
+    let mut data = ArrayTrait::new();
+    data.append(203);
+    data.append(159);
+    data.append(108);
+    data.append(166);
+    data.append(98);
+    data.append(220);
+    data.append(233);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_u32_1d_uneven/output_0.cairo b/tests/nodes/split_u32_1d_uneven/output_0.cairo
new file mode 100644
index 000000000..c39b4bc14
--- /dev/null
+++ b/tests/nodes/split_u32_1d_uneven/output_0.cairo
@@ -0,0 +1,44 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn output_0() -> Array<Tensor<u32>> {
+    let mut sequence = ArrayTrait::new();
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(203);
+    data.append(159);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(108);
+    data.append(166);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(98);
+    data.append(220);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(1);
+
+    let mut data = ArrayTrait::new();
+    data.append(233);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    sequence
+}
diff --git a/tests/nodes/split_u32_1d_variable_parts.cairo b/tests/nodes/split_u32_1d_variable_parts.cairo
new file mode 100644
index 000000000..3e4c7facd
--- /dev/null
+++ b/tests/nodes/split_u32_1d_variable_parts.cairo
@@ -0,0 +1,27 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_split_u32_1d_variable_parts() {
+    let input_0 = input_0::input_0();
+    let z = output_0::output_0();
+
+    let y = input_0
+        .split(
+            0,
+            Option::None(()),
+            Option::Some(
+                TensorTrait::<u32>::new(shape: array![2].span(), data: array![2, 4].span(),)
+            )
+        );
+
+    assert_seq_eq(y, z);
+}
diff --git a/tests/nodes/split_u32_1d_variable_parts/input_0.cairo b/tests/nodes/split_u32_1d_variable_parts/input_0.cairo
new file mode 100644
index 000000000..7cb4b0bef
--- /dev/null
+++ b/tests/nodes/split_u32_1d_variable_parts/input_0.cairo
@@ -0,0 +1,17 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(6);
+
+    let mut data = ArrayTrait::new();
+    data.append(191);
+    data.append(83);
+    data.append(144);
+    data.append(69);
+    data.append(77);
+    data.append(34);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_u32_1d_variable_parts/output_0.cairo b/tests/nodes/split_u32_1d_variable_parts/output_0.cairo
new file mode 100644
index 000000000..9407802e9
--- /dev/null
+++ b/tests/nodes/split_u32_1d_variable_parts/output_0.cairo
@@ -0,0 +1,29 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn output_0() -> Array<Tensor<u32>> {
+    let mut sequence = ArrayTrait::new();
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(191);
+    data.append(83);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(144);
+    data.append(69);
+    data.append(77);
+    data.append(34);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    sequence
+}
diff --git a/tests/nodes/split_u32_2d_equal_parts.cairo b/tests/nodes/split_u32_2d_equal_parts.cairo
new file mode 100644
index 000000000..d5fa6d43a
--- /dev/null
+++ b/tests/nodes/split_u32_2d_equal_parts.cairo
@@ -0,0 +1,20 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_split_u32_2d_equal_parts() {
+    let input_0 = input_0::input_0();
+    let z = output_0::output_0();
+
+    let y = input_0.split(1, Option::Some(2), Option::None(()));
+
+    assert_seq_eq(y, z);
+}
diff --git a/tests/nodes/split_u32_2d_equal_parts/input_0.cairo b/tests/nodes/split_u32_2d_equal_parts/input_0.cairo
new file mode 100644
index 000000000..4da0f4c2f
--- /dev/null
+++ b/tests/nodes/split_u32_2d_equal_parts/input_0.cairo
@@ -0,0 +1,24 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(6);
+
+    let mut data = ArrayTrait::new();
+    data.append(189);
+    data.append(74);
+    data.append(230);
+    data.append(245);
+    data.append(231);
+    data.append(162);
+    data.append(11);
+    data.append(159);
+    data.append(108);
+    data.append(92);
+    data.append(6);
+    data.append(61);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_u32_2d_equal_parts/output_0.cairo b/tests/nodes/split_u32_2d_equal_parts/output_0.cairo
new file mode 100644
index 000000000..7abbf7096
--- /dev/null
+++ b/tests/nodes/split_u32_2d_equal_parts/output_0.cairo
@@ -0,0 +1,37 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn output_0() -> Array<Tensor<u32>> {
+    let mut sequence = ArrayTrait::new();
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(189);
+    data.append(74);
+    data.append(230);
+    data.append(11);
+    data.append(159);
+    data.append(108);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(245);
+    data.append(231);
+    data.append(162);
+    data.append(92);
+    data.append(6);
+    data.append(61);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    sequence
+}
diff --git a/tests/nodes/split_u32_2d_uneven.cairo b/tests/nodes/split_u32_2d_uneven.cairo
new file mode 100644
index 000000000..f663cc95f
--- /dev/null
+++ b/tests/nodes/split_u32_2d_uneven.cairo
@@ -0,0 +1,20 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_split_u32_2d_uneven() {
+    let input_0 = input_0::input_0();
+    let z = output_0::output_0();
+
+    let y = input_0.split(1, Option::Some(3), Option::None(()));
+
+    assert_seq_eq(y, z);
+}
diff --git a/tests/nodes/split_u32_2d_uneven/input_0.cairo b/tests/nodes/split_u32_2d_uneven/input_0.cairo
new file mode 100644
index 000000000..c6f1e2857
--- /dev/null
+++ b/tests/nodes/split_u32_2d_uneven/input_0.cairo
@@ -0,0 +1,28 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(8);
+
+    let mut data = ArrayTrait::new();
+    data.append(114);
+    data.append(94);
+    data.append(130);
+    data.append(214);
+    data.append(213);
+    data.append(226);
+    data.append(218);
+    data.append(47);
+    data.append(173);
+    data.append(181);
+    data.append(108);
+    data.append(140);
+    data.append(123);
+    data.append(14);
+    data.append(181);
+    data.append(7);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_u32_2d_uneven/output_0.cairo b/tests/nodes/split_u32_2d_uneven/output_0.cairo
new file mode 100644
index 000000000..8fdbd5b48
--- /dev/null
+++ b/tests/nodes/split_u32_2d_uneven/output_0.cairo
@@ -0,0 +1,49 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn output_0() -> Array<Tensor<u32>> {
+    let mut sequence = ArrayTrait::new();
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(114);
+    data.append(94);
+    data.append(130);
+    data.append(173);
+    data.append(181);
+    data.append(108);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(3);
+
+    let mut data = ArrayTrait::new();
+    data.append(214);
+    data.append(213);
+    data.append(226);
+    data.append(140);
+    data.append(123);
+    data.append(14);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(218);
+    data.append(47);
+    data.append(181);
+    data.append(7);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    sequence
+}
diff --git a/tests/nodes/split_u32_2d_variable_parts.cairo b/tests/nodes/split_u32_2d_variable_parts.cairo
new file mode 100644
index 000000000..f591103a2
--- /dev/null
+++ b/tests/nodes/split_u32_2d_variable_parts.cairo
@@ -0,0 +1,27 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_split_u32_2d_variable_parts() {
+    let input_0 = input_0::input_0();
+    let z = output_0::output_0();
+
+    let y = input_0
+        .split(
+            1,
+            Option::None(()),
+            Option::Some(
+                TensorTrait::<u32>::new(shape: array![2].span(), data: array![2, 4].span(),)
+            )
+        );
+
+    assert_seq_eq(y, z);
+}
diff --git a/tests/nodes/split_u32_2d_variable_parts/input_0.cairo b/tests/nodes/split_u32_2d_variable_parts/input_0.cairo
new file mode 100644
index 000000000..4da0f4c2f
--- /dev/null
+++ b/tests/nodes/split_u32_2d_variable_parts/input_0.cairo
@@ -0,0 +1,24 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(6);
+
+    let mut data = ArrayTrait::new();
+    data.append(189);
+    data.append(74);
+    data.append(230);
+    data.append(245);
+    data.append(231);
+    data.append(162);
+    data.append(11);
+    data.append(159);
+    data.append(108);
+    data.append(92);
+    data.append(6);
+    data.append(61);
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_u32_2d_variable_parts/output_0.cairo b/tests/nodes/split_u32_2d_variable_parts/output_0.cairo
new file mode 100644
index 000000000..8e835404a
--- /dev/null
+++ b/tests/nodes/split_u32_2d_variable_parts/output_0.cairo
@@ -0,0 +1,37 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn output_0() -> Array<Tensor<u32>> {
+    let mut sequence = ArrayTrait::new();
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(189);
+    data.append(74);
+    data.append(11);
+    data.append(159);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(2);
+    shape.append(4);
+
+    let mut data = ArrayTrait::new();
+    data.append(230);
+    data.append(245);
+    data.append(231);
+    data.append(162);
+    data.append(108);
+    data.append(92);
+    data.append(6);
+    data.append(61);
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    sequence
+}
diff --git a/tests/nodes/split_u32_zero_size.cairo b/tests/nodes/split_u32_zero_size.cairo
new file mode 100644
index 000000000..53c432c19
--- /dev/null
+++ b/tests/nodes/split_u32_zero_size.cairo
@@ -0,0 +1,27 @@
+mod input_0;
+mod output_0;
+
+
+use orion::operators::tensor::U32TensorPartialEq;
+use orion::utils::{assert_eq, assert_seq_eq};
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::U32Tensor;
+use orion::operators::tensor::{TensorTrait, Tensor};
+
+#[test]
+#[available_gas(2000000000)]
+fn test_split_u32_zero_size() {
+    let input_0 = input_0::input_0();
+    let z = output_0::output_0();
+
+    let y = input_0
+        .split(
+            0,
+            Option::None(()),
+            Option::Some(
+                TensorTrait::<u32>::new(shape: array![3].span(), data: array![0, 0, 0].span(),)
+            )
+        );
+
+    assert_seq_eq(y, z);
+}
diff --git a/tests/nodes/split_u32_zero_size/input_0.cairo b/tests/nodes/split_u32_zero_size/input_0.cairo
new file mode 100644
index 000000000..442c49919
--- /dev/null
+++ b/tests/nodes/split_u32_zero_size/input_0.cairo
@@ -0,0 +1,11 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn input_0() -> Tensor<u32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(0);
+
+    let mut data = ArrayTrait::new();
+    TensorTrait::new(shape.span(), data.span())
+}
diff --git a/tests/nodes/split_u32_zero_size/output_0.cairo b/tests/nodes/split_u32_zero_size/output_0.cairo
new file mode 100644
index 000000000..9ab25acaa
--- /dev/null
+++ b/tests/nodes/split_u32_zero_size/output_0.cairo
@@ -0,0 +1,30 @@
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::U32Tensor;
+
+fn output_0() -> Array<Tensor<u32>> {
+    let mut sequence = ArrayTrait::new();
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(0);
+
+    let mut data = ArrayTrait::new();
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(0);
+
+    let mut data = ArrayTrait::new();
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(0);
+
+    let mut data = ArrayTrait::new();
+
+    sequence.append(TensorTrait::new(shape.span(), data.span()));
+
+    sequence
+}
diff --git a/tests/operators/transpose_test.cairo b/tests/operators/transpose_test.cairo
index ffe4e663c..b4683162e 100644
--- a/tests/operators/transpose_test.cairo
+++ b/tests/operators/transpose_test.cairo
@@ -25,6 +25,16 @@ fn transpose_test_values() {
     assert(result.data == array![0, 2, 4, 6, 1, 3, 5, 7].span(), 'wrong data');
 }
 
+#[test]
+#[available_gas(200000000000)]
+fn transpose_test_1D() {
+    let tensor = TensorTrait::<u32>::new(shape: array![4].span(), data: array![0, 1, 2, 3].span(),);
+
+    let result = tensor.transpose(axes: array![0].span());
+
+    assert(result.shape == array![4].span(), 'wrong shape');
+    assert(result.data == array![0, 1, 2, 3].span(), 'wrong data');
+}
 
 #[test]
 #[available_gas(200000000000)]