[onert/python] Introduces loss API (#14542)

* [onert/python] Introduces loss API

This commit introduces loss API.
  - cce.py : Categorical CrossEntropy Loss
  - mse.py : Mean Squared Error Loss

ONE-DCO-1.0-Signed-off-by: ragmani <[email protected]>

* Remove __call__

runtime/onert/api/python/package/experimental/train/losses/__init__.py

@@ -0,0 +1,5 @@
+from .cce import CategoricalCrossentropy
+from .mse import MeanSquaredError
+from onert.native.libnnfw_api_pybind import lossinfo
+
+__all__ = ["CategoricalCrossentropy", "MeanSquaredError", "lossinfo", "loss"]

runtime/onert/api/python/package/experimental/train/losses/cce.py

@@ -0,0 +1,15 @@
+import numpy as np
+from .loss import LossFunction
+
+
+class CategoricalCrossentropy(LossFunction):
+    """
+    Categorical Cross-Entropy Loss Function with reduction type.
+    """
+    def __init__(self, reduction="mean"):
+        """
+        Initialize the Categorical Cross-Entropy loss function.
+        Args:
+            reduction (str): Reduction type ('mean', 'sum').
+        """
+        super().__init__(reduction)

runtime/onert/api/python/package/experimental/train/losses/loss.py

@@ -0,0 +1,24 @@
+from onert.native.libnnfw_api_pybind import loss_reduction
+
+
+class LossFunction:
+    """
+    Base class for loss functions with reduction type.
+    """
+    def __init__(self, reduction="mean"):
+        """
+        Initialize the Categorical Cross-Entropy loss function.
+        Args:
+            reduction (str): Reduction type ('mean', 'sum').
+        """
+        reduction_mapping = {
+            "mean": loss_reduction.SUM_OVER_BATCH_SIZE,
+            "sum": loss_reduction.SUM
+        }
+
+        # Validate and assign the reduction type
+        if reduction not in reduction_mapping:
+            raise ValueError(
+                f"Invalid reduction type. Choose from {list(reduction_mapping.keys())}.")
+
+        self.reduction = reduction_mapping[reduction]

runtime/onert/api/python/package/experimental/train/losses/mse.py

@@ -0,0 +1,15 @@
+import numpy as np
+from .loss import LossFunction
+
+
+class MeanSquaredError(LossFunction):
+    """
+    Mean Squared Error (MSE) Loss Function with reduction type.
+    """
+    def __init__(self, reduction="mean"):
+        """
+        Initialize the MSE loss function.
+        Args:
+            reduction (str): Reduction type ('mean', 'sum').
+        """
+        super().__init__(reduction)