update the docstring for HoVerNet and UpSample (#5818)

Signed-off-by: KumoLiu <[email protected]>

Fixes #5609.

### Description
Update the docstring for `HoVerNet` and `UpSample`.

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [x] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

Signed-off-by: KumoLiu <[email protected]>

monai/networks/blocks/upsample.py

@@ -31,6 +31,9 @@ class UpSample(nn.Sequential):
         - "nontrainable": uses :py:class:`torch.nn.Upsample`.
         - "pixelshuffle": uses :py:class:`monai.networks.blocks.SubpixelUpsample`.
 
+    This operation will cause non-deterministic when ``mode`` is ``UpsampleMode.NONTRAINABLE``.
+    Please check the link below for more details:
+    https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms
     This module can optionally take a pre-convolution
     (often used to map the number of features from `in_channels` to `out_channels`).
     """

monai/networks/nets/hovernet.py

@@ -416,6 +416,10 @@ class HoVerNet(nn.Module):
       https://github.com/vqdang/hover_net
       https://pytorch.org/vision/main/models/generated/torchvision.models.resnet50.html
 
+    This network is non-deterministic since it uses `torch.nn.Upsample` with ``UpsampleMode.NONTRAINABLE`` mode which
+    is implemented with torch.nn.functional.interpolate(). Please check the link below for more details:
+    https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms
+
     Args:
         mode: use original implementation (`HoVerNetMODE.ORIGINAL` or "original") or
           a faster implementation (`HoVerNetMODE.FAST` or "fast"). Defaults to `HoVerNetMODE.FAST`.