6638 use np.prod instead of np.product (#6639)

Fixes #6638 



### 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`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

Author: wyli

monai/networks/nets/regressor.py

@@ -143,7 +143,7 @@ def _get_layer(
         return layer
 
     def _get_final_layer(self, in_shape: Sequence[int]):
-        linear = nn.Linear(int(np.product(in_shape)), int(np.product(self.out_shape)))
+        linear = nn.Linear(int(np.prod(in_shape)), int(np.prod(self.out_shape)))
         return nn.Sequential(nn.Flatten(), linear)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:

monai/networks/nets/varautoencoder.py

@@ -120,7 +120,7 @@ def __init__(
         for s in strides:
             self.final_size = calculate_out_shape(self.final_size, self.kernel_size, s, padding)  # type: ignore
 
-        linear_size = int(np.product(self.final_size)) * self.encoded_channels
+        linear_size = int(np.prod(self.final_size)) * self.encoded_channels
         self.mu = nn.Linear(linear_size, self.latent_size)
         self.logvar = nn.Linear(linear_size, self.latent_size)
         self.decodeL = nn.Linear(self.latent_size, linear_size)