Remove hard coding of bias_downsample in resnet methods (#6848)

Fixes #6811 .

### Description
Remove hard coding of `bias_downsample` in `resnet` methods. This allows
for loading `MedicalNet` models.

### 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: Suraj Pai <bspai@bwh.harvard.edu>

monai/networks/nets/resnet.py

@@ -333,14 +333,16 @@ def _resnet(
     progress: bool,
     **kwargs: Any,
 ) -> ResNet:
-    model: ResNet = ResNet(block, layers, block_inplanes, bias_downsample=not pretrained, **kwargs)
+    model: ResNet = ResNet(block, layers, block_inplanes, **kwargs)
     if pretrained:
         # Author of paper zipped the state_dict on googledrive,
         # so would need to download, unzip and read (2.8gb file for a ~150mb state dict).
         # Would like to load dict from url but need somewhere to save the state dicts.
         raise NotImplementedError(
             "Currently not implemented. You need to manually download weights provided by the paper's author"
             " and load then to the model with `state_dict`. See https://github.com/Tencent/MedicalNet"
+            "Please ensure you pass the appropriate `shortcut_type` and `bias_downsample` args. as specified"
+            "here: https://github.com/Tencent/MedicalNet/tree/18c8bb6cd564eb1b964bffef1f4c2283f1ae6e7b#update20190730"
         )
     return model