Add memory_efficient arg to densenet docs (#1090)

* Add memory_efficient args to densenet docs

This PR adds the arg `memory_efficient` to densenet docs and also mentions the relevant publication.

* linting

torchvision/models/densenet.py

@@ -98,8 +98,8 @@ class DenseNet(nn.Module):
           (i.e. bn_size * k features in the bottleneck layer)
         drop_rate (float) - dropout rate after each dense layer
         num_classes (int) - number of classification classes
-        memory_efficient (bool) - set to True to use checkpointing. Much more memory efficient,
-          but slower. Default: *False*
+        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
+          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
     """
 
     def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
@@ -192,6 +192,8 @@ def densenet121(pretrained=False, progress=True, **kwargs):
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
+        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
+          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
     """
     return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress,
                      **kwargs)
@@ -204,6 +206,8 @@ def densenet161(pretrained=False, progress=True, **kwargs):
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
+        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
+          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
     """
     return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress,
                      **kwargs)
@@ -216,6 +220,8 @@ def densenet169(pretrained=False, progress=True, **kwargs):
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
+        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
+          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
     """
     return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress,
                      **kwargs)
@@ -228,6 +234,8 @@ def densenet201(pretrained=False, progress=True, **kwargs):
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
+        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
+          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
     """
     return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress,
                      **kwargs)