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* load densenet tensors onto cpu

* fix uneven padding in make_grid

* revert incorrectly added commit

* load densenet tensors onto cpu

* fix docs for size param in random + center crop

* revert incorrect commit

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* added FashionMNIST dataset

* documentation

* fixed formatting

* fixed formatting

* DOC fixed typo in make_grid argument name

* DOC document "padding" make_grid argument

isinstance(padding, tuple) is added to the padding assertion to be able to pass a sequence.

* enable ~ expression in datasets

* fixed root in __init__ to self.root

Wrong order of dimensions In transforms.CenterCrop and transforms.RandomCrop:
Documentation says it should be (w, h), but it actually is (h, w).
Fixing docs to match the code

* Add documentation for transforms

* document and remove unused imports in mnist.py

* document lsun, mscoco datasets

* rest of the datasets documented

* Clean up the documentation in other functions

* Add links for datasets

* Add more documentation

* pep8 fix

Fixes #152

See https://github.com/python-pillow/Pillow/issues/835

changed a small mistake.

It can be enabled by calling torchvision.set_image_backend('accimage')

First dimension is batch size, channel is the second

* add pad_value argument

* add argument to save_image

* fix space

* requested change of order

* update docs

* Fixed border missing on bottom and right side using make_grid

* Add support for Transforms.Scale([h, w]) with specific height and width

if self.size is a int then scale the image with the shorter side,
otherwise if self.size is a list then scale the image to self.size
directly

* Add assert of size and doc for README

Add assert of size and doc for README

* Fix linter problem

Fix linter problem

* Add test for Scale

Add test for Scale

* Add both tuple and list support for Scale.size

Add both tuple and list support for Scale.size

* Add order of Scale.size in document and test case for list type of Scale.size

Add order of Scale.size in document and test case for list type of
Scale.size

I have made some performance improvements to the model, and testing them now.  If results turn out good, I will submit a separate PR.