Fix errors on non-contiguous tensors in equalize() Tensor-backend

[datumbox]

The use of view() in equalize() leads to the following errors when the input is non-contiguous. The problem affects stable:

    hist = torch.bincount(img_chan.view(-1), minlength=256)
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.

And prototype:

  File "./torchvision/prototype/transforms/functional/_color.py", line 237, in equalize_video
    return equalize_image_tensor(video)
  File "./torchvision/prototype/transforms/functional/_color.py", line 230, in equalize_image_tensor
    return _equalize_image_tensor_vec(image.view(-1, height, width)).view(image.shape)
  File "./torchvision/prototype/features/_feature.py", line 94, in __torch_function__
    output = func(*args, **kwargs or dict())
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.

It was first discovered after starting doing end-to-end training with complex Augmentations using the Tensor Backend. The PR updates the use of view() to reshape() to ensure that both operators work as expected.

[pmeier]

Thanks Vasilis!