View and select functions

In 0.11, we’ve included a number of different view and select functions – under the hood, these are implemented as pass through functions – i.e. functions that apply the operator to both the mask and the data.

By way of example, consider select; this operation can be applied to both the data and the mask of a MaskedTensor, and the result will then be wrapped into a new MaskedTensor.

A quick example of this:

>>> data = torch.arange(12, dtype=torch.float).reshape((3,4))
>>> mask = torch.tensor([
        [True, False, False, True],
        [False, True, False, False],
        [True, True, True, True]])
>>> mt = masked_tensor(data, mask)
>>> data.select(0, 1)
tensor([4., 5., 6., 7.])
>>> mask.select(0, 1)
tensor([False,  True, False, False])
>>> mt.select(0, 1)
masked_tensor(
[      --,   5.0000,       --,       --]
)

Below is a list of the ops that are currently supported:

`atleast_1d`	Returns a 1-dimensional view of each input tensor with zero dimensions.
`broadcast_tensors`	Broadcasts the given tensors according to broadcasting-semantics.
`broadcast_to`	Broadcasts `input` to the shape `shape`.
`cat`	Concatenates the given sequence of `seq` tensors in the given dimension.
`chunk`	Attempts to split a tensor into the specified number of chunks.
`column_stack`	Creates a new tensor by horizontally stacking the tensors in `tensors`.
`dsplit`	Splits `input`, a tensor with three or more dimensions, into multiple tensors depthwise according to `indices_or_sections`.
`flatten`	Flattens `input` by reshaping it into a one-dimensional tensor.
`hsplit`	Splits `input`, a tensor with one or more dimensions, into multiple tensors horizontally according to `indices_or_sections`.
`hstack`	Stack tensors in sequence horizontally (column wise).
`kron`	Computes the Kronecker product, denoted by $\otimes$ , of `input` and `other`.
`meshgrid`	Creates grids of coordinates specified by the 1D inputs in attr:tensors.
`narrow`	Returns a new tensor that is a narrowed version of `input` tensor.
`ravel`	Return a contiguous flattened tensor.
`select`	Slices the `input` tensor along the selected dimension at the given index.
`split`	Splits the tensor into chunks.
`t`	Expects `input` to be <= 2-D tensor and transposes dimensions 0 and 1.
`transpose`	Returns a tensor that is a transposed version of `input`.
`vsplit`	Splits `input`, a tensor with two or more dimensions, into multiple tensors vertically according to `indices_or_sections`.
`vstack`	Stack tensors in sequence vertically (row wise).
`Tensor.expand`	Returns a new view of the `self` tensor with singleton dimensions expanded to a larger size.
`Tensor.expand_as`	Expand this tensor to the same size as `other`.
`Tensor.reshape`	Returns a tensor with the same data and number of elements as `self` but with the specified shape.
`Tensor.reshape_as`	Returns this tensor as the same shape as `other`.
`Tensor.view`	Returns a new tensor with the same data as the `self` tensor but of a different `shape`.