particular the matrix-matrix (both arguments 2-dimensional)
   supports sparse arguments with the same restrictions as
   "torch.mm()"
Warning:
 Sparse support is a beta feature and some layout(s)/dtype/device
 combinations may not be supported, or may not have autograd
 support. If you notice missing functionality please open a
 feature request.

This operator supports TensorFloat32.
On certain ROCm devices, when using float16 inputs this module will
   use different precision for backward.
Note:
 The 1-dimensional dot product version of this function does not
 support an "out" parameter.

Parameters:
      * input (Tensor) -- the first tensor to be multiplied
  * **other** (*Tensor*) -- the second tensor to be multiplied

Keyword Arguments:
      out (Tensor, optional) -- the output tensor.
Example:
  >>> # vector x vector
  >>> tensor1 = torch.randn(3)
  >>> tensor2 = torch.randn(3)
