torch.mm
torch.mm(input, mat2, *, out=None) -> Tensor
Performs a matrix multiplication of the matrices "input" and
   "mat2".
If "input" is a (n \times m) tensor, "mat2" is a (m \times p)
   tensor, "out" will be a (n \times p) tensor.
Note:
 This function does not broadcast. For broadcasting matrix
 products, see "torch.matmul()".

Supports strided and sparse 2-D tensors as inputs, autograd with
   respect to strided inputs.
This operation has support for arguments with sparse layouts. If
   "out" is provided it's layout will be used. Otherwise, the result
   layout will be deduced from that of "input".
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