matrix, the result will be a (pm \times qn) block matrix:
    \mathbf{A} \otimes \mathbf{B}=\begin{bmatrix} a_{11}
    \mathbf{B} & \cdots & a_{1 n} \mathbf{B} \\ \vdots & \ddots &
    \vdots \\ a_{m 1} \mathbf{B} & \cdots & a_{m n} \mathbf{B}
    \end{bmatrix}

 where "input" is \mathbf{A} and "other" is \mathbf{B}.

Parameters:
      * input (Tensor) --
  * **other** (*Tensor*) --

Keyword Arguments:
      out (Tensor, optional) -- The output tensor. Ignored
      if "None". Default: "None"
Examples:
  >>> mat1 = torch.eye(2)
  >>> mat2 = torch.ones(2, 2)
  >>> torch.kron(mat1, mat2)
  tensor([[1., 1., 0., 0.],
          [1., 1., 0., 0.],
          [0., 0., 1., 1.],
          [0., 0., 1., 1.]])

  >>> mat1 = torch.eye(2)
  >>> mat2 = torch.arange(1, 5).reshape(2, 2)
  >>> torch.kron(mat1, mat2)
  tensor([[1., 2., 0., 0.],
          [3., 4., 0., 0.],
          [0., 0., 1., 2.],
