integers.
This operator supports TensorFloat32.
On certain ROCm devices, when using float16 inputs this module will
   use different precision for backward.
Parameters:
      * batch1 (Tensor) -- the first batch of matrices to be
        multiplied
  * **batch2** (*Tensor*) -- the second batch of matrices to be
    multiplied

Keyword Arguments:
      * beta (Number, optional) -- multiplier for "input"
        (\beta)
  * **input** (*Tensor*) -- matrix to be added

  * **alpha** (*Number**, **optional*) -- multiplier for *batch1 @
    batch2* (\alpha)

  * **out** (*Tensor**, **optional*) -- the output tensor.

Example:
  >>> M = torch.randn(3, 5)
  >>> batch1 = torch.randn(10, 3, 4)
  >>> batch2 = torch.randn(10, 4, 5)
  >>> torch.addbmm(M, batch1, batch2)
  tensor([[  6.6311,   0.0503,   6.9768, -12.0362,  -2.1653],
          [ -4.8185,  -1.4255,  -6.6760,   8.9453,   2.5743],
