integers.
This operator supports TensorFloat32.
On certain ROCm devices, when using float16 inputs this module will
   use different precision for backward.
Parameters:
      * input (Tensor) -- the tensor to be added
  * **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)
  * **alpha** (*Number**, **optional*) -- multiplier for
    \text{batch1} \mathbin{@} \text{batch2} (\alpha)

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

Example:
  >>> M = torch.randn(10, 3, 5)
  >>> batch1 = torch.randn(10, 3, 4)
  >>> batch2 = torch.randn(10, 4, 5)
  >>> torch.baddbmm(M, batch1, batch2).size()
  torch.Size([10, 3, 5])
