code: def batch_log_matvecmul(A, b):
      """For each 'matrix' and 'vector' pair in the batch, do matrix-vector
      multiplication in the log domain, i.e., logsumexp instead of add,
      add instead of multiply.
  
      Arguments
      ---------
      A : torch.Tensor (batch, dim1, dim2)
          Tensor
      b : torch.Tensor (batch, dim1)
          Tensor.
  
      Outputs
      -------
      x : torch.Tensor (batch, dim1)
  
      Example
      -------
      >>> A = torch.tensor([[[   0., 0.],
      ...                    [ -1e5, 0.]]])
      >>> b = torch.tensor([[0., 0.,]])
      >>> x = batch_log_matvecmul(A, b)
      >>> x
      tensor([[0.6931, 0.0000]])
      >>>
      >>> # non-log domain equivalent without batching functionality
      >>> A_ = torch.tensor([[1., 1.],
      ...                    [0., 1.]])
      >>> b_ = torch.tensor([1., 1.,])
      >>> x_ = torch.matmul(A_, b_)
      >>> x_
      tensor([2., 1.])
      """
      b = b.unsqueeze(1)
      x = torch.logsumexp(A + b, dim=2)
  
      return x
  
imports: import torch
  import random
  from speechbrain.utils.checkpoints import register_checkpoint_hooks
  from speechbrain.utils.checkpoints import mark_as_saver
  from speechbrain.utils.checkpoints import mark_as_loader
  from speechbrain.utils.data_utils import undo_padding
label_desc: Use image node_id 2 for calling a global function with example usage: batch_log_matvecmul(A, b) and returns: x