class_docstr: This module provides an optimized MM for beam decoding with attention.
  
  It leverage the fact that the source-side of the input is replicated beam
  times and the target-side of the input is of width one. This layer speeds up
  inference by replacing the inputs {(bsz x 1 x nhu), (bsz x sz2 x nhu)}
  with smaller inputs {(bsz/beam x beam x nhu), (bsz/beam x sz2 x nhu)}.
code: def set_beam_size(self, beam_size):
      self.beam_size = beam_size
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 3 for calling the BeamableMM obj's underlying member method code with example usage: obj.set_beam_size(beam_size) without return types