code: def mult_rst_grad(self, rst, ratio):
      assert isinstance(rst, dict)  # instead of EncoderOut
      assert len(rst["encoder_out"]) == 1
      rst["encoder_out"][0] = GradMultiply.apply(
          rst["encoder_out"][0], ratio
      )
      return rst
  
imports: import logging
  from collections import namedtuple
  import torch
  import torch.nn
  from fairseq import checkpoint_utils
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqDecoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.models.fairseq_encoder import EncoderOut
  from fairseq.models.speech_to_text import TransformerDecoder, S2TTransformerEncoder
  from fairseq.models.transformer import TransformerEncoder
  from fairseq.modules import TransformerEncoderLayer, GradMultiply, LayerNorm
label_desc: Use image node_id 5 for calling the DualInputEncoder obj's underlying member method code with example usage: obj.mult_rst_grad(rst, ratio) and returns: rst