code: def _one_step_forward(
      self,
      decoder_input_ids=None,
      encoder_hidden_states=None,
      encoder_input_mask=None,
      decoder_mems_list=None,
      lm_mems_list=None,
      pos=0,
  ):
      nmt_log_probs, decoder_mems_list = super()._one_step_forward(
          decoder_input_ids,
          encoder_hidden_states,
          encoder_input_mask,
          decoder_mems_list,
          pos,
      )
      input_mask = mask_padded_tokens(
          decoder_input_ids, self.pad
      ).float()
      lm_hidden_states = self.language_model.encoder.embedding.forward(
          decoder_input_ids, start_pos=pos
      )
  
      lm_mems_list = self.language_model.encoder.encoder.forward(
          lm_hidden_states,
          input_mask,
          lm_mems_list,
          return_mems=True,
      )
      lm_log_probs = self.language_model.log_softmax.forward(
          hidden_states=lm_mems_list[-1][:, -1:]
      )
  
      log_probs = nmt_log_probs + self.fusion_coef * lm_log_probs
  
      return log_probs, decoder_mems_list, lm_mems_list
  
imports: from contextlib import contextmanager
  import torch
  from nemo.collections.common.parts import NEG_INF, mask_padded_tokens
label_desc: Use image node_id 2 for calling the BeamSearchSequenceGeneratorWithLanguageModel obj's underlying member method code with example usage: obj._one_step_forward(decoder_input_ids, encoder_hidden_states, encoder_input_mask, decoder_mems_list, lm_mems_list, pos) and returns: log_probs,  decoder_mems_list,  lm_mems_list