code: def forward(
      self,
      input_ids,
      decoder_mask,
      encoder_embeddings,
      encoder_mask,
      decoder_mems=None,
  ):
      start_pos = 0
      if decoder_mems is not None:
          start_pos = input_ids.shape[1] - 1
          input_ids = input_ids[:, -1:]
          decoder_mask = decoder_mask[:, -1:]
          decoder_mems = torch.transpose(decoder_mems, 0, 1)
      decoder_embeddings = self._embedding(
          input_ids=input_ids, start_pos=start_pos
      )
      decoder_hidden_states = self._decoder(
          decoder_states=decoder_embeddings,
          decoder_mask=decoder_mask,
          encoder_states=encoder_embeddings,
          encoder_mask=encoder_mask,
          decoder_mems_list=decoder_mems,
          return_mems=self.return_mems,
          return_mems_as_list=False,
      )
      if self.return_mems:
          decoder_hidden_states = torch.transpose(
              decoder_hidden_states, 0, 1
          )
      return decoder_hidden_states
  
imports: from dataclasses import dataclass
  from typing import Dict, Optional
  import torch
  from omegaconf.omegaconf import MISSING
  from nemo.collections.asr.modules.transformer.decoder_module import DecoderModule
  from nemo.collections.asr.modules.transformer.encoder_module import EncoderModule
  from nemo.collections.asr.modules.transformer.transformer_decoders import TransformerDecoder
  from nemo.collections.asr.modules.transformer.transformer_encoders import TransformerEncoder
  from nemo.collections.asr.modules.transformer.transformer_modules import TransformerEmbedding
  from nemo.core.classes.common import typecheck
  from nemo.core.classes.exportable import Exportable
  from nemo.core.neural_types import ChannelType, NeuralType
label_desc: Use image node_id 2 for calling the TransformerDecoderNM obj's underlying member method code with example usage: obj.forward(input_ids, decoder_mask, encoder_embeddings, encoder_mask, decoder_mems) and returns: decoder_hidden_states