code: def forward(
      self,
      src_tokens,
      src_lengths,
      prev_output_tokens,
      src_lang_ids=None,
      **kwargs,
  ):
      if src_lang_ids is not None:
          encoder_out = self.encoder(
              src_tokens,
              src_lengths=src_lengths,
              src_lang_ids=src_lang_ids,
              **kwargs,
          )
      else:
          encoder_out = self.encoder(
              src_tokens, src_lengths=src_lengths, **kwargs
          )
      decoder_out = self.decoder(
          prev_output_tokens, encoder_out=encoder_out, **kwargs
      )
      return decoder_out
  
imports: import logging
  from collections import OrderedDict, namedtuple
  from typing import Dict, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from fairseq import checkpoint_utils, utils
  from fairseq.file_io import PathManager
  from fairseq.models import FairseqDecoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.models.speech_to_text import MultiInputDecoder, MultiModalityEncoder, SpeechWavTransformerEncoder, StackedSpeechWavTransformerEncoder
  from fairseq.models.transformer import TransformerDecoder, TransformerEncoder, TransformerModel
label_desc: Use image node_id 2 for calling the SpeechTextPreTrainModel obj's underlying member method code with example usage: obj.forward(src_tokens, src_lengths, prev_output_tokens, src_lang_ids) and returns: decoder_out