code: def forward(self, src_tokens, src_lengths=None, **kwargs):
      if (
          self.freezing_updates is not None
          and self.num_updates > self.freezing_updates
      ):
          for p in self.w2v_encoder.w2v_model.parameters():
              p.requires_grad = True
  
      padding_mask = lengths_to_padding_mask(src_lengths)
      out = self.w2v_encoder.forward(src_tokens, padding_mask, tbc=True)
      x, padding_mask = out["encoder_out"], out["padding_mask"]
      if self.w2v_proj_ln is not None:
          x = self.w2v_proj_ln(x)
  
      if self.adaptor is not None:
          x, padding_mask = self.adaptor(x, padding_mask)
  
      return {
          "encoder_out": [x],  # T x B x C
          "encoder_padding_mask": []
          if padding_mask is None
          else [padding_mask],  # B x T
          "encoder_embedding": [],  # B x T x C
          "encoder_states": [],  # List[T x B x C]
          "src_tokens": [],
          "src_lengths": [],
      }
  
imports: import copy
  import logging
  from typing import Dict, List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  from torch import Tensor
  from fairseq import checkpoint_utils, utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqEncoderModel, FairseqLanguageModel, register_model, register_model_architecture
  from fairseq.models.speech_to_speech.modules.ctc_decoder import CTCDecoder
  from fairseq.models.speech_to_text.hub_interface import S2THubInterface
  from fairseq.models.transformer import Embedding, TransformerDecoder, TransformerModelBase
  from fairseq.models.wav2vec import Wav2VecEncoder
  from fairseq.modules.layer_norm import LayerNorm
label_desc: Use image node_id 5 for calling the Wav2VecEncoderWithAdaptor obj's underlying member method code with example usage: obj.forward(src_tokens, src_lengths) and returns: dict