code: def forward(self, **kwargs):
      encoder_out_dict = self.w2v_encoder(**kwargs)
      w2v_encoder_out = encoder_out_dict["encoder_out"]  # TxBxC
      w2v_encoder_padding_mask = encoder_out_dict["padding_mask"]  # BxT
      # w2v_encoder_layer_results = encoder_out_dict["layer_results"]
      return self.pooling_layer(
          last_layer_feats=w2v_encoder_out,
          padding_mask=w2v_encoder_padding_mask,
          # all_layer_feats=w2v_encoder_layer_results,
      )
  
imports: import contextlib
  import logging
  from argparse import Namespace
  from dataclasses import dataclass, field
  from typing import Any, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from omegaconf import II, MISSING, open_dict
  from fairseq import checkpoint_utils, tasks, utils
  from fairseq.dataclass import ChoiceEnum, FairseqDataclass
  from fairseq.dataclass.utils import convert_namespace_to_omegaconf
  from fairseq.models import BaseFairseqModel, FairseqEncoder, register_model
  from fairseq.models.wav2vec.wav2vec2 import MASKING_DISTRIBUTION_CHOICES, Wav2Vec2Config
  from fairseq.models.wav2vec.wav2vec2_asr import Embedding, Linear, Wav2VecEncoder, Wav2Vec2AsrConfig
  from fairseq.tasks import FairseqTask
label_desc: Use image node_id 6 for calling the Wav2VecClassification obj's underlying member method code with example usage: obj.forward() and returns: self