docstr: src_tokens: padded tensor (B, T, C * feat)
  src_lengths: tensor of original lengths of input utterances (B,)
docstr_tok: ["src_tokens",":","padded","tensor","(","B",",","T",",","C","*","feat",")","src_lengths",":","tensor","of","original","lengths","of","input","utterances","(","B",",",")"]
code: def forward(self, src_tokens, src_lengths, **kwargs):
      """
      src_tokens: padded tensor (B, T, C * feat)
      src_lengths: tensor of original lengths of input utterances (B,)
      """
  
      enc_out = super().forward(src_tokens, src_lengths)
      x = self.fc_out(enc_out["encoder_out"])
      # x = F.log_softmax(x, dim=-1)
      # Note: no need this line, because model.get_normalized_prob will call
      # log_softmax
      return {
          "encoder_out": x,  # (T, B, C)
          "encoder_padding_mask": enc_out[
              "encoder_padding_mask"
          ],  # (T, B)
      }
  
imports: import argparse
  import math
  from collections.abc import Iterable
  import torch
  import torch.nn
  from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqEncoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.modules import LinearizedConvolution, TransformerDecoderLayer, TransformerEncoderLayer, VGGBlock
label_desc: Use image node_id 2 for calling the VGGTransformerEncoderOnly obj's underlying member method code with example usage: obj.forward(src_tokens, src_lengths) and returns: dict