class_docstr: VGG + Transformer encoder
class_docstr_tok: ["VGG","+","Transformer","encoder"]
code: def infer_conv_output_dim(self, in_channels, input_dim):
      sample_seq_len = 200
      sample_bsz = 10
      x = torch.randn(
          sample_bsz, in_channels, sample_seq_len, input_dim
      )
      for i, _ in enumerate(self.conv_layers):
          x = self.conv_layers[i](x)
      x = x.transpose(1, 2)
      mb, seq = x.size()[:2]
      return x.contiguous().view(mb, seq, -1).size(-1)
  
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 3 for calling the VGGTransformerEncoder obj's underlying member method code with example usage: obj.infer_conv_output_dim(in_channels, input_dim) and returns: x