code: def __init__(
      self,
      vocab_size,
      input_feat_per_channel,
      vggblock_config=DEFAULT_ENC_VGGBLOCK_CONFIG,
      transformer_config=DEFAULT_ENC_TRANSFORMER_CONFIG,
      encoder_output_dim=512,
      in_channels=1,
      transformer_context=None,
      transformer_sampling=None,
  ):
      super().__init__(
          input_feat_per_channel=input_feat_per_channel,
          vggblock_config=vggblock_config,
          transformer_config=transformer_config,
          encoder_output_dim=encoder_output_dim,
          in_channels=in_channels,
          transformer_context=transformer_context,
          transformer_sampling=transformer_sampling,
      )
      self.fc_out = Linear(self.encoder_output_dim, vocab_size)
  
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 1 to create a new VGGTransformerEncoderOnly object from inherited base classes: VGGTransformerEncoder with example: obj = VGGTransformerEncoderOnly(vocab_size, input_feat_per_channel, vggblock_config, transformer_config, encoder_output_dim, in_channels, transformer_context, transformer_sampling)