class_docstr: VGG + Transformer encoder
class_docstr_tok: ["VGG","+","Transformer","encoder"]
code: def validate_transformer_config(self, transformer_config):
      for config in transformer_config:
          input_dim, num_heads = config[:2]
          if input_dim % num_heads != 0:
              msg = (
                  "ERROR in transformer config {}: ".format(config)
                  + "input dimension {} ".format(input_dim)
                  + "not dividable by number of heads {}".format(
                      num_heads
                  )
              )
              raise ValueError(msg)
  
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 4 for calling the VGGTransformerEncoder obj's underlying member method code with example usage: obj.validate_transformer_config(transformer_config) without return types