class_docstr: Transformer decoder consisting of *args.decoder_layers* layers. Each layer
  is a :class:`TransformerDecoderLayer`.
  Args:
      args (argparse.Namespace): parsed command-line arguments
      dictionary (~fairseq.data.Dictionary): decoding dictionary
      embed_tokens (torch.nn.Embedding): output embedding
      no_encoder_attn (bool, optional): whether to attend to encoder outputs.
          Default: ``False``
      left_pad (bool, optional): whether the input is left-padded. Default:
          ``False``
class_docstr_tok: ["Transformer","decoder","consisting","of","*","args.decoder_layers","*","layers",".","Each","layer","is","a",":","class",":","`","TransformerDecoderLayer","`",".","Args",":","args","(","argparse.Namespace",")",":","parsed","command-line","arguments","dictionary","(","~fairseq.data.Dictionary",")",":","decoding","dictionary","embed_tokens","(","torch.nn.Embedding",")",":","output","embedding","no_encoder_attn","(","bool",",","optional",")",":","whether","to","attend","to","encoder","outputs",".","Default",":","``","False","``","left_pad","(","bool",",","optional",")",":","whether","the","input","is","left-padded",".","Default",":","``","False","``"]
code: def _transpose_if_inference(self, x, incremental_state):
      if incremental_state:
          x = x.transpose(0, 1)
      return x
  
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 5 for calling the TransformerDecoder obj's underlying member method code with example usage: obj._transpose_if_inference(x, incremental_state) and returns: x