code: def __init__(
      self,
      vocab_size: int,
      pos_enc: str = None,
      embed_unit: int = 128,
      att_unit: int = 256,
      head: int = 2,
      unit: int = 1024,
      layer: int = 4,
      dropout_rate: float = 0.5,
  ):
      super().__init__()
      if pos_enc == "sinusoidal":
          pos_enc_class = PositionalEncoding
      elif pos_enc is None:
  
          def pos_enc_class(*args, **kwargs):
              return nn.Sequential()  # indentity
  
      else:
          raise ValueError(f"unknown pos-enc option: {pos_enc}")
  
      self.embed = nn.Embedding(vocab_size, embed_unit)
      self.encoder = Encoder(
          idim=embed_unit,
          attention_dim=att_unit,
          attention_heads=head,
          linear_units=unit,
          num_blocks=layer,
          dropout_rate=dropout_rate,
          input_layer="linear",
          pos_enc_class=pos_enc_class,
      )
      self.decoder = nn.Linear(att_unit, vocab_size)
  
file_path: espnet/espnet2/lm/transformer_lm.py
imports: from typing import Any, List, Tuple
  import torch
  import torch.nn
  from espnet2.lm.abs_model import AbsLM
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
  from espnet.nets.pytorch_backend.transformer.encoder import Encoder
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask
label_desc: Use image node_id 1 to create a new TransformerLM object from inherited base classes: AbsLM with example: obj = TransformerLM(vocab_size, pos_enc, embed_unit, att_unit, head, unit, layer, dropout_rate)