code: def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):
      super(TransformerModel, self).__init__()
      self.pos_encoder = PositionalEncoding(ninp, dropout)
      encoder_layers = torch.nn.TransformerEncoderLayer(
          ninp, nhead, nhid, dropout
      )
      self.transformer_encoder = torch.nn.TransformerEncoder(
          encoder_layers, nlayers
      )
      self.encoder = nn.Embedding(ntoken, ninp)
      self.ninp = ninp
      self.decoder = nn.Linear(ninp, ntoken)
  
      self.init_weights()
  
imports: import math
  import sys
  import pytest
  import torch
  import torch.nn
  import torch.nn
  from fairscale.internal import torch_version
label_desc: Use image node_id 1 to create a new TransformerModel object from inherited base classes: nn with example: obj = TransformerModel(ntoken, ninp, nhead, nhid, nlayers, dropout)