code: def __init__(
      self,
      vocab_size: int = 33278,  # default for WikiText2
      ninp: int = 200,
      nhead: int = 2,
      nhid: int = 200,
      nlayers: int = 2,
      dropout: float = 0.2,
  ) -> None:
      super().__init__()
      self.pos_encoder = PositionalEncoding(ninp, dropout)
      self.embedding = nn.Embedding(vocab_size, ninp)
      self.transformer = nn.Transformer(
          d_model=ninp,
          nhead=nhead,
          num_encoder_layers=nlayers,
          num_decoder_layers=nlayers,
          dim_feedforward=nhid,
          dropout=dropout,
          batch_first=True,
      )
      self.decoder = nn.Linear(ninp, vocab_size)
  
      self.ninp = ninp
      self.vocab_size = vocab_size
      self.src_mask = None
  
imports: import math
  import os
  from pathlib import Path
  from typing import Dict, List, Optional, Tuple
  import requests
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.nn.modules import MultiheadAttention
  from torch.utils.data import DataLoader, Dataset
  from lightning.pytorch import LightningModule
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(vocab_size, ninp, nhead, nhid, nlayers, dropout)