code: def __init__(self, vocab_size, n_layers, hidden_dim, n_heads):
      super().__init__()
  
      self.n_layers = n_layers
      self.embedding = nn.Embedding(vocab_size, hidden_dim)
      self.posencoding = PosEncoding(hidden_dim)
      self.layers = nn.ModuleList(
          [
              copy.deepcopy(
                  TransformerDecoderLayer(n_heads, hidden_dim)
              )
              for _ in range(n_layers)
          ]
      )
      self.layernorm = LayerNorm(hidden_dim)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import math
  import copy
label_desc: Use image node_id 1 to create a new TransformerDecoder object from inherited base classes: nn with example: obj = TransformerDecoder(vocab_size, n_layers, hidden_dim, n_heads)