code: def __init__(
      self,
      embed_dim: int,
      hidden_dim: int,
      num_embeddings: int,
      num_max_positions: int,
      num_heads: int,
      num_layers: int,
      dropout: float,
      causal: bool,
  ):
      super().__init__()
      self.causal: bool = causal
      self.tokens_embeddings: torch.nn.Embedding = torch.nn.Embedding(
          num_embeddings, embed_dim
      )
      self.position_embeddings: torch.nn.Embedding = torch.nn.Embedding(
          num_max_positions, embed_dim
      )
      self.dropout: torch.nn.Dropout = torch.nn.Dropout(dropout)
  
      self.attentions, self.feed_forwards = (
          torch.nn.ModuleList(),
          torch.nn.ModuleList(),
      )
      self.layer_norms_1, self.layer_norms_2 = (
          torch.nn.ModuleList(),
          torch.nn.ModuleList(),
      )
      for _ in range(num_layers):
          self.attentions.append(
              torch.nn.MultiheadAttention(
                  embed_dim, num_heads, dropout=dropout
              )
          )
          self.feed_forwards.append(
              torch.nn.Sequential(
                  torch.nn.Linear(embed_dim, hidden_dim),
                  torch.nn.ReLU(),
                  torch.nn.Linear(hidden_dim, embed_dim),
              )
          )
          self.layer_norms_1.append(
              torch.nn.LayerNorm(embed_dim, eps=1e-12)
          )
          self.layer_norms_2.append(
              torch.nn.LayerNorm(embed_dim, eps=1e-12)
          )
  
      self.attn_mask = None
      self.tokenizer = BertTokenizer.from_pretrained(
          "bert-base-cased", do_lower_case=False
      )
  
imports: import torch
  from pytorch_pretrained_bert import BertTokenizer
  from transfer_nlp.plugins.config import register_plugin
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: torch.nn with example: obj = Transformer(embed_dim, hidden_dim, num_embeddings, num_max_positions, num_heads, num_layers, dropout, causal)