docstr: Transformer with a classification head and adapters. 
docstr_tok: ["Transformer","with","a","classification","head","and","adapters","."]
code: def __init__(
      self,
      adapters_dim: int,
      embed_dim: int,
      hidden_dim: int,
      num_max_positions: int,
      num_heads: int,
      num_layers: int,
      dropout: float,
      causal: bool,
      initializer_range: float,
      num_classes: int,
  ):
      """Transformer with a classification head and adapters."""
      super().__init__()
      self.initializer_range: float = initializer_range
      self.tokenizer = BertTokenizer.from_pretrained(
          "bert-base-cased", do_lower_case=False
      )
      num_embeddings = len(self.tokenizer.vocab)
      self.num_layers = num_layers
      self.transformer: TransformerWithAdapters = (
          TransformerWithAdapters(
              adapters_dim,
              embed_dim,
              hidden_dim,
              num_embeddings,
              num_max_positions,
              num_heads,
              num_layers,
              dropout,
              causal=causal,
          )
      )
  
      self.classification_head = torch.nn.Linear(embed_dim, num_classes)
      self.apply(self.init_weights)
  
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 TransformerWithClfHeadAndAdapters object from inherited base classes: torch.nn with example: obj = TransformerWithClfHeadAndAdapters(adapters_dim, embed_dim, hidden_dim, num_max_positions, num_heads, num_layers, dropout, causal, initializer_range, num_classes)