docstr: Adds a sequence classification head on top of the model.
  
  Args:
      head_name (str): The name of the head.
      num_labels (int, optional): Number of classification labels. Defaults to 2.
      layers (int, optional): Number of layers. Defaults to 2.
      activation_function (str, optional): Activation function. Defaults to 'tanh'.
      overwrite_ok (bool, optional): Force overwrite if a head with the same name exists. Defaults to False.
      multilabel (bool, optional): Enable multilabel classification setup. Defaults to False.
docstr_tok: ["Adds","a","sequence","classification","head","on","top","of","the","model",".","Args",":","head_name","(","str",")",":","The","name","of","the","head",".","num_labels","(","int",",","optional",")",":","Number","of","classification","labels",".","Defaults","to","2.","layers","(","int",",","optional",")",":","Number","of","layers",".","Defaults","to","2.","activation_function","(","str",",","optional",")",":","Activation","function",".","Defaults","to","'tanh","'",".","overwrite_ok","(","bool",",","optional",")",":","Force","overwrite","if","a","head","with","the","same","name","exists",".","Defaults","to","False",".","multilabel","(","bool",",","optional",")",":","Enable","multilabel","classification","setup",".","Defaults","to","False","."]
code: def add_classification_head(
      self,
      head_name,
      num_labels=2,
      layers=2,
      activation_function="tanh",
      overwrite_ok=False,
      multilabel=False,
      id2label=None,
      use_pooler=False,
      dropout_prob=0,
  ):
      """
      Adds a sequence classification head on top of the model.
  
      Args:
          head_name (str): The name of the head.
          num_labels (int, optional): Number of classification labels. Defaults to 2.
          layers (int, optional): Number of layers. Defaults to 2.
          activation_function (str, optional): Activation function. Defaults to 'tanh'.
          overwrite_ok (bool, optional): Force overwrite if a head with the same name exists. Defaults to False.
          multilabel (bool, optional): Enable multilabel classification setup. Defaults to False.
      """
  
      if multilabel:
          head = MultiLabelClassificationHead(
              self,
              head_name,
              num_labels,
              layers,
              activation_function,
              id2label,
              use_pooler,
              dropout_prob=dropout_prob,
          )
      else:
          head = ClassificationHead(
              self,
              head_name,
              num_labels,
              layers,
              activation_function,
              id2label,
              use_pooler,
              dropout_prob=dropout_prob,
          )
      self.add_prediction_head(head, overwrite_ok)
  
file_path: adapters/src/adapters/models/llama/adapter_model.py
imports: import logging
  import torch
  from transformers.models.llama.modeling_llama import LLAMA_START_DOCSTRING, LlamaModel, LlamaPreTrainedModel
  from transformers.utils import add_start_docstrings
  from ...composition import adjust_tensors_for_parallel
  from ...heads import CausalLMHead, ClassificationHead, ModelWithFlexibleHeadsAdaptersMixin, MultiLabelClassificationHead, TaggingHead
  from ...model_mixin import EmbeddingAdaptersWrapperMixin
  from ...wrappers import init
label_desc: Use image node_id 4 for calling the LlamaAdapterModel obj's underlying member method code with example usage: obj.add_classification_head(head_name, num_labels, layers, activation_function, overwrite_ok, multilabel, id2label, use_pooler, dropout_prob) without return types