docstr: Adds an image 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 1.
      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.
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code: def add_image_classification_head(
      self,
      head_name,
      num_labels=2,
      layers=1,
      activation_function="tanh",
      overwrite_ok=False,
      multilabel=False,
      id2label=None,
      use_pooler=False,
  ):
      """
      Adds an image 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 1.
          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.
      """
  
      head = ImageClassificationHead(
          self,
          head_name,
          num_labels=num_labels,
          layers=layers,
          activation_function=activation_function,
          multilabel=multilabel,
          id2label=id2label,
          use_pooler=use_pooler,
      )
      self.add_prediction_head(head, overwrite_ok)
  
file_path: adapters/src/adapters/models/vit/adapter_model.py
imports: from typing import Optional
  import torch
  from transformers.models.vit.modeling_vit import VIT_INPUTS_DOCSTRING, VIT_START_DOCSTRING, ViTModel, ViTPreTrainedModel
  from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from ...context import AdapterSetup
  from ...heads import ImageClassificationHead, ModelWithFlexibleHeadsAdaptersMixin
  from ...wrappers import init
label_desc: Use image node_id 3 for calling the ViTAdapterModel obj's underlying member method code with example usage: obj.add_image_classification_head(head_name, num_labels, layers, activation_function, overwrite_ok, multilabel, id2label, use_pooler) without return types