docstr: Args:
      unique_id: A unique identifier for the head
      in_plane: Input size for the fully connected layer
      num_classes: Number of output classes for the head
      hidden_dim: If not None, a hidden layer with the specific dimension is added
      normalize_inputs: If specified, normalize the inputs using the specified
          method. Supports "l2" normalization.
code: def __init__(
      self,
      unique_id: str,
      in_plane: int,
      num_classes: Optional[int] = None,
      hidden_dim: Optional[int] = None,
      normalize_inputs: Optional[str] = None,
  ):
      """
      Args:
          unique_id: A unique identifier for the head
          in_plane: Input size for the fully connected layer
          num_classes: Number of output classes for the head
          hidden_dim: If not None, a hidden layer with the specific dimension is added
          normalize_inputs: If specified, normalize the inputs using the specified
              method. Supports "l2" normalization.
      """
      super().__init__(unique_id, num_classes)
  
      if (
          normalize_inputs is not None
          and normalize_inputs != NORMALIZE_L2
      ):
          raise ValueError(
              f"Unsupported value for normalize_inputs: {normalize_inputs}"
          )
  
      if num_classes is None:
          layers = []
      elif hidden_dim is None:
          layers = [("head", nn.Linear(in_plane, num_classes))]
      else:
          layers = [
              ("pre_logits", nn.Linear(in_plane, hidden_dim)),
              ("act", nn.Tanh()),
              ("head", nn.Linear(hidden_dim, num_classes)),
          ]
      self.layers = nn.Sequential(OrderedDict(layers))
      self.normalize_inputs = normalize_inputs
      self.init_weights()
  
file_path: ClassyVision/classy_vision/heads/vision_transformer_head.py
imports: import copy
  from collections import OrderedDict
  from typing import Optional
  import torch.nn
  from classy_vision.heads import ClassyHead, register_head
  from ..models.lecun_normal_init import lecun_normal_init
label_desc: Use image node_id 1 to create a new VisionTransformerHead object from inherited base classes: ClassyHead with example: obj = VisionTransformerHead(unique_id, in_plane, num_classes, hidden_dim, normalize_inputs)