class_docstr: <a id="ClassificationHead"></a>
  
  ## MLP Classification Head
  
  This is the two layer MLP head to classify the image based on `[CLS]` token embedding.
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docstr: * `d_model` is the transformer embedding size
  * `n_hidden` is the size of the hidden layer
  * `n_classes` is the number of classes in the classification task
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code: def __init__(self, d_model: int, n_hidden: int, n_classes: int):
      """
      * `d_model` is the transformer embedding size
      * `n_hidden` is the size of the hidden layer
      * `n_classes` is the number of classes in the classification task
      """
      super().__init__()
      # First layer
      self.linear1 = nn.Linear(d_model, n_hidden)
      # Activation
      self.act = nn.ReLU()
      # Second layer
      self.linear2 = nn.Linear(n_hidden, n_classes)
  
imports: import torch
  from torch import nn
  from labml_helpers.module import Module
  from labml_nn.transformers import TransformerLayer
  from labml_nn.utils import clone_module_list
label_desc: Use image node_id 1 to create a new ClassificationHead object from inherited base classes: Module with example: obj = ClassificationHead(d_model, n_hidden, n_classes)