code: def Head(num_classes=1000, classifier_activation=None, name=None):
      """Implementation of classification head of ConvNeXt.
  
      Args:
          num_classes: number of classes for Dense layer
          classifier_activation: activation function for the Dense layer
          name: name prefix
  
      Returns:
          Classification head function.
      """
      if name is None:
          name = str(backend.get_uid("head"))
  
      def apply(x):
          x = layers.GlobalAveragePooling2D(name=name + "_head_gap")(x)
          x = layers.LayerNormalization(
              epsilon=1e-6, name=name + "_head_layernorm"
          )(x)
          x = layers.Dense(
              num_classes,
              activation=classifier_activation,
              name=name + "_head_dense",
          )(x)
          return x
  
      return apply
  
imports: import numpy
  from keras import backend
  from keras import initializers
  from keras import layers
  from keras import ops
  from keras import random
  from keras.api_export import keras_export
  from keras.applications import imagenet_utils
  from keras.layers.layer import Layer
  from keras.models import Functional
  from keras.models import Sequential
  from keras.ops import operation_utils
  from keras.utils import file_utils
label_desc: Use image node_id 3 for calling a global function with example usage: Head(num_classes, classifier_activation, name) and returns: apply, x