code: def __init__(
      self,
      filters: int = 1280,
      num_classes: int = 1000,
      drop_rate: float = 0.0,
      name: str = None,
  ):
      super(Head, self).__init__(name=name)
      self.conv_head = layers.Conv2D(
          filters=filters,
          kernel_size=1,
          kernel_initializer=CONV_KERNEL_INITIALIZER,
          padding="same",
          use_bias=False,
          name="conv2d",
      )
      self.norm = layers.BatchNormalization(
          axis=-1,
          momentum=0.9,
          epsilon=1e-3,
          name="batch_normalization",
      )
      self.act = layers.Activation("swish")
  
      self.avg = layers.GlobalAveragePooling2D()
      self.fc = layers.Dense(
          num_classes, kernel_initializer=DENSE_KERNEL_INITIALIZER
      )
  
      if drop_rate > 0:
          self.dropout = layers.Dropout(drop_rate)
  
imports: import itertools
  import tensorflow
  from tensorflow.keras import layers, Model, Input
label_desc: Use image node_id 1 to create a new Head object from inherited base classes: layers with example: obj = Head(filters, num_classes, drop_rate, name)