code: def conv_block(x, growth_rate, name):
      """A building block for a dense block.
  
      Args:
          x: input tensor.
          growth_rate: float, growth rate at dense layers.
          name: string, block label.
  
      Returns:
          Output tensor for the block.
      """
      bn_axis = (
          3 if backend.image_data_format() == "channels_last" else 1
      )
      x1 = layers.BatchNormalization(
          axis=bn_axis, epsilon=1.001e-5, name=name + "_0_bn"
      )(x)
      x1 = layers.Activation("relu", name=name + "_0_relu")(x1)
      x1 = layers.Conv2D(
          4 * growth_rate, 1, use_bias=False, name=name + "_1_conv"
      )(x1)
      x1 = layers.BatchNormalization(
          axis=bn_axis, epsilon=1.001e-5, name=name + "_1_bn"
      )(x1)
      x1 = layers.Activation("relu", name=name + "_1_relu")(x1)
      x1 = layers.Conv2D(
          growth_rate,
          3,
          padding="same",
          use_bias=False,
          name=name + "_2_conv",
      )(x1)
      x = layers.Concatenate(axis=bn_axis, name=name + "_concat")(
          [x, x1]
      )
      return x
  
file_path: keras/keras/applications/densenet.py
imports: from keras import backend
  from keras import layers
  from keras.api_export import keras_export
  from keras.applications import imagenet_utils
  from keras.models import Functional
  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: conv_block(x, growth_rate, name) and returns: x