code: def conv(
      x,
      num_filters,
      kernel_size=(3, 3),
      activation="relu",
      strides=(2, 2),
  ):
      "2d convolution block."
      if activation == "selu":
          x = layers.Conv2D(
              num_filters,
              kernel_size,
              strides=strides,
              activation="selu",
              padding="same",
              kernel_initializer="lecun_normal",
              bias_initializer="zeros",
          )(x)
      elif activation == "relu":
          x = layers.Conv2D(
              num_filters,
              kernel_size,
              strides=strides,
              padding="same",
              use_bias=False,
          )(x)
          x = layers.BatchNormalization()(x)
          x = layers.Activation("relu")(x)
      return x
  
file_path: keras-tuner/keras_tuner/applications/xception.py
imports: from keras_tuner.api_export import keras_tuner_export
  from keras_tuner.backend import keras
  from keras_tuner.backend import ops
  from keras_tuner.backend.keras import layers
  from keras_tuner.engine import hypermodel
label_desc: Use image node_id 3 for calling a global function with example usage: conv(x, num_filters, kernel_size, activation, strides) and returns: x