code: def sep_conv(x, num_filters, kernel_size=(3, 3), activation="relu"):
      if activation == "selu":
          x = layers.SeparableConv2D(
              num_filters,
              kernel_size,
              activation="selu",
              padding="same",
              depthwise_initializer="lecun_normal",
              pointwise_initializer="lecun_normal",
          )(x)
      elif activation == "relu":
          x = layers.SeparableConv2D(
              num_filters, kernel_size, 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 1 for calling a global function with example usage: sep_conv(x, num_filters, kernel_size, activation) and returns: x