code: def dense(x, dims, activation="relu", batchnorm=True, dropout_rate=0):
      if activation == "selu":
          x = layers.Dense(
              dims,
              activation="selu",
              kernel_initializer="lecun_normal",
              bias_initializer="zeros",
          )(x)
          if dropout_rate:
              x = layers.Dropout(dropout_rate)(x)
      elif activation == "relu":
          x = layers.Dense(dims, activation="relu")(x)
          if batchnorm:
              x = layers.BatchNormalization()(x)
          if dropout_rate:
              x = layers.Dropout(dropout_rate)(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 4 for calling a global function with example usage: dense(x, dims, activation, batchnorm, dropout_rate) and returns: x