text: import tensorflow as tf
  from tensorflow.keras.layers import DenseFeatures, Dense
  
  from easyrec.blocks import DenseBlock
  
  
  class MLP(tf.keras.models.Model):
      """
      Multi-layer Perceptron (MLP).
      """
  
      def __init__(self,
                   feature_columns,
                   units_list=None,
                   activation='relu'
                   ):
          """
  
          Args:
              feature_columns: List[FeatureColumn] to directly feed into tf.keras.layers.DenseFeatures, which basically
                  contains all feature fields.
              units_list: Dimension of fully connected stack outputs.
              activation: Activation to use.
          """
          super(MLP, self).__init__()
          if units_list is None:
              units_list = [256, 128, 64]
          self.input_layer = DenseFeatures(feature_columns=feature_columns)
          self.dense_block = DenseBlock(units_list=units_list, activation=activation)
          self.score = Dense(units=1, activation='sigmoid')
  
      def call(self, inputs, training=None, mask=None):
          inputs = self.input_layer(inputs)
          x = self.dense_block(inputs)
          return self.score(x)
  