code: def convert_advanced_relu(builder, layer, input_names, output_names, keras_layer):
      
      # Get input and output names
      input_name, output_name = (input_names[0], output_names[0])
  
      if keras_layer.max_value is None:
          builder.add_activation(layer, 'RELU', input_name, output_name)
          return
  
      # No direct support of RELU with max-activation value - use negate and
      # clip layers
      relu_output_name = output_name + '_relu'
      builder.add_activation(layer, 'RELU', input_name, relu_output_name)
      # negate it
      neg_output_name = relu_output_name + '_neg'
      builder.add_activation(layer+'__neg__', 'LINEAR', relu_output_name,
              neg_output_name,[-1.0, 0])
      # apply threshold
      clip_output_name = relu_output_name + '_clip'
      builder.add_unary(layer+'__clip__', neg_output_name, clip_output_name,
              'threshold', alpha = -keras_layer.max_value)
      # negate it back
      builder.add_activation(layer+'_neg2', 'LINEAR', clip_output_name,
              output_name,[-1.0, 0])
docs: Convert an ReLU layer with maximum value from keras to coreml.
  
      Parameters
      ----------
      keras_layer: layer
          A keras layer object.
  
      builder: NeuralNetworkBuilder
          A neural network builder object.