code: def get_dncnn(
      image_size: int = 32,
      image_chnls: int = 3,
      depth: int = 17,
      n_channels: int = 64,
  ) -> tf.keras.Model:
      """Constructs a DNCNN model.
  
      :param image_size: size of the images (int)
      :param image_chnls: number of channels in the inputs images (int)
      :param depth: depth of the network (int)
      :param n_channels: number of channels in the convolutional layers (int)
      :return: DNCNN model
      """
      inputs = layers.Input((image_size, image_size, image_chnls))
      outputs = run_dncnn(inputs, depth=depth, n_channels=n_channels)
      outputs = layers.Subtract()([inputs, outputs])
      return tf.keras.Model(inputs, outputs)
  
imports: from tensorflow.keras import layers
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: get_dncnn(image_size, image_chnls, depth, n_channels) and returns: tf