code: def transform_function(features, mode):
      """Transform function for DASALC model."""
      mask = features.pop(_MASK)
      (
          context_features,
          example_features,
      ) = tfr.feature.encode_listwise_features(
          features=features,
          context_feature_columns=context_feature_columns(),
          example_feature_columns=example_feature_columns(),
          mode=mode,
          scope="transform_layer",
      )
  
      training = mode == tf_estimator.ModeKeys.TRAIN
      concat_tensor = tfr.keras.layers.ConcatFeatures()(
          inputs=(context_features, example_features, mask)
      )
      din_layer = tfr.keras.layers.DocumentInteractionAttention(
          num_heads=FLAGS.num_attention_heads,
          head_size=FLAGS.head_size,
          num_layers=FLAGS.num_attention_layers,
          dropout=FLAGS.dropout_rate,
          input_noise_stddev=FLAGS.input_noise_stddev,
      )
      example_features[
          "document_interaction_network_embedding"
      ] = din_layer(inputs=(concat_tensor, mask), training=training)
      return context_features, example_features
  
file_path: ranking/tensorflow_ranking/research/dasalc.py
imports: from absl import flags
  import tensorflow
  from tensorflow import estimator
  from tensorflow.python.estimator.canned import optimizers
  import tensorflow_ranking
label_desc: Use image node_id 3 for calling a global function with example usage: transform_function(features, mode) and returns: context_features,  example_features