code: def get_reader_input_fn(train_config, preprocess_output_dir, model_type,
                          data_paths, batch_size, shuffle, num_epochs=None):
    
  
    def get_input_features():
      """Read the input features from the given data paths."""
      _, examples = util.read_examples(
          input_files=data_paths,
          batch_size=batch_size,
          shuffle=shuffle,
          num_epochs=num_epochs)
      features = util.parse_example_tensor(examples=examples,
                                           train_config=train_config,
                                           keep_target=True)
  
      target_name = train_config['target_column']
      target = features.pop(target_name)
      features, target = util.preprocess_input(
          features=features,
          target=target,
          train_config=train_config,
          preprocess_output_dir=preprocess_output_dir,
          model_type=model_type)
  
      return features, target
  
    # Return a function to input the feaures into the model from a data path.
    return get_input_features
docs: Builds input layer for training.