code: def prepare_data(problem, hparams, params, config):
      """Construct input pipeline."""
      input_fn = problem.make_estimator_input_fn(
          tf_estimator.ModeKeys.EVAL, hparams, force_repeat=True
      )
      dataset = input_fn(params, config)
      features, _ = dataset.make_one_shot_iterator().get_next()
      inputs, labels = features["targets"], features["inputs"]
      inputs = tf.to_float(inputs)
      input_shape = inputs.shape.as_list()
      inputs = tf.reshape(
          inputs, [hparams.batch_size] + input_shape[1:]
      )
      labels = tf.reshape(labels, [hparams.batch_size])
      return inputs, labels, features
  
file_path: tensor2tensor/tensor2tensor/bin/t2t_attack.py
imports: import os
  from tensor2tensor.bin import t2t_trainer
  from tensor2tensor.data_generators import problem
  from tensor2tensor.utils import adv_attack_utils
  from tensor2tensor.utils import cloud_mlengine
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  from tensor2tensor.utils import trainer_lib
  from tensor2tensor.utils import usr_dir
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 5 for calling a global function with example usage: prepare_data(problem, hparams, params, config) and returns: inputs,  labels,  features