import tensorflow as tf

      hparams, tf.estimator.ModeKeys.TRAIN
  )
  try:
    num_target_frames = hparams.video_num_target_frames
  except AttributeError:
    num_target_frames = 1
  target_value_shape_suffix = [num_target_frames]
  if distributional_size > 1:
    target_value_shape_suffix = [num_target_frames, distributional_size]
  features = {
      "inputs": observations,
      "epoch": tf.constant(epoch + 1),
      "input_action": tf.zeros(obs_shape[:2] + [1], dtype=tf.int32),
      "input_reward": tf.zeros(obs_shape[:2] + [1], dtype=tf.int32),
      "targets": tf.zeros(obs_shape[:1] + [num_target_frames] + obs_shape[2:]),
      "target_action": tf.zeros(
          obs_shape[:1] + [num_target_frames, 1], dtype=tf.int32),
      "target_reward": tf.zeros(
          obs_shape[:1] + [num_target_frames, 1], dtype=tf.int32),
      "target_policy": tf.zeros(
          obs_shape[:1] + [num_target_frames] + [action_space.n]),
      "target_value": tf.zeros(
          obs_shape[:1] + target_value_shape_suffix)
  }
  model.distributional_value_size = max(distributional_size, 1)
  model.use_epochs = hparams.use_epochs
  with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
    t2t_model.create_dummy_vars()
    (targets, _) = model(features)
  target_values = targets["target_value"][:, 0]
