Consolidate loop

        ids = ids[valid_inds]

        if len(self.tasks) > 0:

          # Featurize task results iff they exist.

          # Featurize task results if they exist.

          y, w = _convert_df_to_numpy(shard, self.tasks)

          if self.label_field:

    """

    features = []

    valid_inds = []

    field = self.feature_field

    data = shard[field].tolist()

    for idx, datapoint in enumerate(data):

      features.append(featurizer.featurize([datapoint]))

    valid_inds = np.array(

        [1 if elt.size > 0 else 0 for elt in features], dtype=bool)

    features = [

        elt for (is_valid, elt) in zip(valid_inds, features) if is_valid

    ]

    for idx, datapoint in enumerate(data):

      feat = featurizer.featurize([datapoint])

      is_valid = True if feat.size > 0 else False

      valid_inds.append(is_valid)

      if is_valid:

        features.append(feat)

    return np.squeeze(np.array(features), axis=1), valid_inds

    self.current_dir = os.path.dirname(os.path.abspath(__file__))

  def test_json_loader(self):

    input_file = os.path.join(self.current_dir, 'perov_test.json')

    input_file = os.path.join(self.current_dir,

                              'inorganic_crystal_sample_data.json')

    featurizer = SineCoulombMatrix(max_atoms=5)

    loader = JsonLoader(

        tasks=['e_form'],