Removed the custom-predict method

        feed_dict[self.X] = atom_features

        yield feed_dict

  # def predict(self, dataset, transformers=[], outputs=None):

  #   """

  #   Uses self to make predictions on provided Dataset object.

  #

  #   Parameters

  #   ----------

  #   dataset: dc.data.Dataset

  #     Dataset to make prediction on

  #   transformers: list

  #     List of dc.trans.Transformers.

  #   outputs: object

  #     If outputs is None, then will assume outputs=self.default_outputs. If outputs is

  #     a Layer/Tensor, then will evaluate and return as a single ndarray. If

  #     outputs is a list of Layers/Tensors, will return a list of ndarrays.

  #

  #   Returns

  #   -------

  #   results: numpy ndarray or list of numpy ndarrays

  #   """

  #   generator = self.default_generator(dataset, predict=True, pad_batches=True)

  #   preds = self.predict_on_generator(generator, transformers, outputs)

  #   if len(dataset.y) % self.batch_size == 0:

  #     return preds

  #   else:

  #     after_pad = (len(dataset.y) // self.batch_size + 1) * self.batch_size

  #     closest = (len(dataset.y) // self.batch_size) * self.batch_size

  #     remainder = len(dataset.y) % self.batch_size

  #     num_added = after_pad - remainder - closest

  #     preds = preds[:-num_added]

  #     return preds