debug

          result = np.array(sess.run(out_tensors, feed_dict=feed_dict))

          if len(result.shape) == 3:

            result = np.transpose(result, axes=[1, 0, 2])

          if self.mode == 'regression':

            result = np.squeeze(result, 2)

          result = undo_transforms(result, transformers)

          results.append(result)

        return np.concatenate(results, axis=0)

    if self.transform_X:

      return z * self.X_stds + self.X_means

    elif self.transform_y:

      return z * self.y_stds + self.y_means

      y_stds = self.y_stds

      y_means = self.y_means

      n_tasks = self.y_stds.shape[0]

      z_shape = list(z.shape)

      z_shape.reverse()

      for dim in z_shape:

        if dim != n_tasks and dim == 1:

          y_stds = np.expand_dims(y_stds, -1)

          y_means = np.expand_dims(y_means, -1)

      return z * y_stds + y_means

  def untransform_grad(self, grad, tasks):

    """