remove NPE when tasks is None

      y = np.reshape(y, (n_samples, -1))

      if w is not None:

        w = np.reshape(w, (n_samples, -1))

    n_tasks = y.shape[1]

    if ids is None:

      ids = np.arange(n_samples)

    if w is None:

      w = np.ones_like(y)

    if tasks is None:

      n_tasks = y.shape[1]

      tasks = np.arange(n_tasks)

    # raw_data = (X, y, w, ids)

    return DiskDataset.create_dataset(