def METHOD_NAME(points, boxes):
    """Find all boxes in which each point is (CPU). The CPU version of
    :meth:`points_in_boxes_all`.
    Args:
        points (torch.Tensor): [B, M, 3], [x, y, z] in
            LiDAR/DEPTH coordinate
        boxes (torch.Tensor): [B, T, 7],
            num_valid_boxes <= T, [x, y, z, x_size, y_size, z_size, rz],
            (x, y, z) is the bottom center.
    Returns:
        box_idxs_of_pts (torch.Tensor): (B, M, T), default background = 0.
    """
    assert points.shape[0] == boxes.shape[0], \
        'Points and boxes should have the same batch size, ' \
        f'but got {points.shape[0]} and {boxes.shape[0]}'
    assert boxes.shape[2] == 7, \
        'boxes dimension should be 7, ' \
        f'but got unexpected shape {boxes.shape[2]}'
    assert points.shape[2] == 3, \
        'points dimension should be 3, ' \
        f'but got unexpected shape {points.shape[2]}'
    batch_size, num_points, _ = points.shape
    num_boxes = boxes.shape[1]
    point_indices = points.new_zeros((batch_size, num_boxes, num_points),
                                     dtype=torch.int)
    for b in range(batch_size):
        ext_module.points_in_boxes_cpu_forward(boxes[b].float().contiguous(),
                                               points[b].float().contiguous(),
                                               point_indices[b])
    point_indices = point_indices.transpose(1, 2)
    return point_indices