def masked_gather(points: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
    """
    Helper function for torch.gather to collect the points at
    the given indices in idx where some of the indices might be -1 to
    indicate padding. These indices are first replaced with 0.
    Then the points are gathered after which the padded values
    are set to 0.0.

    Args:
        points: (N, P, D) float32 tensor of points
        idx: (N, K) or (N, P, K) long tensor of indices into points, where
            some indices are -1 to indicate padding

    Returns:
        selected_points: (N, K, D) float32 tensor of points
            at the given indices
    """

    if len(idx) != len(points):
        raise ValueError("points and idx must have the same batch dimension")

    N, P, D = points.shape

    if idx.ndim == 3:
        # Case: KNN, Ball Query where idx is of shape (N, P', K)
        # where P' is not necessarily the same as P as the
        # points may be gathered from a different pointcloud.
        K = idx.shape[2]
        # Match dimensions for points and indices
        idx_expanded = idx[..., None].expand(-1, -1, -1, D)
        points = points[:, :, None, :].expand(-1, -1, K, -1)
    elif idx.ndim == 2:
        # Farthest point sampling where idx is of shape (N, K)
        idx_expanded = idx[..., None].expand(-1, -1, D)
    else:
        raise ValueError("idx format is not supported %s" % repr(idx.shape))

    idx_expanded_mask = idx_expanded.eq(-1)
    idx_expanded = idx_expanded.clone()
    # Replace -1 values with 0 for gather
    idx_expanded[idx_expanded_mask] = 0
    # Gather points
    selected_points = points.gather(dim=1, index=idx_expanded)
    # Replace padded values
    selected_points[idx_expanded_mask] = 0.0
    return selected_points

    Helper function for torch.gather to collect the points at
    the given indices in idx where some of the indices might be -1 to
    indicate padding. These indices are first replaced with 0.
    Then the points are gathered after which the padded values
    are set to 0.0.

    Args:
        points: (N, P, D) float32 tensor of points
        idx: (N, K) or (N, P, K) long tensor of indices into points, where
            some indices are -1 to indicate padding

    Returns:
        selected_points: (N, K, D) float32 tensor of points
            at the given indices
    
third_party/pytorch3d_simplified/pytorch3d/ops/sample_farthest_points.py