def METHOD_NAME(grid: List[torch.Tensor]) -> torch.Tensor:
    """
    :param grid: Each Tensor is a 1D set of increments for the grid in that dimension
    :type grid: List[torch.Tensor]
    :return: The set of points on the grid going by column-major order
    :rtype: torch.Tensor
    """
    if torch.is_tensor(grid):
        grid = convert_legacy_grid(grid)
    ndims = len(grid)
    assert all(axis.dim() == 1 for axis in grid)
    projections = torch.meshgrid(*grid, indexing="ij")
    grid_tensor = torch.stack(projections, axis=-1)
    # Note that if we did
    #     grid_data = grid_tensor.reshape(-1, ndims)
    # instead, we would be iterating through the points of our grid from the
    # last data dimension to the first data dimension. However, due to legacy
    # reasons, we need to iterate from the first data dimension to the last data
    # dimension when creating grid_data
    grid_data = grid_tensor.permute(*(reversed(range(ndims + 1)))).reshape(ndims, -1).transpose(0, 1)
    return grid_data