def build_linear_positions(index_dims, output_range=(-1.0, 1.0)):
    """
    Generate an array of position indices for an N-D input array.

    Args:
      index_dims (`List[int]`):
        The shape of the index dimensions of the input array.
      output_range (`Tuple[float]`, *optional*, defaults to `(-1.0, 1.0)`):
        The min and max values taken by each input index dimension.

    Returns:
      `torch.FloatTensor` of shape `(index_dims[0], index_dims[1], .., index_dims[-1], N)`.
    """

    def _linspace(n_xels_per_dim):
        return torch.linspace(start=output_range[0], end=output_range[1], steps=n_xels_per_dim, dtype=torch.float32)

    dim_ranges = [_linspace(n_xels_per_dim) for n_xels_per_dim in index_dims]
    array_index_grid = meshgrid(*dim_ranges, indexing="ij")

    return torch.stack(array_index_grid, dim=-1)

    Generate an array of position indices for an N-D input array.

    Args:
      index_dims (`List[int]`):
        The shape of the index dimensions of the input array.
      output_range (`Tuple[float]`, *optional*, defaults to `(-1.0, 1.0)`):
        The min and max values taken by each input index dimension.

    Returns:
      `torch.FloatTensor` of shape `(index_dims[0], index_dims[1], .., index_dims[-1], N)`.
    
dpkd/transformers/src/transformers/models/perceiver/modeling_perceiver.py