def create_meshgrid3d(
    depth: int,
    height: int,
    width: int,
    normalized_coordinates: bool = True,
    device: Optional[torch.device] = None,
    dtype: Optional[torch.dtype] = None,
) -> Tensor:
    """Generate a coordinate grid for an image.

    When the flag ``normalized_coordinates`` is set to True, the grid is
    normalized to be in the range :math:`[-1,1]` to be consistent with the pytorch
    function :py:func:`torch.nn.functional.grid_sample`.

    Args:
        depth: the image depth (channels).
        height: the image height (rows).
        width: the image width (cols).
        normalized_coordinates: whether to normalize
          coordinates in the range :math:`[-1,1]` in order to be consistent with the
          PyTorch function :py:func:`torch.nn.functional.grid_sample`.
        device: the device on which the grid will be generated.
        dtype: the data type of the generated grid.

    Return:
        grid tensor with shape :math:`(1, D, H, W, 3)`.

    """
    xs: Tensor = torch.linspace(0, width - 1, width, device=device, dtype=dtype)
    ys: Tensor = torch.linspace(0, height - 1, height, device=device, dtype=dtype)
    zs: Tensor = torch.linspace(0, depth - 1, depth, device=device, dtype=dtype)
    # Fix TracerWarning
    if normalized_coordinates:
        xs = (xs / (width - 1) - 0.5) * 2
        ys = (ys / (height - 1) - 0.5) * 2
        zs = (zs / (depth - 1) - 0.5) * 2
    # generate grid by stacking coordinates
    base_grid = stack(torch_meshgrid([zs, xs, ys], indexing="ij"), dim=-1)  # DxWxHx3
    return base_grid.permute(0, 2, 1, 3).unsqueeze(0)  # 1xDxHxWx3
Generate a coordinate grid for an image.

    When the flag ``normalized_coordinates`` is set to True, the grid is
    normalized to be in the range :math:`[-1,1]` to be consistent with the pytorch
    function :py:func:`torch.nn.functional.grid_sample`.

    Args:
        depth: the image depth (channels).
        height: the image height (rows).
        width: the image width (cols).
        normalized_coordinates: whether to normalize
          coordinates in the range :math:`[-1,1]` in order to be consistent with the
          PyTorch function :py:func:`torch.nn.functional.grid_sample`.
        device: the device on which the grid will be generated.
        dtype: the data type of the generated grid.

    Return:
        grid tensor with shape :math:`(1, D, H, W, 3)`.

    
kornia/utils/grid.py