code: 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)
  
imports: from typing import Optional
  import torch
  from kornia.core import Tensor, stack
  from kornia.utils._compat import torch_meshgrid
label_desc: Use image node_id 2 for calling a global function with example usage: create_meshgrid3d(depth, height, width, normalized_coordinates, device, dtype) and returns: base_grid