code: def normal_transform_pixel3d(
      depth: int,
      height: int,
      width: int,
      eps: float = 1e-14,
      device: Optional[torch.device] = None,
      dtype: Optional[torch.dtype] = None,
  ) -> Tensor:
      r"""Compute the normalization matrix from image size in pixels to [-1, 1].
  
      Args:
          depth: image depth.
          height: image height.
          width: image width.
          eps: epsilon to prevent divide-by-zero errors
  
      Returns:
          normalized transform with shape :math:`(1, 4, 4)`.
      """
      tr_mat = tensor(
          [
              [1.0, 0.0, 0.0, -1.0],
              [0.0, 1.0, 0.0, -1.0],
              [0.0, 0.0, 1.0, -1.0],
              [0.0, 0.0, 0.0, 1.0],
          ],
          device=device,
          dtype=dtype,
      )  # 4x4
  
      # prevent divide by zero bugs
      width_denom: float = eps if width == 1 else width - 1.0
      height_denom: float = eps if height == 1 else height - 1.0
      depth_denom: float = eps if depth == 1 else depth - 1.0
  
      tr_mat[0, 0] = tr_mat[0, 0] * 2.0 / width_denom
      tr_mat[1, 1] = tr_mat[1, 1] * 2.0 / height_denom
      tr_mat[2, 2] = tr_mat[2, 2] * 2.0 / depth_denom
  
      return tr_mat.unsqueeze(0)
  
imports: from __future__ import annotations
  from typing import Optional
  import torch
  import torch.nn.functional
  from kornia.constants import pi
  from kornia.core import Tensor, concatenate, cos, pad, sin, stack, tensor, where, zeros_like
  from kornia.core.check import KORNIA_CHECK, KORNIA_CHECK_SHAPE
  from kornia.utils import deprecated
  from kornia.utils.helpers import _torch_inverse_cast
label_desc: Use image node_id 32 for calling a global function with example usage: normal_transform_pixel3d(depth, height, width, eps, device, dtype) and returns: tr_mat