code: def _reflection_pad_3d(
      inputs: Tensor, pad_h: int, pad_w: int, pad_d: int
  ) -> Tensor:
      """Reflective padding of 3d input.
  
      Args:
          inputs: tensor to pad, should be a 3D tensor of shape ``[N, C, H, W, D]``
          pad_w: amount of padding in the height dimension
          pad_h: amount of padding in the width dimension
          pad_d: amount of padding in the depth dimension
  
      Returns:
          padded input tensor
  
      """
      return F.pad(
          inputs,
          (pad_h, pad_h, pad_w, pad_w, pad_d, pad_d),
          mode="reflect",
      )
  
file_path: torchmetrics/src/torchmetrics/functional/image/helper.py
imports: from typing import Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from torch.nn import functional
label_desc: Use image node_id 8 for calling a global function with example usage: _reflection_pad_3d(inputs, pad_h, pad_w, pad_d) and returns: F