code: def pad_input(
      volume,
      roi_shape,
      strides,
      padding_mode="constant",
      padding_val=-2.2,
      dim=3,
  ):
      bounds = [
          (strides[i] - volume.shape[2:][i] % strides[i]) % strides[i]
          for i in range(dim)
      ]
      bounds = [
          bounds[i]
          if (volume.shape[2:][i] + bounds[i]) >= roi_shape[i]
          else bounds[i] + strides[i]
          for i in range(dim)
      ]
      paddings = [
          bounds[2] // 2,
          bounds[2] - bounds[2] // 2,
          bounds[1] // 2,
          bounds[1] - bounds[1] // 2,
          bounds[0] // 2,
          bounds[0] - bounds[0] // 2,
          0,
          0,
          0,
          0,
      ]
      return (
          F.pad(
              torch.from_numpy(volume),
              paddings,
              mode=padding_mode,
              value=padding_val,
          ).numpy(),
          paddings,
      )
  
file_path: tinygrad/extra/datasets/kits19.py
imports: import random
  import functools
  from pathlib import Path
  import numpy
  import nibabel
  from scipy import signal
  import torch
  import torch.nn.functional
  from tinygrad.tensor import Tensor
  from tinygrad.helpers import fetch
label_desc: Use image node_id 9 for calling a global function with example usage: pad_input(volume, roi_shape, strides, padding_mode, padding_val, dim) and returns: F,  paddings