code: def pad_tensor_to_size(
      x: Tensor, size: Tuple[int, ...], mode="constant", value=0
  ) -> Tuple[Tensor, Tuple[slice, ...]]:
      """
      Pad tensor to given size by appending elements to the beginning and end of each axis.
  
      :param x: Input tensor of shape [B, C, *num_spatial_dims]
      :param size: Target tensor size defined as an array of [num_spatial_dims] elements
      :param mode: Padding mode, see torch.nn.functional.pad
      :param value: Padding value, see torch.nn.functional.pad
      :return: Tuple of padded tensor and crop parameters. Second argument can be used to reverse pad operation of model output
      """
      num_spatial_dims = len(size)
      if num_spatial_dims != len(x.shape) - 2:
          raise ValueError(
              f"Expected {num_spatial_dims} spatial dimensions, got {len(x.shape) - 2}"
          )
  
      spatial_dims = x.shape[-num_spatial_dims:]
      padding = torch.tensor(size) - torch.tensor(spatial_dims)
      padding_before = padding // 2
      padding_after = padding - padding_before
  
      padding_pairs = tuple(
          zip(padding_before.tolist(), padding_after.tolist())
      )
      padding_params = tuple(itertools.chain(*reversed(padding_pairs)))
  
      x = torch.nn.functional.pad(
          x, pad=padding_params, mode=mode, value=value
      )
  
      crop_params = [slice(None), slice(None)] + [
          slice(before, before + total_size)
          for (before, after, total_size) in zip(
              padding_before, padding_after, spatial_dims
          )
      ]
      return x, crop_params
  
imports: import itertools
  from collections.abc import Sized, Iterable
  from typing import Union, Tuple
  import torch
  from torch import Tensor
  from ..utils.support import pytorch_toolbelt_deprecated
label_desc: Use image node_id 18 for calling a global function with example usage: pad_tensor_to_size(x, size, mode, value) and returns: x,  crop_params