code: def pad_func(
      img: torch.Tensor,
      to_pad: tuple[tuple[int, int]],
      transform_info: dict,
      mode: str = PytorchPadMode.CONSTANT,
      lazy: bool = False,
      **kwargs,
  ) -> torch.Tensor:
      """
      Functional implementation of padding a MetaTensor. This function operates eagerly or lazily according
      to ``lazy`` (default ``False``).
  
      `torch.nn.functional.pad` is used unless the mode or kwargs are not available in torch,
      in which case `np.pad` will be used.
  
      Args:
          img: data to be transformed, assuming `img` is channel-first and padding doesn't apply to the channel dim.
          to_pad: the amount to be padded in each dimension [(low_H, high_H), (low_W, high_W), ...].
              note that it including channel dimension.
          transform_info: a dictionary with the relevant information pertaining to an applied transform.
          mode: available modes: (Numpy) {``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
              ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
              (PyTorch) {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
              One of the listed string values or a user supplied function. Defaults to ``"constant"``.
              See also: https://numpy.org/doc/stable/reference/generated/numpy.pad.html
              https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
          lazy: a flag indicating whether the operation should be performed in a lazy fashion or not.
          transform_info: a dictionary with the relevant information pertaining to an applied transform.
          kwargs: other arguments for the `np.pad` or `torch.pad` function.
              note that `np.pad` treats channel dimension as the first dimension.
      """
      extra_info = {"padded": to_pad, "mode": f"{mode}"}
      img_size = (
          img.peek_pending_shape()
          if isinstance(img, MetaTensor)
          else img.shape[1:]
      )
      spatial_rank = (
          img.peek_pending_rank() if isinstance(img, MetaTensor) else 3
      )
      do_pad = np.asarray(to_pad).any()
      if do_pad:
          to_pad_list = [(int(p[0]), int(p[1])) for p in to_pad]
          if len(to_pad_list) < len(img.shape):
              to_pad_list += [(0, 0)] * (
                  len(img.shape) - len(to_pad_list)
              )
          to_shift = [
              -s[0] for s in to_pad_list[1:]
          ]  # skipping the channel pad
          xform = create_translate(spatial_rank, to_shift)
          shape = [
              d + s + e for d, (s, e) in zip(img_size, to_pad_list[1:])
          ]
      else:
          shape = img_size
          xform = torch.eye(
              int(spatial_rank) + 1,
              device=torch.device("cpu"),
              dtype=torch.float64,
          )
      meta_info = TraceableTransform.track_transform_meta(
          img,
          sp_size=shape,
          affine=xform,
          extra_info=extra_info,
          orig_size=img_size,
          transform_info=transform_info,
          lazy=lazy,
      )
      out = convert_to_tensor(
          img.as_tensor() if isinstance(img, MetaTensor) else img,
          track_meta=get_track_meta(),
      )
      if lazy:
          return out.copy_meta_from(meta_info) if isinstance(out, MetaTensor) else meta_info  # type: ignore
      out = pad_nd(out, to_pad_list, mode, **kwargs) if do_pad else out
      out = convert_to_tensor(out, track_meta=get_track_meta())
      return (
          out.copy_meta_from(meta_info)
          if isinstance(out, MetaTensor)
          else out
      )
  
file_path: MONAI/monai/transforms/croppad/functional.py
imports: from __future__ import annotations
  import warnings
  import numpy
  import torch
  from torch.nn.functional import pad
  from monai.config.type_definitions import NdarrayTensor
  from monai.data.meta_obj import get_track_meta
  from monai.data.meta_tensor import MetaTensor
  from monai.data.utils import to_affine_nd
  from monai.transforms.inverse import TraceableTransform
  from monai.transforms.utils import convert_pad_mode, create_translate
  from monai.utils import PytorchPadMode, convert_to_dst_type, convert_to_numpy, convert_to_tensor, ensure_tuple
label_desc: Use image node_id 6 for calling a global function with example usage: pad_func(img, to_pad, transform_info, mode, lazy) and returns: unknown, unknown