code: def crop_func(
      img: torch.Tensor,
      slices: tuple[slice, ...],
      lazy: bool,
      transform_info: dict,
  ) -> torch.Tensor:
      """
      Functional implementation of cropping a MetaTensor. This function operates eagerly or lazily according
      to ``lazy`` (default ``False``).
  
      Args:
          img: data to be transformed, assuming `img` is channel-first and cropping doesn't apply to the channel dim.
          slices: the crop slices computed based on specified `center & size` or `start & end` or `slices`.
          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.
      """
      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
      )
      cropped = np.asarray(
          [
              [s.indices(o)[0], o - s.indices(o)[1]]
              for s, o in zip(slices[1:], img_size)
          ]
      )
      extra_info = {"cropped": cropped.flatten().tolist()}
      to_shift = []
      for i, s in enumerate(ensure_tuple(slices)[1:]):
          if s.start is not None:
              to_shift.append(
                  img_size[i] + s.start if s.start < 0 else s.start
              )
          else:
              to_shift.append(0)
      shape = [
          s.indices(o)[1] - s.indices(o)[0]
          for s, o in zip(slices[1:], img_size)
      ]
      meta_info = TraceableTransform.track_transform_meta(
          img,
          sp_size=shape,
          affine=create_translate(spatial_rank, to_shift),
          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 = out[slices]
      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 7 for calling a global function with example usage: crop_func(img, slices, lazy, transform_info) and returns: unknown, unknown