code: def flip(img, sp_axes, lazy, transform_info):
      """
      Functional implementation of flip.
      This function operates eagerly or lazily according to
      ``lazy`` (default ``False``).
  
      Args:
          img: data to be changed, assuming `img` is channel-first.
          sp_axes: spatial axes along which to flip over.
              If None, will flip over all of the axes of the input array.
              If axis is negative it counts from the last to the first axis.
              If axis is a tuple of ints, flipping is performed on all of the axes
              specified in the tuple.
          lazy: a flag that indicates whether the operation should be performed lazily or not
          transform_info: a dictionary with the relevant information pertaining to an applied transform.
      """
      sp_size = (
          img.peek_pending_shape()
          if isinstance(img, MetaTensor)
          else img.shape[1:]
      )
      sp_size = convert_to_numpy(sp_size, wrap_sequence=True).tolist()
      extra_info = {"axes": sp_axes}  # track the spatial axes
      axes = monai.transforms.utils.map_spatial_axes(
          img.ndim, sp_axes
      )  # use the axes with channel dim
      rank = (
          img.peek_pending_rank()
          if isinstance(img, MetaTensor)
          else torch.tensor(3.0, dtype=torch.double)
      )
      # axes include the channel dim
      xform = torch.eye(int(rank) + 1, dtype=torch.double)
      for axis in axes:
          sp = axis - 1
          xform[sp, sp], xform[sp, -1] = (
              xform[sp, sp] * -1,
              sp_size[sp] - 1,
          )
      meta_info = TraceableTransform.track_transform_meta(
          img,
          sp_size=sp_size,
          affine=xform,
          extra_info=extra_info,
          transform_info=transform_info,
          lazy=lazy,
      )
      out = _maybe_new_metatensor(img)
      if lazy:
          return (
              out.copy_meta_from(meta_info)
              if isinstance(out, MetaTensor)
              else meta_info
          )
      out = torch.flip(out, axes)
      return (
          out.copy_meta_from(meta_info)
          if isinstance(out, MetaTensor)
          else out
      )
  
imports: from __future__ import annotations
  import math
  import warnings
  from enum import Enum
  import numpy
  import torch
  import monai
  from monai.config import USE_COMPILED
  from monai.config.type_definitions import NdarrayOrTensor
  from monai.data.meta_obj import get_track_meta
  from monai.data.meta_tensor import MetaTensor
  from monai.data.utils import AFFINE_TOL, compute_shape_offset, to_affine_nd
  from monai.networks.layers import AffineTransform
  from monai.transforms.croppad.array import ResizeWithPadOrCrop
  from monai.transforms.intensity.array import GaussianSmooth
  from monai.transforms.inverse import TraceableTransform
  from monai.transforms.utils import create_rotate, create_translate, resolves_modes, scale_affine
  from monai.transforms.utils_pytorch_numpy_unification import allclose
  from monai.utils import LazyAttr, TraceKeys, convert_to_dst_type, convert_to_numpy, convert_to_tensor, ensure_tuple, ensure_tuple_rep, fall_back_tuple, optional_import
label_desc: Use image node_id 4 for calling a global function with example usage: flip(img, sp_axes, lazy, transform_info) and returns: unknown, unknown