code: def create_transform_im(
      transform,
      transform_args,
      data,
      ndim=3,
      colorbar=False,
      update_doc=True,
      seed=0,
      is_post=False,
  ):
      """Create an image with the before and after of the transform.
      Also update the transform's documentation to point to this image.
      """
  
      transform = transform(**transform_args)
  
      if not has_matplotlib:
          raise RuntimeError
  
      if isinstance(transform, Randomizable):
          # increment the seed for map transforms so they're different to the array versions.
          seed = (
              seed + 1 if isinstance(transform, MapTransform) else seed
          )
          transform.set_random_state(seed)
  
      out_dir = MONAIEnvVars.doc_images()
      if out_dir is None:
          raise RuntimeError(
              "Please git clone https://github.com/Project-MONAI/DocImages"
              + " and then set the environment variable `MONAI_DOC_IMAGES`"
          )
      out_dir = os.path.join(out_dir, "transforms")
  
      # Path is transform name
      transform_name = transform.__class__.__name__
      out_fname = transform_name + ".png"
      out_file = os.path.join(out_dir, out_fname)
  
      is_map = isinstance(transform, MapTransform)
      data_in = pre_process_data(deepcopy(data), ndim, is_map, is_post)
  
      data_tr = transform(deepcopy(data_in))
  
      images_before, before_shape = get_images(data_in)
      images_after, after_shape = get_images(data_tr)
      images = (images_before, images_after)
      shapes = (before_shape, after_shape)
  
      labels = None
      if is_map:
          labels_before, *_ = get_images(data_in, is_label=True)
          labels_after, *_ = get_images(data_tr, is_label=True)
          labels = (labels_before, labels_after)
  
      save_image(
          images,
          labels,
          out_file,
          transform_name,
          transform_args,
          shapes,
          colorbar,
      )
  
      if update_doc:
          base_dir = pathlib.Path(__file__).parent.parent.parent
          rst_path = os.path.join(
              base_dir, "docs", "source", "transforms.rst"
          )
          update_docstring(rst_path, transform_name)
  
imports: from __future__ import annotations
  import os
  import pathlib
  import tempfile
  import textwrap
  from copy import deepcopy
  from glob import glob
  from typing import TYPE_CHECKING
  import numpy
  import torch
  from monai.apps import download_and_extract
  from monai.transforms import Affine, Affined, AsDiscrete, Compose, EnsureChannelFirstd, Flip, Flipd, LoadImaged, MapTransform, Orientation, Orientationd, Rand3DElastic, Rand3DElasticd, RandFlip, RandFlipd, Randomizable, RandRotate, RandRotated, RandZoom, RandZoomd, Rotate, Rotate90, Rotate90d, Rotated, ScaleIntensity, ScaleIntensityd, SpatialPadd, Zoom, Zoomd
  from monai.transforms.croppad.array import BorderPad, CenterScaleCrop, CenterSpatialCrop, CropForeground, DivisiblePad, RandCropByLabelClasses, RandCropByPosNegLabel, RandScaleCrop, RandSpatialCrop, RandSpatialCropSamples, RandWeightedCrop, ResizeWithPadOrCrop, SpatialCrop, SpatialPad
  from monai.transforms.croppad.dictionary import BorderPadd, CenterScaleCropd, CenterSpatialCropd, CropForegroundd, DivisiblePadd, RandCropByLabelClassesd, RandCropByPosNegLabeld, RandScaleCropd, RandSpatialCropd, RandSpatialCropSamplesd, RandWeightedCropd, ResizeWithPadOrCropd, SpatialCropd
  from monai.transforms.intensity.array import AdjustContrast, ForegroundMask, GaussianSharpen, GaussianSmooth, GibbsNoise, HistogramNormalize, KSpaceSpikeNoise, MaskIntensity, MedianSmooth, NormalizeIntensity, RandAdjustContrast, RandBiasField, RandCoarseDropout, RandCoarseShuffle, RandGaussianNoise, RandGaussianSharpen, RandGaussianSmooth, RandGibbsNoise, RandHistogramShift, RandKSpaceSpikeNoise, RandRicianNoise, RandScaleIntensity, RandShiftIntensity, RandStdShiftIntensity, SavitzkyGolaySmooth, ScaleIntensityRange, ScaleIntensityRangePercentiles, ShiftIntensity, StdShiftIntensity, ThresholdIntensity
  from monai.transforms.intensity.dictionary import AdjustContrastd, ForegroundMaskd, GaussianSharpend, GaussianSmoothd, GibbsNoised, HistogramNormalized, KSpaceSpikeNoised, MaskIntensityd, MedianSmoothD, NormalizeIntensityd, RandAdjustContrastd, RandBiasFieldd, RandCoarseDropoutd, RandCoarseShuffled, RandGaussianNoised, RandGaussianSharpend, RandGaussianSmoothd, RandGibbsNoised, RandHistogramShiftd, RandKSpaceSpikeNoised, RandRicianNoised, RandScaleIntensityd, RandShiftIntensityd, RandStdShiftIntensityd, SavitzkyGolaySmoothd, ScaleIntensityRanged, ScaleIntensityRangePercentilesd, ShiftIntensityd, StdShiftIntensityd, ThresholdIntensityd
  from monai.transforms.post.array import KeepLargestConnectedComponent, LabelFilter, LabelToContour, RemoveSmallObjects
  from monai.transforms.post.dictionary import AsDiscreted, KeepLargestConnectedComponentd, LabelFilterd, LabelToContourd, RemoveSmallObjectsd
  from monai.transforms.smooth_field.array import RandSmoothDeform, RandSmoothFieldAdjustContrast, RandSmoothFieldAdjustIntensity
  from monai.transforms.smooth_field.dictionary import RandSmoothDeformd, RandSmoothFieldAdjustContrastd, RandSmoothFieldAdjustIntensityd
  from monai.transforms.spatial.array import GridDistortion, Rand2DElastic, RandAffine, RandAxisFlip, RandGridDistortion, RandRotate90, Resize, Spacing
  from monai.transforms.spatial.dictionary import GridDistortiond, Rand2DElasticd, RandAffined, RandAxisFlipd, RandGridDistortiond, RandRotate90d, Resized, Spacingd
  from monai.utils.enums import CommonKeys
  from monai.utils.misc import MONAIEnvVars
  from monai.utils.module import optional_import
label_desc: Use image node_id 9 for calling a global function with example usage: create_transform_im(transform, transform_args, data, ndim, colorbar, update_doc, seed, is_post) without return types