code: def show_transform(
      image_path: str, transform: Any, sample_num: int = 6
  ):
      """
      Show the result which `torchvision.transforms` or any other callable function act on the image.
      Only the Image transforms is supported. Such as `ToTensor` or `Normalize` is invalid.
      Args:
          image_path (`str`):
              The original image path.
          transform (`Any`):
              torchvision.transforms` or any other callable function.
          sample_num (`int`):
              Sample number.
  
      """
      if not is_matplotlib_available():
          trainer_log.warning("Matplotlib is not available.")
      from towhee.utils.matplotlib_utils import matplotlib
      import matplotlib.pylab as plt  # pylint: disable=import-outside-toplevel
  
      plt.rcParams["savefig.bbox"] = "tight"
      orig_img = Image.open(image_path)
      trans_img_list = [transform(orig_img) for _ in range(sample_num)]
      _plot_transform(orig_img, trans_img_list)
  
imports: from typing import Optional, List, Any, Tuple
  from torchvision import transforms
  from torchvision.io import read_image
  from pathlib import Path
  from torch import nn
  from towhee.utils.pil_utils import PILImage
  from towhee.trainer.optimization.optimization import get_scheduler, get_warmup_steps
  from towhee.trainer.utils.trainer_utils import _construct_scheduler_from_config
  from towhee.trainer.training_config import TrainingConfig
  from towhee.trainer.utils.file_utils import is_captum_available, is_matplotlib_available
  from towhee.utils.log import trainer_log
  import torch
  import torch.nn.functional
  import numpy
  import random
  import os
  import copy
label_desc: Use image node_id 4 for calling a global function with example usage: show_transform(image_path, transform, sample_num) without return types