code: def create_transform(
      from_numpy: bool = False,
      resize: int = None,
      crop: int = None,
      mean: List = None,
      std: List = None,
  ):
      """Creates a torchvision transform to convert images from PIL or numpy."""
      t = []
      if from_numpy:
          t.append(transforms.ToPILImage())
      if resize is not None:
          t.append(transforms.Resize(resize))
      if crop is not None:
          t.append(transforms.CenterCrop(crop))
      t.append(transforms.ToTensor())
      if mean is not None and std is not None:
          t.append(
              transforms.Normalize(mean=mean, std=std),
          )
      return transforms.Compose(t)
  
imports: import numpy
  import torch
  from torch.utils.data import Dataset
  from torchvision import datasets, transforms
  from typing import List, Union
  from pathlib import Path
  from PIL import Image
label_desc: Use image node_id 11 for calling a global function with example usage: create_transform(from_numpy, resize, crop, mean, std) and returns: transforms