code: def torch_to_numpy(data: Dataset) -> List[np.ndarray]:
      """Convert data from torch dataset to list of numpy arrays [input, target]."""
  
      # Create empty numpy arrays.
      images_shape = (len(data), *data[0][0].shape)
      images = np.zeros(images_shape)
      labels = np.zeros(len(data))
  
      # Fill arrays with samples from torch dataset.
      # Note that samples in torch datasets may change from iteration to iteration
      # because of random transforms.
      # TODO: What to do if data is too large for memory?
      for i, (image, label) in enumerate(data):
          images[i] = image
          labels[i] = label
      return [images, labels]
  
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 8 for calling a global function with example usage: torch_to_numpy(data) and returns: list