code: def make_datasets(data_table, n_classes):
      n_samples = len(data_table.data)
      # n_classes = len(BDD_CLASSES)
      height = data_table.data[0][1].image.height
      width = data_table.data[0][1].image.width
  
      train_data = np.array(
          [
              np.array(data_table.data[i][1].image).reshape(
                  height, width, 3
              )
              for i in range(n_samples)
          ]
      )
      mask_data = np.array(
          [
              np.array(data_table.data[i][3].image).reshape(
                  height, width
              )
              for i in range(n_samples)
          ]
      )
      return train_data, mask_data
  
imports: import os
  import pickle
  import shutil
  import time
  import numpy
  import wandb
  from PIL import Image
label_desc: Use image node_id 17 for calling a global function with example usage: make_datasets(data_table, n_classes) and returns: train_data,  mask_data