code: def get_datasets(initial_pool):
      transform = transforms.Compose(
          [
              transforms.Resize((224, 224)),
              transforms.RandomHorizontalFlip(),
              transforms.RandomRotation(30),
              transforms.ToTensor(),
              transforms.Normalize(3 * [0.5], 3 * [0.5]),
          ]
      )
      test_transform = transforms.Compose(
          [
              transforms.Resize((224, 224)),
              transforms.ToTensor(),
              transforms.Normalize(3 * [0.5], 3 * [0.5]),
          ]
      )
      # Note: We use the test set here as an example. You should make your own validation set.
      train_ds = datasets.CIFAR10(
          ".",
          train=True,
          transform=transform,
          target_transform=None,
          download=True,
      )
      test_set = datasets.CIFAR10(
          ".",
          train=False,
          transform=test_transform,
          target_transform=None,
          download=True,
      )
  
      active_set = ActiveLearningDataset(
          train_ds, pool_specifics={"transform": test_transform}
      )
  
      # We start labeling randomly.
      active_set.label_randomly(initial_pool)
      return active_set, test_set
  
file_path: baal/experiments/vgg_mcdropout_cifar10.py
imports: import argparse
  from pprint import pprint
  import random
  from copy import deepcopy
  import torch
  import torch.backends
  from torch import optim
  from torch.hub import load_state_dict_from_url
  from torch.nn import CrossEntropyLoss
  from torchvision import datasets
  from torchvision.models import vgg16
  from torchvision.transforms import transforms
  from tqdm import tqdm
  from baal.active import get_heuristic, ActiveLearningDataset
  from baal.active.active_loop import ActiveLearningLoop
  from baal.bayesian.dropout import patch_module
  from baal import ModelWrapper
label_desc: Use image node_id 2 for calling a global function with example usage: get_datasets(initial_pool) and returns: active_set,  test_set