code: def setUp(self):
      import torch
      from torch.utils.data import DataLoader
  
      master_seed(seed=42)
  
      class DummyDataset(torch.utils.data.Dataset):
          def __init__(self):
              self._size = 10
              self._x = np.random.rand(self._size, 1, 5, 5)
              self._y = np.random.randint(0, high=10, size=self._size)
  
          def __len__(self):
              return self._size
  
          def __getitem__(self, idx):
              return self._x[idx], self._y[idx]
  
      dataset = DummyDataset()
      data_loader = DataLoader(
          dataset=dataset, batch_size=5, shuffle=True
      )
      self.data_gen = PyTorchDataGenerator(
          data_loader, size=10, batch_size=5
      )
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import unittest
  import tensorflow
  import numpy
  from keras.preprocessing.image import ImageDataGenerator
  from art.data_generators import KerasDataGenerator, PyTorchDataGenerator, MXDataGenerator, TensorFlowDataGenerator
  from art.data_generators import TensorFlowV2DataGenerator, NumpyDataGenerator
  from tests.utils import master_seed
label_desc: Use image node_id 1 for calling the TestPyTorchGenerator obj's underlying member method code with example usage: obj.setUp() and returns: self, self,  self