code: def master_seed(
      seed=1234,
      set_random=True,
      set_numpy=True,
      set_tensorflow=False,
      set_mxnet=False,
      set_torch=False,
  ):
      """
      Set the seed for all random number generators used in the library. This ensures experiments reproducibility and
      stable testing.
  
      :param seed: The value to be seeded in the random number generators.
      :type seed: `int`
      :param set_random: The flag to set seed for `random`.
      :type set_random: `bool`
      :param set_numpy: The flag to set seed for `numpy`.
      :type set_numpy: `bool`
      :param set_tensorflow: The flag to set seed for `tensorflow`.
      :type set_tensorflow: `bool`
      :param set_mxnet: The flag to set seed for `mxnet`.
      :type set_mxnet: `bool`
      :param set_torch: The flag to set seed for `torch`.
      :type set_torch: `bool`
      """
      import numbers
  
      if not isinstance(seed, numbers.Integral):
          raise TypeError(
              "The seed for random number generators has to be an integer."
          )
  
      # Set Python seed
      if set_random:
          import random
  
          random.seed(seed)
  
      # Set Numpy seed
      if set_numpy:
          np.random.seed(seed)
          np.random.RandomState(seed)
  
      # Now try to set seed for all specific frameworks
      if set_tensorflow:
          try:
              import tensorflow as tf
  
              logger.info("Setting random seed for TensorFlow.")
              if tf.__version__[0] == "2":
                  tf.random.set_seed(seed)
              else:
                  tf.set_random_seed(seed)
          except ImportError:
              logger.info("Could not set random seed for TensorFlow.")
  
      if set_mxnet:
          try:
              import mxnet as mx
  
              logger.info("Setting random seed for MXNet.")
              mx.random.seed(seed)
          except ImportError:
              logger.info("Could not set random seed for MXNet.")
  
      if set_torch:
          try:
              logger.info("Setting random seed for PyTorch.")
              import torch
  
              torch.manual_seed(seed)
              if torch.cuda.is_available():
                  torch.cuda.manual_seed_all(seed)
          except ImportError:
              logger.info("Could not set random seed for PyTorch.")
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import json
  import logging
  import os
  import pickle
  import time
  import unittest
  import warnings
  import sklearn
  import numpy
  from art.estimators.classification.tensorflow import TensorFlowV2Classifier
  from art.estimators.encoding.tensorflow import TensorFlowEncoder
  from art.estimators.generation.tensorflow import TensorFlowGenerator, TensorFlowV2Generator
  from art.estimators.gan.tensorflow import TensorFlowV2GAN
  from art.utils import load_dataset
label_desc: Use image node_id 35 for calling a global function with example usage: master_seed(seed, set_random, set_numpy, set_tensorflow, set_mxnet, set_torch) without return types