code: def set_random_seed(seed):
      """Sets all random seeds (Python, NumPy, and backend framework, e.g. TF).
  
      You can use this utility to make almost any Keras program fully
      deterministic. Some limitations apply in cases where network communications
      are involved (e.g. parameter server distribution), which creates additional
      sources of randomness, or when certain non-deterministic cuDNN ops are
      involved.
  
      Calling this utility is equivalent to the following:
  
      ```python
      import random
      import numpy as np
      from keras.utils.module_utils import tensorflow as tf
      random.seed(seed)
      np.random.seed(seed)
      tf.random.set_seed(seed)
      ```
  
      Note that the TensorFlow seed is set even if you're not using TensorFlow
      as your backend framework, since many workflows leverage `tf.data`
      pipelines (which feature random shuffling). Likewise many workflows
      might leverage NumPy APIs.
  
      Arguments:
          seed: Integer, the random seed to use.
      """
      if not isinstance(seed, int):
          raise ValueError(
              "Expected `seed` argument to be an integer. "
              f"Received: seed={seed} (of type {type(seed)})"
          )
      random.seed(seed)
      np.random.seed(seed)
      if tf.available:
          tf.random.set_seed(seed)
      if backend.backend() == "torch":
          import torch
  
          torch.manual_seed(seed)
  
imports: import random
  import numpy
  from keras import backend
  from keras.api_export import keras_export
  from keras.utils.module_utils import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: set_random_seed(seed) without return types