code: def set_random_seed(seed):
      """Sets the graph-level random seed for the default graph.
  
      Operations that rely on a random seed actually derive it from two seeds:
      the graph-level and operation-level seeds. This sets the graph-level seed.
  
      Its interactions with operation-level seeds is as follows:
  
        1. If neither the graph-level nor the operation seed is set:
          A random seed is used for this op.
        2. If the graph-level seed is set, but the operation seed is not:
          The system deterministically picks an operation seed in conjunction with
          the graph-level seed so that it gets a unique random sequence. Within the
          same version of tensorflow and user code, this sequence is deterministic.
          However across different versions, this sequence might change. If the
          code depends on particular seeds to work, specify both graph-level
          and operation-level seeds explicitly.
        3. If the graph-level seed is not set, but the operation seed is set:
          A default graph-level seed and the specified operation seed are used to
          determine the random sequence.
        4. If both the graph-level and the operation seed are set:
          Both seeds are used in conjunction to determine the random sequence.
  
      To illustrate the user-visible effects, consider these examples:
  
      To generate different sequences across sessions, set neither
      graph-level nor op-level seeds:
  
      ```python
      a = tf.random.uniform([1])
      b = tf.random.normal([1])
  
      print("Session 1")
      with tf.compat.v1.Session() as sess1:
        print(sess1.run(a))  # generates 'A1'
        print(sess1.run(a))  # generates 'A2'
        print(sess1.run(b))  # generates 'B1'
        print(sess1.run(b))  # generates 'B2'
  
      print("Session 2")
      with tf.compat.v1.Session() as sess2:
        print(sess2.run(a))  # generates 'A3'
        print(sess2.run(a))  # generates 'A4'
        print(sess2.run(b))  # generates 'B3'
        print(sess2.run(b))  # generates 'B4'
      ```
  
      To generate the same repeatable sequence for an op across sessions, set the
      seed for the op:
  
      ```python
      a = tf.random.uniform([1], seed=1)
      b = tf.random.normal([1])
  
      # Repeatedly running this block with the same graph will generate the same
      # sequence of values for 'a', but different sequences of values for 'b'.
      print("Session 1")
      with tf.compat.v1.Session() as sess1:
        print(sess1.run(a))  # generates 'A1'
        print(sess1.run(a))  # generates 'A2'
        print(sess1.run(b))  # generates 'B1'
        print(sess1.run(b))  # generates 'B2'
  
      print("Session 2")
      with tf.compat.v1.Session() as sess2:
        print(sess2.run(a))  # generates 'A1'
        print(sess2.run(a))  # generates 'A2'
        print(sess2.run(b))  # generates 'B3'
        print(sess2.run(b))  # generates 'B4'
      ```
  
      To make the random sequences generated by all ops be repeatable across
      sessions, set a graph-level seed:
  
      ```python
      tf.compat.v1.random.set_random_seed(1234)
      a = tf.random.uniform([1])
      b = tf.random.normal([1])
  
      # Repeatedly running this block with the same graph will generate the same
      # sequences of 'a' and 'b'.
      print("Session 1")
      with tf.compat.v1.Session() as sess1:
        print(sess1.run(a))  # generates 'A1'
        print(sess1.run(a))  # generates 'A2'
        print(sess1.run(b))  # generates 'B1'
        print(sess1.run(b))  # generates 'B2'
  
      print("Session 2")
      with tf.compat.v1.Session() as sess2:
        print(sess2.run(a))  # generates 'A1'
        print(sess2.run(a))  # generates 'A2'
        print(sess2.run(b))  # generates 'B1'
        print(sess2.run(b))  # generates 'B2'
      ```
  
      @compatibility(TF2)
      'tf.compat.v1.set_random_seed' is compatible with eager mode. However,
      in eager mode this API will set the global seed instead of the
      graph-level seed of the default graph. In TF2 this API is changed to
      [tf.random.set_seed]
      (https://www.tensorflow.org/api_docs/python/tf/random/set_seed).
      @end_compatibility
  
      Args:
        seed: integer.
      """
      if context.executing_eagerly():
          context.set_global_seed(seed)
      else:
          ops.get_default_graph().seed = seed
  
file_path: tensorflow/tensorflow/python/framework/random_seed.py
imports: import weakref
  from tensorflow.python.eager import context
  from tensorflow.python.framework import config
  from tensorflow.python.framework import ops
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 3 for calling a global function with example usage: set_random_seed(seed) without return types