code: def random_signs(num_elements, seed, dtype=tf.float32):
      """Returns a Tensor of `num_elements` random +1/-1 values as `dtype`.
  
      If run twice with the same seeds, it will produce the same pseudorandom
      numbers. The output is consistent across multiple runs on the same hardware
      (and between CPU and GPU), but may change between versions of TensorFlow or
      on non-CPU/GPU hardware.
  
      If consistency is required, use `random_signs_cmwc` instead.
  
      Args:
        num_elements: A Python integer. The number of random values to be generated.
        seed: A shape [2] integer Tensor of seeds to the random number generator.
        dtype: The type of the output.
  
      Returns:
        A Tensor of `num_elements` random +1/-1 values as `dtype`.
      """
      return tf.cast(
          tf.sign(
              tf.random.stateless_uniform([num_elements], seed) - 0.5
          ),
          dtype,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import logging
  import math
  import numpy
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: random_signs(num_elements, seed, dtype) and returns: tf