docstr: stateless_random_shuffle output is independent of input_tensor values.
docstr_tok: ["stateless_random_shuffle","output","is","independent","of","input_tensor","values","."]
code: def testOutputIsIndependentOfInputValues(self):
      """stateless_random_shuffle output is independent of input_tensor values."""
      # Generate sorted array of random numbers to control that the result
      # is independent of `input_tesnor` values
      np.random.seed(25)
      random_input = np.random.normal(size=[10])
      random_input.sort()
      for dtype in (tf.int32, tf.int64, tf.float32, tf.float64):
          # Permutation of a sequence [0, 1, .., 9]
          random_permutation = tff_rnd.stateless_random_shuffle(
              tf.range(10, dtype=dtype), seed=(100, 42)
          )
          random_permutation = self.evaluate(random_permutation)
          # Shuffle `random_input` with the same seed
          random_shuffle_control = tff_rnd.stateless_random_shuffle(
              random_input, seed=(100, 42)
          )
          random_shuffle_control = self.evaluate(random_shuffle_control)
          # Checks that the generated permutation does not depend on the underlying
          # values
          np.testing.assert_array_equal(
              np.argsort(random_permutation),
              np.argsort(random_shuffle_control),
          )
  
file_path: tf-quant-finance/tf_quant_finance/math/random_ops/stateless_test.py
imports: import numpy
  import tensorflow.compat.v2
  import tf_quant_finance
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 3 for calling the StatelessRandomOpsTest obj's underlying member method code with example usage: obj.testOutputIsIndependentOfInputValues() without return types