code: def _testSingleSessionNotConstant(
      self,
      rng_func,
      num,
      dtype,
      min_or_mean,
      max_or_stddev,
      use_gpu,
      op_seed=None,
      graph_seed=None,
  ):
      with self.session(use_gpu=use_gpu, graph=ops.Graph()) as sess:
          if graph_seed is not None:
              random_seed.set_random_seed(graph_seed)
          x = rng_func(
              [num],
              min_or_mean,
              max_or_stddev,
              dtype=dtype,
              seed=op_seed,
          )
  
          y = self.evaluate(x)
          z = self.evaluate(x)
          w = self.evaluate(x)
  
          # We use exact equality here. If the random-number generator is producing
          # the same output, all three outputs will be bitwise identical.
          self.assertTrue(
              (not np.array_equal(y, z))
              or (not np.array_equal(z, w))
              or (not np.array_equal(y, w))
          )
  
imports: import numpy
  from tensorflow.python.eager import context
  from tensorflow.python.framework import config
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_random_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling the RandomOpTestCommon obj's underlying member method code with example usage: obj._testSingleSessionNotConstant(rng_func, num, dtype, min_or_mean, max_or_stddev, use_gpu, op_seed, graph_seed) without return types