code: def testDistinct(self):
      for dt in get_float_types():
          sampler = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=True)
          x = sampler()
          y = sampler()
          # Number of different samples.
          count = (x == y).sum()
          if count >= 10:
              print("x = ", x)
              print("y = ", y)
              print("count = ", count)
          self.assertTrue(count < 10)
  
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 2 for calling the RandomNormalTest obj's underlying member method code with example usage: obj.testDistinct() without return types