code: def testDistinct(self):
      # NOTE: TruncatedNormal on GPU is not supported.
      if not test.is_gpu_available():
          for dt in get_float_types():
              sampler = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=False)
              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 TruncatedNormalTest obj's underlying member method code with example usage: obj.testDistinct() without return types