code: def testSamplingWithSmallStdDevFarFromBound(self):
      sample_op = random_ops.parameterized_truncated_normal(
          shape=(int(1e5),),
          means=0.8,
          stddevs=0.05,
          minvals=-1.0,
          maxvals=1.0,
      )
      new_seed = random_ops.random_uniform(
          [2], seed=1234, minval=0, maxval=(2**31 - 1), dtype=np.int32
      )
      sample_op_stateless = (
          stateless.stateless_parameterized_truncated_normal(
              shape=(int(1e5),),
              seed=new_seed,
              means=0.8,
              stddevs=0.05,
              minvals=-1.0,
              maxvals=1.0,
          )
      )
  
      with self.session() as sess:
          samples, samples_stateless = sess.run(
              [sample_op, sample_op_stateless]
          )
          # 0. is more than 16 standard deviations from the mean, and
          # should have a likelihood < 1e-57.
          assert (~np.isnan(samples)).all()
          assert (~np.isnan(samples_stateless)).all()
          self.assertAllGreater(samples, 0.0)
          self.assertAllGreater(samples_stateless, 0.0)
  
imports: import functools
  import math
  import timeit
  import numpy
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.python.client import session
  from tensorflow.python.eager import backprop
  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 control_flow_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import stateless_random_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 13 for calling the ParameterizedTruncatedNormalTest obj's underlying member method code with example usage: obj.testSamplingWithSmallStdDevFarFromBound() without return types