class_docstr: Test cases for random-number generating operators.
code: def testParameterizedTruncatedNormalBatched(self):
      # TODO(b/112289993): Make this test work with dtype np.float64.
      for dtype in self._random_types() & {np.float32}:
          with self.session():
              with self.test_scope():
                  count = 10000000
                  a = -100.0
                  b = 100.0
                  mu0 = 0.0
                  mu1 = 1.0
                  sigma = 0.1
                  x = random_ops.parameterized_truncated_normal(
                      shape=[2, count],
                      dtype=dtype,
                      means=[mu0, mu1],
                      stddevs=sigma,
                      minvals=[a],
                      maxvals=[b],
                  )
              self._checkTruncatedNormalIsInRange(
                  x[0],
                  a=a,
                  b=b,
                  mu=mu0,
                  sigma=sigma,
                  count=count,
                  stat_test=True,
              )
              self._checkTruncatedNormalIsInRange(
                  x[1],
                  a=a,
                  b=b,
                  mu=mu1,
                  sigma=sigma,
                  count=count,
                  stat_test=True,
              )
  
imports: import math
  from absl.testing import parameterized
  import numpy
  from tensorflow.compiler.tests import xla_test
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops.distributions import special_math
  from tensorflow.python.platform import googletest
label_desc: Use image node_id 12 for calling the RandomOpsTest obj's underlying member method code with example usage: obj.testParameterizedTruncatedNormalBatched() without return types