code: def testNormalQuantile(self):
      batch_size = 52
      mu = self._rng.randn(batch_size)
      sigma = self._rng.rand(batch_size) + 1.0
      p = np.linspace(0.0, 1.0, batch_size - 2).astype(np.float64)
      # Quantile performs piecewise rational approximation so adding some
      # special input values to make sure we hit all the pieces.
      p = np.hstack((p, np.exp(-33), 1.0 - np.exp(-33)))
  
      normal = normal_lib.Normal(loc=mu, scale=sigma)
      x = normal.quantile(p)
  
      self.assertAllEqual(
          self.evaluate(normal.batch_shape_tensor()), x.get_shape()
      )
      self.assertAllEqual(
          self.evaluate(normal.batch_shape_tensor()),
          self.evaluate(x).shape,
      )
      self.assertAllEqual(normal.batch_shape, x.get_shape())
      self.assertAllEqual(normal.batch_shape, self.evaluate(x).shape)
  
      if not stats:
          return
      expected_x = stats.norm(mu, sigma).ppf(p)
      self.assertAllClose(expected_x, self.evaluate(x), atol=0.0)
  
imports: import importlib
  import math
  import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.distributions import kullback_leibler
  from tensorflow.python.ops.distributions import normal
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 19 for calling the NormalTest obj's underlying member method code with example usage: obj.testNormalQuantile() without return types