docstr: Verifies that ndtri computation is correct.
code: def testNdtri(self):
      """Verifies that ndtri computation is correct."""
      if not special:
          return
  
      p = np.linspace(0.0, 1.0, 50).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(-32),
              1.0 - np.exp(-32),
              np.exp(-2),
              1.0 - np.exp(-2),
          )
      )
      expected_x = special.ndtri(p)
      x = special_math.ndtri(p)
      self.assertAllClose(expected_x, self.evaluate(x), atol=0.0)
  
imports: import collections
  import importlib
  import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops.distributions import special_math
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 2 for calling the NdtriTest obj's underlying member method code with example usage: obj.testNdtri() without return types