code: def _baseQuantileFiniteGradientAtDifficultPoints(self, dtype):
      g = ops.Graph()
      with g.as_default():
          mu = variables.Variable(dtype(0.0))
          sigma = variables.Variable(dtype(1.0))
          dist = normal_lib.Normal(loc=mu, scale=sigma)
          p = variables.Variable(
              np.array(
                  [
                      0.0,
                      np.exp(-32.0),
                      np.exp(-2.0),
                      1.0 - np.exp(-2.0),
                      1.0 - np.exp(-32.0),
                      1.0,
                  ]
              ).astype(dtype)
          )
  
          value = dist.quantile(p)
          grads = gradients_impl.gradients(value, [mu, p])
          with self.cached_session(graph=g):
              self.evaluate(variables.global_variables_initializer())
              self.assertAllFinite(grads[0])
              self.assertAllFinite(grads[1])
  
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 20 for calling the NormalTest obj's underlying member method code with example usage: obj._baseQuantileFiniteGradientAtDifficultPoints(dtype) without return types