code: def testHalfCauchyGradientsAndValueFiniteAtGreaterThanLoc(self):
      batch_size = 1000
      loc = self._create_placeholder_with_default(
          [0.0] * batch_size, name="loc"
      )
      scale = self._create_placeholder_with_default([1.0], name="scale")
      x_ = np.linspace(1e-3, 100.0, batch_size)
      x = self._create_placeholder_with_default(x_, name="x")
  
      def get_half_cauchy_func(func_name):
          def half_cauchy_func(loc, scale, x):
              return getattr(
                  half_cauchy.HalfCauchy(
                      loc, scale, validate_args=True
                  ),
                  func_name,
              )(x)
  
          return half_cauchy_func
  
      for func_name in [
          "prob",
          "log_prob",
          "cdf",
          "log_cdf",
          "survival_function",
          "log_survival_function",
      ]:
          func = get_half_cauchy_func(func_name)
          value, grads = self.evaluate(
              gradient.value_and_gradient(func, [loc, scale, x])
          )
          self.assertAllFinite(value)
          for grad in grads:
              self.assertAllFinite(grad)
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import half_cauchy
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 27 for calling the _HalfCauchyTest obj's underlying member method code with example usage: obj.testHalfCauchyGradientsAndValueFiniteAtGreaterThanLoc() and returns: half_cauchy_func, getattr