code: def test_float32_extreme_values_result_and_gradient_finite_and_nonzero(
      self,
  ):
      # On the lower branch, log_cdf_laplace(x) = x, so we know this will be
      # fine, but test to -200 anyways.
      grid = _make_grid(
          np.float32, GridSpec(min=-200, max=80, shape=[20, 100])
      )
      grid = tf.convert_to_tensor(value=grid)
  
      actual, grad = value_and_gradient(
          special_math.log_cdf_laplace, grid
      )
      actual_, grad_ = self.evaluate([actual, grad])
  
      # isfinite checks for NaN and Inf.
      self.assertAllTrue(np.isfinite(actual_))
      self.assertAllTrue(np.isfinite(grad_))
      self.assertFalse(np.any(actual_ == 0))
      self.assertFalse(np.any(grad_ == 0))
  
imports: import collections
  import numpy
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import special_math
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.gradient import value_and_gradient
label_desc: Use image node_id 5 for calling the LogCDFLaplaceTest obj's underlying member method code with example usage: obj.test_float32_extreme_values_result_and_gradient_finite_and_nonzero() without return types