code: def testCallWorksCorrectly(self):
      predicted_linear_response = (
          np.stack(
              [np.linspace(-5.0, -1e-3, 11), np.linspace(1e-3, 5, 11)]
          )
          .reshape(2, 11, 1)
          .astype(self.dtype)
      )
      (
          expected_mean,
          expected_variance,
          expected_grad_mean,
      ) = self.expected(predicted_linear_response)
      actual_mean, actual_variance, actual_grad_mean = self.model(
          predicted_linear_response
      )
      [
          expected_mean_,
          expected_variance_,
          expected_grad_mean_,
          actual_mean_,
          actual_variance_,
          actual_grad_mean_,
      ] = self.evaluate(
          [
              expected_mean,
              expected_variance,
              expected_grad_mean,
              actual_mean,
              actual_variance,
              actual_grad_mean,
          ]
      )
      self.assertAllClose(
          expected_mean_, actual_mean_, atol=1e-6, rtol=1e-4
      )
      self.assertAllClose(
          expected_variance_, actual_variance_, atol=1e-6, rtol=1e-3
      )
      self.assertAllClose(
          expected_grad_mean_, actual_grad_mean_, atol=1e-6, rtol=1e-4
      )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import binomial
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import negative_binomial
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.glm import family
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 2 for calling the _GLMTestHarness obj's underlying member method code with example usage: obj.testCallWorksCorrectly() without return types