code: def testFitGLMFromData_SimilarModel(self):
      # Run fit_sparse where the loss function is negative log likelihood of a
      # synthetic data set generated from a similar model (probit vs. logit).
      # Expect the returned value of model_coefficients to be close to the true
      # parameters.
      self._test_fit_glm_from_data(
          n=int(1e5),
          d=100,
          link="probit",
          model=family.Bernoulli(),
          batch_shape=None,
      )
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.glm import family
  from tensorflow_probability.python.glm import proximal_hessian
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import sparse
label_desc: Use image node_id 7 for calling the _ProximalHessianTest obj's underlying member method code with example usage: obj.testFitGLMFromData_SimilarModel() without return types