code: def _test_compare_batch_to_single_instance(
      self, use_sparse_tensor=False
  ):
      n = int(1e4)
      d = 25
      link = "linear"
      model = family.Normal()
  
      # Create two sets of synthetic data according to the given `link` function.
      model_matrix_1_, response_1_, _, _ = self._make_dataset(
          n=n, d=d, link=link, seed=41
      )
      model_matrix_2_, response_2_, _, _ = self._make_dataset(
          n=n, d=d, link=link, seed=42
      )
  
      # Fit both the batches of data individually.
      (
          model_matrix_1,
          response_1,
          model_coefficients_start_1,
      ) = self._prepare_inputs_for_fit_sparse(
          model_matrix_1_,
          response_1_,
          convert_to_sparse_tensor=use_sparse_tensor,
      )
  
      model_coefficients_1_, _, _ = self.evaluate(
          proximal_hessian.fit_sparse(
              model_matrix_1,
              response_1,
              model,
              model_coefficients_start_1,
              l1_regularizer=800.0,
              l2_regularizer=None,
              maximum_iterations=10,
              maximum_full_sweeps_per_iteration=10,
              tolerance=1e-6,
              learning_rate=None,
          )
      )
  
      (
          model_matrix_2,
          response_2,
          model_coefficients_start_2,
      ) = self._prepare_inputs_for_fit_sparse(
          model_matrix_2_,
          response_2_,
          convert_to_sparse_tensor=use_sparse_tensor,
      )
  
      model_coefficients_2_, _, _ = self.evaluate(
          proximal_hessian.fit_sparse(
              model_matrix_2,
              response_2,
              model,
              model_coefficients_start_2,
              l1_regularizer=800.0,
              l2_regularizer=None,
              maximum_iterations=10,
              maximum_full_sweeps_per_iteration=10,
              tolerance=1e-6,
              learning_rate=None,
          )
      )
  
      # Combine the data into a single batch of 2 and fit the batched data.
      model_matrix_ = np.stack([model_matrix_1_, model_matrix_2_])
      response_ = np.stack([response_1_, response_2_])
  
      (
          model_matrix,
          response,
          model_coefficients_start,
      ) = self._prepare_inputs_for_fit_sparse(
          model_matrix_,
          response_,
          convert_to_sparse_tensor=use_sparse_tensor,
      )
  
      model_coefficients_, _, _ = self.evaluate(
          proximal_hessian.fit_sparse(
              model_matrix,
              response,
              model,
              model_coefficients_start,
              l1_regularizer=800.0,
              l2_regularizer=None,
              maximum_iterations=10,
              maximum_full_sweeps_per_iteration=10,
              tolerance=1e-6,
              learning_rate=None,
          )
      )
  
      # Ensure that the learned coefficients from the individual samples are close
      # to those learned from the batched samples.
      self.assertAllClose(
          model_coefficients_1_,
          model_coefficients_[0],
          atol=0.0,
          rtol=1e-3,
      )
      self.assertAllClose(
          model_coefficients_2_,
          model_coefficients_[1],
          atol=0.0,
          rtol=1e-3,
      )
  
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 15 for calling the _ProximalHessianTest obj's underlying member method code with example usage: obj._test_compare_batch_to_single_instance(use_sparse_tensor) without return types