code: def _prepare_inputs_for_fit_sparse(
      self,
      model_matrix,
      response,
      model_coefficients_start=None,
      convert_to_sparse_tensor=False,
  ):
      if model_coefficients_start is None:
          model_coefficients_start = np.zeros(
              model_matrix.shape[:-2] + model_matrix.shape[-1:]
          )
      if convert_to_sparse_tensor:
          model_matrix = sparse.dense_to_sparse(model_matrix)
  
      model_matrix = self._adjust_dtype_and_shape_hints(model_matrix)
      response = self._adjust_dtype_and_shape_hints(response)
      model_coefficients_start = self._adjust_dtype_and_shape_hints(
          model_coefficients_start
      )
  
      return model_matrix, response, model_coefficients_start
  
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 4 for calling the _ProximalHessianTest obj's underlying member method code with example usage: obj._prepare_inputs_for_fit_sparse(model_matrix, response, model_coefficients_start, convert_to_sparse_tensor) and returns: model_matrix,  response,  model_coefficients_start