code: def run_test_for_glm(
      self,
      model,
      model_spars=None,
      delta_check_grad=None,
      delta_model_grad=None,
  ):
      coeffs = np.random.randn(model.n_coeffs).astype(self.dtype)
      # dense case
      self._test_grad(
          model,
          coeffs,
          delta_check_grad=delta_check_grad,
          delta_model_grad=delta_model_grad,
      )
      # sparse case
      if model_spars is not None:
          self._test_grad(
              model_spars,
              coeffs,
              delta_check_grad=delta_check_grad,
              delta_model_grad=delta_model_grad,
          )
          # Check that loss computed in the dense and sparse case are
          # the same
          self.assertAlmostEqual(
              model.loss(coeffs),
              model_spars.loss(coeffs),
              places=self.decimal_places,
          )
          # Check that gradients computed in the dense and sparse
          # case are the same
          np.testing.assert_almost_equal(
              model.grad(coeffs),
              model_spars.grad(coeffs),
              decimal=self.decimal_places,
          )
  
      if model.dtype != "float64":
          model_float64 = model.astype("float64")
  
          self.assertAlmostEqual(
              model.loss(coeffs),
              model_float64.loss(coeffs.astype(float)),
              places=self.decimal_places,
          )
          np.testing.assert_almost_equal(
              model.grad(coeffs),
              model_float64.grad(coeffs.astype(float)),
              decimal=self.decimal_places,
          )
  
file_path: tick/tick/base_model/tests/generalized_linear_model.py
imports: import warnings
  import numpy
  from numpy.linalg import norm
  from scipy.optimize import check_grad, fmin_bfgs
  import unittest
label_desc: Use image node_id 3 for calling the TestGLM obj's underlying member method code with example usage: obj.run_test_for_glm(model, model_spars, delta_check_grad, delta_model_grad) without return types