code: def test_glm_backward_compare():
      tst_data = h2o.import_file(
          pyunit_utils.locate(
              "bigdata/laptop/model_selection/backwardBinomial200C50KRows.csv"
          )
      )
      predictors = tst_data.columns[0:-1]
      response_col = "response"
      weight = "wt"
      tst_data["wt"] = 1
      tst_data[tst_data["response"] == 1, "wt"] = 100
      tst_data["response"] = tst_data["response"].asfactor()
      min_predictor_num = 200
      backward_model = H2OModelSelectionEstimator(
          family="binomial",
          weights_column=weight,
          mode="backward",
          min_predictor_number=min_predictor_num,
      )
      backward_model.train(
          predictors, response_col, training_frame=tst_data
      )
      backward_model_coeff = backward_model.coef()[0]
      glm_model = H2OGeneralizedLinearEstimator(
          family="binomial",
          lambda_=0,
          compute_p_values=True,
          weights_column=weight,
      )
      glm_model.train(predictors, response_col, training_frame=tst_data)
      glm_coeff = glm_model.coef()
      pyunit_utils.assertEqualCoeffDicts(
          glm_coeff, backward_model_coeff, tol=1e-6
      )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.model_selection import H2OModelSelectionEstimator
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_glm_backward_compare() without return types