code: def glm_solvers():
      predictors = [
          "displacement",
          "power",
          "weight",
          "acceleration",
          "year",
      ]
  
      for solver in [
          "AUTO",
          "IRLSM",
          "L_BFGS",
          "COORDINATE_DESCENT_NAIVE",
          "COORDINATE_DESCENT",
      ]:
          print("Solver = {0}".format(solver))
          for family in [
              "binomial",
              "gaussian",
              "poisson",
              "tweedie",
              "gamma",
          ]:
              if family == "binomial":
                  response_col = "economy_20mpg"
              elif family == "gaussian":
                  response_col = "economy"
              else:
                  response_col = "cylinders"
              print("Family = {0}".format(family))
              training_data = h2o.import_file(
                  pyunit_utils.locate("smalldata/junit/cars_20mpg.csv")
              )
              if family == "binomial":
                  training_data[response_col] = training_data[
                      response_col
                  ].asfactor()
              else:
                  training_data[response_col] = training_data[
                      response_col
                  ].asnumeric()
  
              model = H2OGeneralizedLinearEstimator(
                  family=family, alpha=0, Lambda=1e-5, solver=solver
              )
              model.train(
                  x=predictors,
                  y=response_col,
                  training_frame=training_data,
              )
              h2o.remove(training_data)
  
imports: import sys
  from h2o.exceptions import H2OResponseError
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: glm_solvers() without return types