code: def grab_lambda_min():
      boston = h2o.import_file(
          pyunit_utils.locate("smalldata/gbm_test/BostonHousing.csv")
      )
  
      # set the predictor names and the response column name
      predictors = boston.columns[:-1]
      # set the response column to "medv", the median value of owner-occupied homes in $1000's
      response = "medv"
  
      # convert the chas column to a factor (chas = Charles River dummy variable (= 1 if tract bounds river; 0 otherwise))
      boston["chas"] = boston["chas"].asfactor()
  
      # split into train and validation sets
      train, valid = boston.split_frame(ratios=[0.8], seed=1234)
      boston_glm = H2OGeneralizedLinearEstimator(
          lambda_search=True, seed=1234, cold_start=True
      )
      boston_glm.train(
          x=predictors,
          y=response,
          training_frame=train,
          validation_frame=valid,
      )
      r = H2OGeneralizedLinearEstimator.getGLMRegularizationPath(
          boston_glm
      )
  
      for l in range(0, len(r["lambdas"])):
          m = H2OGeneralizedLinearEstimator(
              alpha=[r["alphas"][l]],
              Lambda=r["lambdas"][l],
              solver="COORDINATE_DESCENT",
          )
          m.train(
              x=predictors,
              y=response,
              training_frame=train,
              validation_frame=valid,
          )
          cs = r["coefficients"][l]
          cs_norm = r["coefficients_std"][l]
          print("comparing coefficients for submodel {0}".format(l))
          pyunit_utils.assertEqualCoeffDicts(cs, m.coef(), tol=1e-6)
          pyunit_utils.assertEqualCoeffDicts(
              cs_norm, m.coef_norm(), tol=1e-6
          )
  
imports: import sys
  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: grab_lambda_min() without return types