code: def run_print_model_performance(
      family,
      train,
      nfolds,
      bc_constraints,
      x,
      y,
      printText,
      seed,
      solver,
  ):
      print(printText)
      print("Without lambda search and with solver {0}".format(solver))
      h2o_model = H2OGeneralizedLinearEstimator(
          family=family,
          nfolds=nfolds,
          beta_constraints=bc_constraints,
          seed=seed,
          solver=solver,
      )
      h2o_model.train(x=x, y=y, training_frame=train)
  
      # check coefficients to be within bounds
      coeff = h2o_model.coef()
      colNames = bc_constraints["names"]
      lowerB = bc_constraints["lower_bounds"]
      upperB = bc_constraints["upper_bounds"]
      for count in range(0, len(colNames)):
          low_diff = (
              abs(coeff[colNames[count, 0]] - lowerB[count, 0]) < 1e-6
          )
          up_diff = (
              abs(coeff[colNames[count, 0]] <= upperB[count, 0]) < 1e-6
          )
          coef_inactive = coeff[colNames[count, 0]] == 0
          assert (
              (
                  coeff[colNames[count, 0]] >= lowerB[count, 0]
                  or low_diff
              )
              and (
                  coeff[colNames[count, 0]] <= upperB[count, 0]
                  or up_diff
              )
          ) or coef_inactive, "coef for {0}: {1}, lower bound: {2}, upper bound: {3}".format(
              colNames[count, 0],
              coeff[colNames[count, 0]],
              lowerB[count, 0],
              upperB[count, 0],
          )
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 2 for calling a global function with example usage: run_print_model_performance(family, train, nfolds, bc_constraints, x, y, printText, seed, solver) without return types