code: def buildModelCheckPredict(
      train_data, myy, gamX, family, searchLambda=False, stdardize=True
  ):
      numKnots = [5, 5, 5]
      x = ["C1", "C2"]
  
      h2o_model = H2OGeneralizedAdditiveEstimator(
          family=family,
          gam_columns=gamX,
          scale=[0.1, 0.1, 0.1],
          num_knots=numKnots,
          lambda_search=searchLambda,
          standardize=stdardize,
      )
      h2o_model.train(x=x, y=myy, training_frame=train_data)
      return h2o_model
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
  import math
label_desc: Use image node_id 2 for calling a global function with example usage: buildModelCheckPredict(train_data, myy, gamX, family, searchLambda, stdardize) and returns: h2o_model