code: def knots_error():
      # load and prepare California housing dataset
      np.random.seed(1234)
      data = h2o.H2OFrame(
          python_obj={
              "C1": list(np.random.randint(0, 9, size=1000)),
              "target": list(np.random.randint(0, 2, size=1000)),
          }
      )
      # use only 3 features and transform into classification problem
      feature_names = ["C1"]
      data["target"] = data["target"].asfactor()
      # split into train and validation sets
      train, test = data.split_frame([0.8], seed=1234)
      # build the GAM model
      h2o_model = H2OGeneralizedAdditiveEstimator(
          family="binomial",
          gam_columns=feature_names,
          scale=[1],
          num_knots=[10],
      )
      try:
          h2o_model.train(
              x=feature_names, y="target", training_frame=train
          )
          assert False, "Number of knots validation should have failed"
      except Exception as ex:
          exception = str(ex)
          assert "H2OModelBuilderIllegalArgumentException" in exception
          assert (
              "has cardinality lower than the number of knots"
              in exception
          )
          assert (
              "chosen gam_column C1 does have not enough values to generate well-defined knots"
              in exception
          )
          print("Error correctly raised when cardinality < num_knots")
  
imports: import h2o
  import numpy
  from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
  from tests import pyunit_utils
label_desc: Use image node_id 1 for calling a global function with example usage: knots_error() without return types