code: def nb_iris():
      print("Importing iris_wheader.csv data...\n")
      iris = h2o.upload_file(
          pyunit_utils.locate("smalldata/iris/iris_wheader.csv")
      )
      iris.describe()
  
      laplace_range = [0, 1, 0.25]
      for i in laplace_range:
          print(
              "H2O Naive Bayes with Laplace smoothing = {0}".format(i)
          )
          iris_nbayes = H2ONaiveBayesEstimator(laplace=i)
          iris_nbayes.train(x=list(range(4)), y=4, training_frame=iris)
          iris_nbayes.show()
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.naive_bayes import H2ONaiveBayesEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: nb_iris() without return types