code: def test_anova_table_frame():
      train = h2o.import_file(
          path=pyunit_utils.locate("smalldata/anovaGlm/Moore.csv")
      )
      y = "conformity"
      x = ["fcategory", "partner.status"]
  
      model = H2OANOVAGLMEstimator(
          family="gaussian", lambda_=0, save_transformed_framekeys=True
      )
      model.train(x=x, y=y, training_frame=train)
      anova_table = model.result()
      # compare model summary and anova table frame
      colNames = anova_table.names
      for name in colNames:
          summaryCol = pyunit_utils.extract_col_value_H2OTwoDimTable(
              model._model_json["output"]["model_summary"], name
          )
          for ind in range(0, anova_table.nrow):
              if anova_table[name].isnumeric()[0]:
                  assert (
                      abs(summaryCol[ind] - anova_table[name][ind, 0])
                      < 1e-6
                  ), (
                      "expected value: {0}, actual value: {1} and they"
                      " are different.".format(
                          summaryCol[ind], anova_table[name][ind, 0]
                      )
                  )
              else:
                  assert summaryCol[ind] == anova_table[name][ind, 0], (
                      "expected value: {0}, actual value: {1} and they are"
                      " different.".format(
                          summaryCol[ind], anova_table[name][ind, 0]
                      )
                  )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.anovaglm import H2OANOVAGLMEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_anova_table_frame() without return types