code: def pubdev_5265():
      training_data = {
          "response": [
              "A",
              "A",
              "A",
              "A",
              "A",
              "B",
              "B",
              "B",
              "B",
              "B",
              "B",
              "C",
              "C",
              "C",
              "C",
              "C",
              "C",
              "C",
          ],
          "explanatory": [
              "nan",
              1,
              1,
              1,
              1,
              2,
              2,
              2,
              2,
              2,
              2,
              3,
              3,
              3,
              3,
              3,
              3,
              3,
          ],
      }
  
      test_data = {
          "response": [
              "A",
              "A",
              "A",
              "A",
              "A",
              "B",
              "B",
              "B",
              "B",
              "B",
              "B",
              "C",
              "C",
              "C",
              "C",
              "C",
              "C",
              "C",
          ],
          "explanatory": [
              "nan",
              1,
              1,
              1,
              1,
              2,
              2,
              2,
              2,
              2,
              2,
              3,
              3,
              3,
              3,
              3,
              3,
              4,
          ],
      }
  
      training_data = h2o.H2OFrame(training_data)
      training_data["explanatory"] = training_data[
          "explanatory"
      ].asfactor()
  
      test_data = h2o.H2OFrame(test_data)
      test_data["explanatory"] = test_data["explanatory"].asfactor()
  
      glm_estimator = H2OGeneralizedLinearEstimator(
          family="multinomial",
          missing_values_handling="MeanImputation",
          seed=1234,
          lambda_=0,
      )
  
      glm_estimator.train(
          x=["explanatory"], y="response", training_frame=training_data
      )
      buffer = StringIO()  # redirect output
      sys.stderr = buffer
  
      # Training on the given dataset should not fail if there is a missing categorical variable (present in training dataset)
      glm_estimator.predict(
          test_data=test_data
      )  # remove as_data_frame to avoid warning generated unrelated to missing categorical values
      warns = buffer.getvalue()
      assert (
          "Test/Validation dataset column 'explanatory' has levels not trained on: [\"4\"]"
          in warns
      )
  
      # The very first value corresponding to 'A' in the explanatory variable column should be replaced by the mode value, which is 3.
      # As a result, 8 occurances of type C should be predicted
      sys.stderr = buffer
      grouped_occurances = (
          glm_estimator.predict(test_data=test_data)
          .group_by((0))
          .count()
          .get_frame()
          .as_data_frame()
      )
      grouped_occurances.values.tolist() == [
          ["A", 4],
          ["B", 6],
          ["C", 8],
      ]
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: pubdev_5265() without return types