code: def grid_f0point5_metrics():
      gbm_grid1 = train_grid()
  
      gbm_gridper_f0point5 = gbm_grid1.get_grid(
          sort_by="f0point5", decreasing=True
      )
      print(gbm_gridper_f0point5)
      sorted_metric_table_f0point5 = (
          gbm_gridper_f0point5.sorted_metric_table()
      )
  
      # Lets compare values grid was sorted with and metrics from each model in the grid
      print("Model 0:")
      best_gbm_f0point5 = gbm_gridper_f0point5.models[0]
      model0_f0point5_valid = best_gbm_f0point5.F0point5(valid=True)
      print(model0_f0point5_valid)
      errorMsg = "Expected that metric value from sorted_metric_table is equal to corresponding metric for the model"
      assert float(model0_f0point5_valid[0][1]) == float(
          sorted_metric_table_f0point5["f0point5"][0]
      ), errorMsg
  
      print("Model 1:")
      best_gbm_f0point5_1 = gbm_gridper_f0point5.models[1]
      model1_f0point5_valid = best_gbm_f0point5_1.F0point5(valid=True)
      print(model1_f0point5_valid)
      assert float(model1_f0point5_valid[0][1]) == float(
          sorted_metric_table_f0point5["f0point5"][1]
      ), errorMsg
  
      print("Model 2:")
      best_gbm_f0point5_2 = gbm_gridper_f0point5.models[2]
      model2_f0point5_valid = best_gbm_f0point5_2.F0point5(valid=True)
      print(model2_f0point5_valid)
      assert float(model2_f0point5_valid[0][1]) == float(
          sorted_metric_table_f0point5["f0point5"][2]
      ), errorMsg
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
  from h2o.grid.grid_search import H2OGridSearch
label_desc: Use image node_id 1 for calling a global function with example usage: grid_f0point5_metrics() without return types