code: def test_rf_binary_compile_onnx_no_config_bagging(fit_helper):
      # FIXME: The below code will crash because:
      #  1. We train with a bag, specifically RandomForest that has efficient OOB and thus only trains 1 fold model
      #  2. We compile RandomForest bag to onnx
      #  3. We call refit_full, performing efficient cloning when fitting refit_full for RandomForest, avoiding fitting again
      #  4. The save of the fold 1 model of RandomForest_BAG_L1 into new location in RandomForest_BAG_L1_FULL does not carry over the model.onnx file
      #  5. Crashes when trying to load the model.onnx file because it doesn't exist.
      # FIXME: This bug only appears if the user calls refit_full on compiled models.
      # Solution: Either,
      #  1. force copy the entire directory of the fold 1 model when cloning instead of calling model.save() -> reuse logic in predictor.clone()
      #  2. model._compiler stores context of files it has created / depends on, and then detects if saving to a new location,
      #     then copies the files to that location or computes them again.
      run_test = False
      if run_test:
          fit_args = dict(
              hyperparameters={RFModel: {}},
              num_bag_folds=2,
          )
          dataset_name = "adult"
          compiler_configs = "auto"
          fit_helper.fit_and_validate_dataset(
              dataset_name=dataset_name,
              fit_args=fit_args,
              compile=True,
              compiler_configs=compiler_configs,
          )
  
file_path: autogluon/tabular/tests/unittests/models/test_rf.py
imports: from autogluon.tabular.models.rf.rf_model import RFModel
label_desc: Use image node_id 8 for calling a global function with example usage: test_rf_binary_compile_onnx_no_config_bagging(fit_helper) without return types