Update test_nnunetv2runner (#7483)

Fixes #7013 #7478

### Description
replace `predict_from_raw_data` with `nnUNetPredictor` in
test_nnunetv2runner

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: YunLiu <[email protected]>

Author: KumoLiu

monai/apps/nnunet/nnunetv2_runner.py

@@ -37,6 +37,7 @@ class nnUNetV2Runner:  # noqa: N801
     """
     ``nnUNetV2Runner`` provides an interface in MONAI to use `nnU-Net` V2 library to analyze, train, and evaluate
     neural networks for medical image segmentation tasks.
+    A version of nnunetv2 higher than 2.2 is needed for this class.
 
     ``nnUNetV2Runner`` can be used in two ways:
 
@@ -770,7 +771,7 @@ def find_best_configuration(
     def predict(
         self,
         list_of_lists_or_source_folder: str | list[list[str]],
-        output_folder: str,
+        output_folder: str | None | list[str],
         model_training_output_dir: str,
         use_folds: tuple[int, ...] | str | None = None,
         tile_step_size: float = 0.5,
@@ -824,28 +825,29 @@ def predict(
         """
         os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu_id}"
 
-        from nnunetv2.inference.predict_from_raw_data import predict_from_raw_data
+        from nnunetv2.inference.predict_from_raw_data import nnUNetPredictor
 
         n_processes_preprocessing = (
             self.default_num_processes if num_processes_preprocessing < 0 else num_processes_preprocessing
         )
         n_processes_segmentation_export = (
             self.default_num_processes if num_processes_segmentation_export < 0 else num_processes_segmentation_export
         )
-
-        predict_from_raw_data(
-            list_of_lists_or_source_folder=list_of_lists_or_source_folder,
-            output_folder=output_folder,
-            model_training_output_dir=model_training_output_dir,
-            use_folds=use_folds,
+        predictor = nnUNetPredictor(
             tile_step_size=tile_step_size,
             use_gaussian=use_gaussian,
             use_mirroring=use_mirroring,
-            perform_everything_on_gpu=perform_everything_on_gpu,
+            perform_everything_on_device=perform_everything_on_gpu,
             verbose=verbose,
+        )
+        predictor.initialize_from_trained_model_folder(
+            model_training_output_dir=model_training_output_dir, use_folds=use_folds, checkpoint_name=checkpoint_name
+        )
+        predictor.predict_from_files(
+            list_of_lists_or_source_folder=list_of_lists_or_source_folder,
+            output_folder_or_list_of_truncated_output_files=output_folder,
             save_probabilities=save_probabilities,
             overwrite=overwrite,
-            checkpoint_name=checkpoint_name,
             num_processes_preprocessing=n_processes_preprocessing,
             num_processes_segmentation_export=n_processes_segmentation_export,
             folder_with_segs_from_prev_stage=folder_with_segs_from_prev_stage,