Refactor MONetBundleInferenceOperator type checking and formatting

- Improved the type checking for the model_network parameter to enhance readability and maintainability.
- Adjusted formatting in the predict method for better clarity and consistency in multimodal data handling.
- These changes contribute to cleaner code and improved functionality within the MONAI framework.

Author: SimoneBendazzoli93

monai/deploy/operators/monet_bundle_inference_operator.py

@@ -16,6 +16,7 @@
 from monai.deploy.utils.importutil import optional_import
 from monai.transforms import ConcatItemsd, ResampleToMatch
 from monai.deploy.core.models.torch_model import TorchScriptModel
+
 torch, _ = optional_import("torch", "1.10.2")
 MetaTensor, _ = optional_import("monai.data.meta_tensor", name="MetaTensor")
 __all__ = ["MONetBundleInferenceOperator"]
@@ -71,10 +72,14 @@ def _set_model_network(self, model_network):
         model_network : torch.nn.Module or torch.jit.ScriptModule
             The model network to be used for inference.
         """
-        if not isinstance(model_network, torch.nn.Module) and not torch.jit.isinstance(model_network, torch.jit.ScriptModule) and not isinstance(model_network, TorchScriptModel):
+        if (
+            not isinstance(model_network, torch.nn.Module)
+            and not torch.jit.isinstance(model_network, torch.jit.ScriptModule)
+            and not isinstance(model_network, TorchScriptModel)
+        ):
             raise TypeError("model_network must be an instance of torch.nn.Module or torch.jit.ScriptModule")
         self._nnunet_predictor.predictor.network = model_network
-    
+
     def predict(self, data: Any, *args, **kwargs) -> Union[Image, Any, Tuple[Any, ...], Dict[Any, Any]]:
         """Predicts output using the inferer. If multimodal data is provided as keyword arguments,
         it concatenates the data with the main input data."""
@@ -85,9 +90,8 @@ def predict(self, data: Any, *args, **kwargs) -> Union[Image, Any, Tuple[Any, ..
             multimodal_data = {"image": data}
             for key in kwargs.keys():
                 if isinstance(kwargs[key], MetaTensor):
-                    multimodal_data[key] = ResampleToMatch(mode="bilinear")(kwargs[key], img_dst=data
-                                                         )
-            data = ConcatItemsd(keys=list(multimodal_data.keys()),name="image")(multimodal_data)["image"]
+                    multimodal_data[key] = ResampleToMatch(mode="bilinear")(kwargs[key], img_dst=data)
+            data = ConcatItemsd(keys=list(multimodal_data.keys()), name="image")(multimodal_data)["image"]
         if len(data.shape) == 4:
             data = data[None]
         return self._nnunet_predictor(data)