feat: integrate SientiaModel wrapper and enhance logging in Training class

- Imported SientiaModel to standardize the wrapper type in the Training class.
- Added conditional logging to capture training process details when a logger is provided, improving traceability during model training.

model_manager/activities/training.py

@@ -6,6 +6,7 @@ The activity extends BaseActivity and receives pre-downloaded files
 and raises `ModelTrainingError` when training fails.
 """
 
+from sientia_model.wrappers.sientia_model import SientiaModel
 from temporalio import activity, workflow
 
 with workflow.unsafe.imports_passed_through():
@@ -228,6 +229,9 @@ class Training(SientiaMonitoring):
                 metadata=metadata,
             )
 
+            if self.logger is not None:
+                wrapper.logger = self.logger.base_logger
+
             self.info(f'Training model for {train_params.model_type}', metadata)
             train_data = train_result.train_data
             val_data = train_result.val_data
@@ -317,7 +321,7 @@ class Training(SientiaMonitoring):
         self,
         train_result: TrainModelResult,
         train_params: TrainModelParams,
-        wrapper: Any,
+        wrapper: SientiaModel,
         metadata: dict[str, Any] | None,
     ) -> None:
         self.info(f'Generating report for {train_params.model_type}', metadata)