add type hints

mlflow/getml/autologging.py

@@ -154,7 +154,7 @@ def _extract_engine_system_metrics(
             step += 1
             stop_event.wait(1)
 
-    def patched_fit_mlflow(original, self: getml.Pipeline, *args, **kwargs):
+    def patched_fit_mlflow(original, self: getml.Pipeline, *args, **kwargs) -> getml.pipeline.Pipeline:
         autologging_client = MlflowAutologgingQueueingClient()
         assert (active_run := mlflow.active_run())
         run_id = active_run.info.run_id
@@ -187,7 +187,7 @@ def patched_fit_mlflow(original, self: getml.Pipeline, *args, **kwargs):
         autologging_client.flush(synchronous=True)
         return fit_output
 
-    def patched_score_method(original, self: getml.Pipeline, *args, **kwargs):
+    def patched_score_method(original, self: getml.Pipeline, *args, **kwargs) -> getml.pipeline.Scores:
 
         target = self.data_model.population.roles.target[0]
         pop_df = args[0].population.to_pandas()
@@ -202,9 +202,13 @@ def patched_score_method(original, self: getml.Pipeline, *args, **kwargs):
             model_type=["regressor" if self.is_regression else "classifier"][0],
             evaluators=["default"],
         )
-
         return original(self, *args, **kwargs)
-    def _log_pretraining_metadata(autologging_client, self: getml.Pipeline, run_id, *args):
+
+    def _log_pretraining_metadata(autologging_client: MlflowAutologgingQueueingClient,
+                                  self: getml.Pipeline,
+                                  run_id: str,
+                                  *args
+                                  ) -> dict:
 
         pipeline_log_info = _extract_pipeline_informations(self)
         autologging_client.log_params(