SIENTIAPDE-1646

Refactor MLFlow retraining logic to always use retrain method

- Removed the conditional logic for full retraining, ensuring the `retrain` method is always called.
- Updated the documentation in the `retrain_model` method to reflect the changes in retraining flow.
- Adjusted tests to verify that the `retrain` method is invoked correctly, while ensuring `train` is not called when the full retrain flag is set.

tests/laborious/activities/test_mlflow.py

@@ -477,7 +477,7 @@ async def test_retrain_model_success_with_payload_data(
 @patch('laborious.activities.mlflow.tempfile.mkdtemp')
 @patch('laborious.activities.mlflow.rmtree')
 @patch('laborious.activities.mlflow.to_datetime')
-async def test_retrain_model_success_full_retrain_branch(
+async def test_retrain_model_always_uses_retrain_even_with_full_retrain_flag(
     mock_to_datetime, mock_rmtree, mock_mkdtemp, mock_log_artifact, mlflow
 ):
     mock_mkdtemp.return_value = 'tmp'
@@ -510,7 +510,8 @@ async def test_retrain_model_success_full_retrain_branch(
         }
     )
 
-    wrapper.train.assert_called_once()
+    wrapper.retrain.assert_called_once()
+    wrapper.train.assert_not_called()
     assert response['success'] is True