SIENTIAPDE-1646

Implement new scheduling configurations for model retraining and drift analysis in input_sample.json

- Added three new schedule configurations: `minimal-retrain-test-runtime`, `drift-test-runtime`, and `simple-metrics-test-runtime`.
- Each configuration includes parameters such as model ID, workflow type, frequency, and specific queries for data retrieval.
- Enhanced the structure to support active status and updated timestamps for better tracking of schedule states.

tests/laborious/activities/test_mlflow.py

@@ -311,6 +311,7 @@ def test_request_predict(mock_to_datetime, mock_from_dataframe, mlflow):
 )
 @patch('laborious.activities.mlflow.to_datetime')
 def test_request_predict_success_dataframe_and_meta(mock_to_datetime, mock_from_dataframe, mlflow):
+    mock_to_datetime.side_effect = lambda x, **kwargs: x
     wrapper = MagicMock()
     pred_df = pd.DataFrame({'raw': [0.3]})
     wrapper.predict.return_value = (pred_df, {'m': 1})