diff --git a/openstef/model/regressors/flatliner.py b/openstef/model/regressors/flatliner.py
index e5bd2a416..764773d52 100644
--- a/openstef/model/regressors/flatliner.py
+++ b/openstef/model/regressors/flatliner.py
@@ -23,9 +23,9 @@ class FlatlinerRegressor(OpenstfRegressor, RegressorMixin):
     def __init__(self, quantiles=None):
         """Initialize FlatlinerRegressor.
 
-        The model always predicts 0.0, regardless of the input features. The model is
-        meant to be used for flatliner locations that still expect a prediction while
-        preserving the prediction interface.
+        The model always predicts 0.0, regardless of the input features. The model is meant to be used for flatliner
+        locations that still expect a prediction while preserving the prediction interface.
+
         """
         super().__init__()
         self.quantiles = quantiles
diff --git a/openstef/model_selection/model_selection.py b/openstef/model_selection/model_selection.py
index a2fcf6ea3..d8ec40824 100644
--- a/openstef/model_selection/model_selection.py
+++ b/openstef/model_selection/model_selection.py
@@ -106,7 +106,7 @@ def split_data_train_validation_test(
     validation_fraction: float = 0.15,
     back_test: bool = False,
     stratification_min_max: bool = True,
-) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
     """Split input data into train, test and validation set.
 
     Function for splitting data with features in a train, test and
diff --git a/openstef/pipeline/train_model.py b/openstef/pipeline/train_model.py
index 14430fa69..59a47df0d 100644
--- a/openstef/pipeline/train_model.py
+++ b/openstef/pipeline/train_model.py
@@ -3,7 +3,7 @@
 # SPDX-License-Identifier: MPL-2.0
 import logging
 import os
-from typing import Optional, Union
+from typing import Optional, Union, Tuple
 
 import pandas as pd
 import structlog
@@ -155,7 +155,7 @@ def train_model_pipeline_core(
     input_data: pd.DataFrame,
     old_model: OpenstfRegressor = None,
     horizons: list[float] = DEFAULT_TRAIN_HORIZONS_HOURS,
-) -> Union[
+) -> Tuple[
     OpenstfRegressor,
     Report,
     ModelSpecificationDataClass,
@@ -246,7 +246,9 @@ def train_pipeline_common(
     test_fraction: float = 0.0,
     backtest: bool = False,
     test_data_predefined: pd.DataFrame = pd.DataFrame(),
-) -> tuple[OpenstfRegressor, Report, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+) -> tuple[
+    OpenstfRegressor, Report, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame
+]:
     """Common pipeline shared with operational training and backtest training.
 
     Args:
@@ -314,7 +316,8 @@ def train_pipeline_common(
 
 def train_pipeline_step_load_model(
     pj: PredictionJobDataClass, serializer: MLflowSerializer
-) -> tuple[OpenstfRegressor, ModelSpecificationDataClass, Union[int, float]]:
+) -> Tuple[OpenstfRegressor, ModelSpecificationDataClass, Union[int, float]]:
+    old_model: Optional[OpenstfRegressor]
     try:
         old_model, model_specs = serializer.load_model(experiment_name=str(pj.id))
         old_model_age = old_model.age  # Age attribute is openstef specific
@@ -509,7 +512,7 @@ def train_pipeline_step_split_data(
     test_fraction: float,
     backtest: bool = False,
     test_data_predefined: pd.DataFrame = pd.DataFrame(),
-) -> Union[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
     """The default way to perform train, val, test split.
 
     Args: