diff --git a/paper/README.md b/paper/README.md
index a053acf..30b2f1a 100644
--- a/paper/README.md
+++ b/paper/README.md
@@ -4,25 +4,25 @@
 - `20220310_plot_causalimpact.ipynb` used to plots the results of the causal impact analysis (e.g. generated via `xgboost_causalimpact.py`)
 - `20220310_plot_forecast_overview.ipynb` used to plot an overview of the historical forecasts (Figure 7 in the main text as well as the model evaluation)
 - `20220310_train_gbdt_on_all.ipynb` used to train the GBDT model on all data (subsequently used to compute the scenarios)
-- `20220306_predict_w_gbdt.ipynb` example for training a GBDT model 
+- `20220306_predict_w_gbdt.ipynb` example for training a GBDT model
 
-## Scripts 
-### Causal impact analysis 
+## Scripts
+### Causal impact analysis
 - `causalimpact_sweep.py` run the hyperparamter sweep (assumes [Weights and Biases](https://wandb.ai/site) is set up)
-- `causalimpact_xgboost.py` run the causal impact analysis using GBDT models 
+- `causalimpact_xgboost.py` run the causal impact analysis using GBDT models
 - `tcn_causalimpact.py` run the analysis using TCN models
-- `step_times.pkl` contains the timestamps for the step changes in our study 
+- `step_times.pkl` contains the timestamps for the step changes in our study
 
-### Scenarios 
-- `loop_over_maps_gbdt.py` / `loop_over_maps_scitas.py` used to create and submit slurm script for "scenario" analysis 
-- `plot_effects_gbdt.py` / `plot_effects.py` used to convert the outputs of the scenario scripts into heatmaps 
-- `run_gbdt_scenarios.py` / `run_scenarios.py` contain the logic for running the scenarios 
+### Scenarios
+- `loop_over_maps_gbdt.py` / `loop_over_maps_scitas.py` used to create and submit slurm script for "scenario" analysis
+- `plot_effects_gbdt.py` / `plot_effects.py` used to convert the outputs of the scenario scripts into heatmaps
+- `run_gbdt_scenarios.py` / `run_scenarios.py` contain the logic for running the scenarios
 
 ### Models
 
-Model checkpoints are archived on Zenodo (DOI: [https://dx.doi.org/10.5281/zenodo.5153417](10.5281/zenodo.5153417)) but also available in the `model` subdirectory. 
+Model checkpoints are archived on Zenodo (DOI: [https://dx.doi.org/10.5281/zenodo.5153417](10.5281/zenodo.5153417)) but also available in the `model` subdirectory.
 Unfortunately, we could only serialize the models as pickle files wherefore the same Python version and package versions are needed for reusing the models.
 
-### Results 
+### Results
 
-The `results` subdirectory contains pre-computed results that are used in the notebooks that plot the results.
\ No newline at end of file
+The `results` subdirectory contains pre-computed results that are used in the notebooks that plot the results.
diff --git a/paper/causalimpact_sweep.py b/paper/causalimpact_sweep.py
index ab89069..b6eb7fa 100644
--- a/paper/causalimpact_sweep.py
+++ b/paper/causalimpact_sweep.py
@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 
 import logging
 import pickle
@@ -92,7 +93,7 @@ def inner_train_test(x, y, day, target):
     x = x[features]
 
     x_trains = []
-    y_trains = []   
+    y_trains = []
 
     before, during, after, way_after = get_causalimpact_splits(x, y, day, times, DF)
 
@@ -115,9 +116,9 @@ def inner_train_test(x, y, day, target):
     x_trains[shorter] = xscaler.transform(x_trains[shorter])
     y_trains[shorter] = yscaler.transform(y_trains[shorter])
 
-    steps =len(during[0]) 
+    steps = len(during[0])
 
-    if steps >  len(x_trains[shorter]):
+    if steps > len(x_trains[shorter]):
         ts = choose_index(x, 0.3)
         x_before, x_after = x_trains[longer].split_before(ts)
         y_before, y_after = y_trains[longer].split_before(ts)
diff --git a/paper/causalimpact_xgboost.py b/paper/causalimpact_xgboost.py
index 8aa2f3e..f5918d8 100644
--- a/paper/causalimpact_xgboost.py
+++ b/paper/causalimpact_xgboost.py
@@ -1,27 +1,32 @@
-from aeml.causalimpact.utils import get_timestep_tuples, get_causalimpact_splits
+# -*- coding: utf-8 -*-
+import math
 import pickle
-from aeml.causalimpact.utils import _select_unrelated_x
+import time
+from copy import deepcopy
+
+import click
+import numpy as np
+import pandas as pd
+from darts import TimeSeries
+from darts.dataprocessing.transformers import Scaler
+
+from aeml.causalimpact.utils import (
+    _select_unrelated_x,
+    get_causalimpact_splits,
+    get_timestep_tuples,
+)
 from aeml.models.gbdt.gbmquantile import LightGBMQuantileRegressor
 from aeml.models.gbdt.run import run_ci_model
 from aeml.models.gbdt.settings import *
 
-from darts.dataprocessing.transformers import Scaler
-from darts import TimeSeries
-import pandas as pd
-from copy import deepcopy
-import time
-import numpy as np 
-import click
-import math 
-
 settings = {
-    0: {0: ci_0_0, 1: ci_0_1}, 
-1: {0: ci_1_0, 1: ci_1_1},
-2: {0: ci_2_0, 1: ci_2_1},
-3: {0: ci_3_0, 1: ci_3_1},
-4: {0: ci_4_0, 1: ci_4_1},
-5: {0: ci_5_0, 1: ci_5_1},
-6: {0: ci_6_0, 1: ci_6_1}
+    0: {0: ci_0_0, 1: ci_0_1},
+    1: {0: ci_1_0, 1: ci_1_1},
+    2: {0: ci_2_0, 1: ci_2_1},
+    3: {0: ci_3_0, 1: ci_3_1},
+    4: {0: ci_4_0, 1: ci_4_1},
+    5: {0: ci_5_0, 1: ci_5_1},
+    6: {0: ci_6_0, 1: ci_6_1},
 }
 
 TIMESTR = time.strftime("%Y%m%d-%H%M%S")
@@ -87,16 +92,17 @@
     6: [],
 }
 
+
 def select_columns(day):
     feat_to_exclude = to_exclude[day]
     feats = [f for f in MEAS_COLUMNS if f not in feat_to_exclude]
     return feats
 
 
-@click.command('cli')
-@click.argument('day', type=click.INT)
-@click.argument('target', type=click.INT)
-def run_causalimpact_analysis(day, target): 
+@click.command("cli")
+@click.argument("day", type=click.INT)
+@click.argument("target", type=click.INT)
+def run_causalimpact_analysis(day, target):
     cols = select_columns(day)
     y = TimeSeries.from_dataframe(DF)[TARGETS_clean[target]]
     x = TimeSeries.from_dataframe(DF[cols])
@@ -104,11 +110,9 @@ def run_causalimpact_analysis(day, target):
     x_trains = []
     y_trains = []
 
-    before, during, after, way_after = get_causalimpact_splits(
-        x, y, day, times, DF
-    )
+    before, during, after, way_after = get_causalimpact_splits(x, y, day, times, DF)
 
-    # We do multiseries training 
+    # We do multiseries training
     x_trains.append(before[0])
     y_trains.append(before[1])
     x_trains.append(way_after[0])
@@ -126,7 +130,7 @@ def run_causalimpact_analysis(day, target):
     x_trains[shorter] = xscaler.transform(x_trains[shorter])
     y_trains[shorter] = yscaler.transform(y_trains[shorter])
 
-    if len(x_trains[shorter]) < 300: 
+    if len(x_trains[shorter]) < 300:
         x_trains.pop(shorter)
         y_trains.pop(shorter)
 
@@ -160,39 +164,39 @@ def run_causalimpact_analysis(day, target):
 
     day_y_df = pd.concat([before_y_df, during_y_df, after_y_df], axis=0)
     day_y_ts = TimeSeries.from_dataframe(day_y_df)
-    
-    steps = math.ceil(len(during[0])/2)# * 2
+
+    steps = math.ceil(len(during[0]) / 2)  # * 2
 
     model = run_ci_model(
         x_trains,
         y_trains,
         **settings[day][target],
         num_features=len(cols),
-                quantiles=(0.05, 0.5, 0.95), 
-                output_chunk_length=steps
+        quantiles=(0.05, 0.5, 0.95),
+        output_chunk_length=steps,
     )
-    buffer = math.ceil(len(during[0])/3)
+    buffer = math.ceil(len(during[0]) / 3)
     b = before[1][:-buffer]
-    predictions = model.forecast( 
-                      n = len(during[0]) + 2* buffer,
-        series =  b,
-        past_covariates = day_x_ts,
-                
-)
+    predictions = model.forecast(
+        n=len(during[0]) + 2 * buffer,
+        series=b,
+        past_covariates=day_x_ts,
+    )
 
     results = {
-        'predictions': predictions, 
-        'x_all': day_x_ts, 
-        'before': before, 
-        'during': during, 
-        'after': after
+        "predictions": predictions,
+        "x_all": day_x_ts,
+        "before": before,
+        "during": during,
+        "after": after,
     }
 
     with open(
-            f"{TIMESTR}-causalimpact_{day}_{target}",
-            "wb",
-        ) as handle:
-            pickle.dump(results, handle)
+        f"{TIMESTR}-causalimpact_{day}_{target}",
+        "wb",
+    ) as handle:
+        pickle.dump(results, handle)
+
 
-if __name__ == '__main__': 
-    run_causalimpact_analysis()
\ No newline at end of file
+if __name__ == "__main__":
+    run_causalimpact_analysis()
diff --git a/paper/loop_over_maps.py b/paper/loop_over_maps.py
index 49d66a3..21b9aac 100644
--- a/paper/loop_over_maps.py
+++ b/paper/loop_over_maps.py
@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
-from os import fchdir
 import subprocess
 import time
+from os import fchdir
 
 import click
 
@@ -46,7 +46,7 @@
 #SBATCH --constraint=gpu
 #SBATCH --account=pr128
 
-module load daint-gpu 
+module load daint-gpu
 source /home/kjablonk/anaconda3/bin/activate
 conda activate aeml
 
diff --git a/paper/loop_over_maps_scitas.py b/paper/loop_over_maps_scitas.py
index 187f05f..bc17dc3 100644
--- a/paper/loop_over_maps_scitas.py
+++ b/paper/loop_over_maps_scitas.py
@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
-from os import fchdir
 import subprocess
 import time
+from os import fchdir
 
 import click
 
diff --git a/paper/plot_effects.py b/paper/plot_effects.py
index 181a29e..bca3f38 100644
--- a/paper/plot_effects.py
+++ b/paper/plot_effects.py
@@ -1,13 +1,15 @@
-import matplotlib.pyplot as plt
-from glob import glob
+# -*- coding: utf-8 -*-
+import os
 import pickle
+import traceback
+from glob import glob
 from pathlib import Path
-import numpy as np
+
 import click
 import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
 from scipy.ndimage import gaussian_filter
-import os
-import traceback
 
 
 def load_pickle(filename):
diff --git a/paper/plot_effects_gbdt.py b/paper/plot_effects_gbdt.py
index d9b74d5..9f65e4f 100644
--- a/paper/plot_effects_gbdt.py
+++ b/paper/plot_effects_gbdt.py
@@ -1,23 +1,27 @@
-import matplotlib.pyplot as plt
-from glob import glob
+# -*- coding: utf-8 -*-
+import os
 import pickle
+import traceback
+from glob import glob
 from pathlib import Path
-import numpy as np
+
 import click
 import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+from loguru import logger
 from scipy.ndimage import gaussian_filter
-import os
-import traceback
-from loguru import logger 
 
 plt.style.reload_library()
-plt.style.use('science')
+plt.style.use("science")
 from matplotlib import rcParams
-rcParams['font.family'] = 'sans-serif'
+
+rcParams["font.family"] = "sans-serif"
 from scipy.constants import golden
 
 TARGETS_clean = ["2-Amino-2-methylpropanol C4H11NO", "Piperazine C4H10N2"]
 
+
 def load_pickle(filename):
     with open(filename, "rb") as handle:
         res = pickle.load(handle)
@@ -37,6 +41,7 @@ def get_grids(d):
 
     return outer_keys, inner_keys
 
+
 def make_image(res, objective="1"):
     outer, inner = get_grids(res)
 
@@ -47,21 +52,17 @@ def make_image(res, objective="1"):
     for i, point_x in enumerate(outer):
         for j, point_y in enumerate(inner):
             image_m[i][j] = np.sum(
-                res[point_x][point_y][1][objective].values()
-                - res[0][0][1][objective].values()
+                res[point_x][point_y][1][objective].values() - res[0][0][1][objective].values()
             )
 
             image_l[i][j] = np.sum(
-                res[point_x][point_y][0][objective].values()
-                - res[0][0][1][objective].values()
+                res[point_x][point_y][0][objective].values() - res[0][0][1][objective].values()
             )
 
             image_t[i][j] = np.sum(
-                res[point_x][point_y][2][objective].values()
-                - res[0][0][1][objective].values()
+                res[point_x][point_y][2][objective].values() - res[0][0][1][objective].values()
             )
 
-
     return image_m, outer, inner
 
 
@@ -79,7 +80,9 @@ def get_conditions_from_name(name):
         .replace("_amine", "")
         .replace("_amp", "")
         .replace("_pz", "")
-        .replace("_nh3", "").replace("_True", "").replace('_False', "")
+        .replace("_nh3", "")
+        .replace("_True", "")
+        .replace("_False", "")
     )
     parts = stem.split("_")
     _ = parts.pop(0)
@@ -121,9 +124,9 @@ def get_color_norm(numbers):
 def cm2inch(*tupl):
     inch = 2.54
     if isinstance(tupl[0], tuple):
-        return tuple(i/inch for i in tupl[0])
+        return tuple(i / inch for i in tupl[0])
     else:
-        return tuple(i/inch for i in tupl)
+        return tuple(i / inch for i in tupl)
 
 
 def plot_amp_pz_image(
@@ -134,13 +137,12 @@ def plot_amp_pz_image(
     one_color_scale: bool = False,
     single_output: bool = False,
     forecast: bool = False,
-    targets = TARGETS_clean
+    targets=TARGETS_clean,
 ):
 
     pip_image, pip_inner, pip_outer = None, None, None
     amp_image, pip_inner, pip_outer = None, None, None
 
-
     for file in all_files:
         if condition in file:
 
@@ -150,14 +152,22 @@ def plot_amp_pz_image(
             if single_output:
                 if "amp" in file:
                     print(f"amp file {file}")
-                    amp_image, amp_inner, amp_outer = make_image(load_pickle(file), objective=amp_name)
+                    amp_image, amp_inner, amp_outer = make_image(
+                        load_pickle(file), objective=amp_name
+                    )
                 if "pz" in file:
                     print(f"pz file {file}")
-                    pip_image, pip_inner, pip_outer = make_image(load_pickle(file), objective=pz_name)
+                    pip_image, pip_inner, pip_outer = make_image(
+                        load_pickle(file), objective=pz_name
+                    )
             else:
                 if "amp" in file:
-                    amp_image, amp_inner, amp_outer = make_image(load_pickle(file), objective=amp_name)
-                    pip_image, pip_inner, pip_outer = make_image(load_pickle(file), objective=pz_name)
+                    amp_image, amp_inner, amp_outer = make_image(
+                        load_pickle(file), objective=amp_name
+                    )
+                    pip_image, pip_inner, pip_outer = make_image(
+                        load_pickle(file), objective=pz_name
+                    )
 
                 continue
 
@@ -170,7 +180,7 @@ def plot_amp_pz_image(
     # except AssertionError:
     #     print(condition)
 
-    fig, ax = plt.subplots(1, 2, sharex="all", sharey="all", figsize=cm2inch(10, 10/golden))
+    fig, ax = plt.subplots(1, 2, sharex="all", sharey="all", figsize=cm2inch(10, 10 / golden))
 
     print(pip_image.shape)
     if blur is not None:
@@ -246,7 +256,9 @@ def plot_amp_pz_image(
 
     if outdir is not None:
         fig.savefig(
-            os.path.join(outdir, f"{raw_conditions}_{str(one_color_scale)}_{str(blur)}_{str(forecast)}.pdf"),
+            os.path.join(
+                outdir, f"{raw_conditions}_{str(one_color_scale)}_{str(blur)}_{str(forecast)}.pdf"
+            ),
             bbox_inches="tight",
         )
         plt.close(fig)
@@ -257,24 +269,31 @@ def plot_amp_pz_image(
 @click.command("cli")
 @click.argument("indir", type=click.Path(exists=True))
 @click.argument("outdir", type=click.Path(exists=False))
-@click.option('--forecast', is_flag=True)
+@click.option("--forecast", is_flag=True)
 def compute_single_output_maps(indir, outdir, forecast):
     if not os.path.exists(outdir):
         os.makedirs(outdir)
 
     if forecast:
         all_files = glob(os.path.join(indir, "*_True"))
-        targets = ['2-Amino-2-methylpropanol C4H11NO', 'Piperazine C4H10N2'] 
-    else: 
+        targets = ["2-Amino-2-methylpropanol C4H11NO", "Piperazine C4H10N2"]
+    else:
         all_files = glob(os.path.join(indir, "*_False"))
-        targets = ['0', '0'] 
+        targets = ["0", "0"]
 
-    logger.info(f'Found {len(all_files)} files')
+    logger.info(f"Found {len(all_files)} files")
     all_conditions = get_all_conditions(all_files)
 
     for condition in all_conditions:
         try:
-            plot_amp_pz_image(condition, all_files, outdir=outdir, single_output=True, forecast=forecast, targets=targets)
+            plot_amp_pz_image(
+                condition,
+                all_files,
+                outdir=outdir,
+                single_output=True,
+                forecast=forecast,
+                targets=targets,
+            )
         except Exception as e:
             print(traceback.format_exc())
             print(e)
diff --git a/paper/run_gbdt_scenarios.py b/paper/run_gbdt_scenarios.py
index dfad150..bfa26d7 100644
--- a/paper/run_gbdt_scenarios.py
+++ b/paper/run_gbdt_scenarios.py
@@ -1,10 +1,10 @@
 # -*- coding: utf-8 -*-
-from curses.ascii import TAB
 import os
 import pickle
 import time
 from collections import defaultdict
 from copy import deepcopy
+from curses.ascii import TAB
 
 import click
 import joblib
@@ -12,7 +12,6 @@
 import pandas as pd
 from darts import TimeSeries
 
-
 from aeml.utils import choose_index
 
 THIS_DIR = os.path.dirname(os.path.realpath(__file__))
@@ -68,25 +67,20 @@ def dump_pickle(object, filename):
 FEAT_NUM_MAPPING = dict(zip(MEAS_COLUMNS, [str(i) for i in range(len(MEAS_COLUMNS))]))
 UPDATE_MAPPING = {
     "amp": {
-        "scaler": joblib.load(
-            "20220312_y_transformer"
-        ),
+        "scaler": joblib.load("20220312_y_transformer"),
         "model": joblib.load("20220312_model_all_data_0"),
         "name": ["2-Amino-2-methylpropanol C4H11NO"],
     },
     "pz": {
-        "scaler": joblib.load(
-            "20220312_y_transformer"
-        ),
+        "scaler": joblib.load("20220312_y_transformer"),
         "model": joblib.load("20220312_model_all_data_1"),
-        "name": [ "Piperazine C4H10N2"],
+        "name": ["Piperazine C4H10N2"],
     },
 }
-SCALER = joblib.load(
-    "20220312_x_transformer"
-)
+SCALER = joblib.load("20220312_x_transformer")
+
+TARGETS_clean = ["2-Amino-2-methylpropanol C4H11NO", "Piperazine C4H10N2"]
 
-TARGETS_clean = ['2-Amino-2-methylpropanol C4H11NO', 'Piperazine C4H10N2'] 
 
 # making the input one item longer as "safety margin"
 def calculate_initialization_percentage(timeseries_length: int, input_sequence_length: int = 61):
@@ -101,10 +95,10 @@ def run_update_historical_forecast(df, x, target="amine"):
 
     predictions = model_dict["model"].historical_forecasts(
         past_covariates=x,
-        series=y[model_dict['name']],
+        series=y[model_dict["name"]],
         start=calculate_initialization_percentage(len(y)),
         forecast_horizon=1,
-        retrain=False
+        retrain=False,
     )
     return predictions
 
@@ -120,7 +114,7 @@ def run_update_forecast(df, x, target="amine"):
 
     predictions = model_dict["model"].forecast(
         past_covariates=x,
-        series=y_past[model_dict['name']],
+        series=y_past[model_dict["name"]],
         n=len(x) - len(y_past),
     )
     return predictions
diff --git a/paper/run_scenarios.py b/paper/run_scenarios.py
index e39dec7..048b83a 100644
--- a/paper/run_scenarios.py
+++ b/paper/run_scenarios.py
@@ -9,11 +9,10 @@
 import joblib
 import numpy as np
 import pandas as pd
-from darts import TimeSeries
 import torch
+from darts import TimeSeries
 
-
-from aeml.models.forecast import parallelized_inference, forecast, summarize_results
+from aeml.models.forecast import forecast, parallelized_inference, summarize_results
 from aeml.utils import choose_index
 
 THIS_DIR = os.path.dirname(os.path.realpath(__file__))
diff --git a/paper/tcn_causalimpact.py b/paper/tcn_causalimpact.py
index 9f0f6d6..dc45388 100644
--- a/paper/tcn_causalimpact.py
+++ b/paper/tcn_causalimpact.py
@@ -1,23 +1,20 @@
-from statistics import quantiles
+# -*- coding: utf-8 -*-
 import sys
 import traceback
+from statistics import quantiles
 
 sys.path.append("../src")
-from aeml.causalimpact.utils import get_timestep_tuples
 import pickle
-from aeml.causalimpact.utils import _select_unrelated_x
-from aeml.models.forecast import (
-    forecast,
-    parallelized_inference,
-)
-
-from aeml.models.tcn_dropout import TCNDropout
+import time
+from copy import deepcopy
 
-from darts.dataprocessing.transformers import Scaler
-from darts import TimeSeries
 import pandas as pd
-from copy import deepcopy
-import time
+from darts import TimeSeries
+from darts.dataprocessing.transformers import Scaler
+
+from aeml.causalimpact.utils import _select_unrelated_x, get_timestep_tuples
+from aeml.models.forecast import forecast, parallelized_inference
+from aeml.models.tcn_dropout import TCNDropout
 
 TIMESTR = time.strftime("%Y%m%d-%H%M%S")
 
diff --git a/paper/train_forecast.py b/paper/train_forecast.py
index b583615..c381807 100644
--- a/paper/train_forecast.py
+++ b/paper/train_forecast.py
@@ -1,19 +1,20 @@
-from aeml.models.run import run_model
-from aeml.models.forecast import forecast, parallelized_inference, summarize_results
-from aeml.models.utils import split_data
-from aeml.models.plotting import make_forecast_plot
-from aeml.utils.io import dump_pickle
+# -*- coding: utf-8 -*-
+import os
+import time
+import traceback
 
+import hydra
+import matplotlib.pyplot as plt
+import pandas as pd
 from darts import TimeSeries
 from darts.dataprocessing.transformers import Scaler
-import pandas as pd
-
-import matplotlib.pyplot as plt
-import hydra
-import time
-import os
 from matplotlib import rcParams
-import traceback
+
+from aeml.models.forecast import forecast, parallelized_inference, summarize_results
+from aeml.models.plotting import make_forecast_plot
+from aeml.models.run import run_model
+from aeml.models.utils import split_data
+from aeml.utils.io import dump_pickle
 
 plt.style.use("science")
 rcParams["font.family"] = "sans-serif"
@@ -36,7 +37,6 @@
 
 from aeml.models.run import run_model
 
-
 STEP_INDICES = [75, 645, 2075, 2880, 3520, 4245]
 
 
diff --git a/src/aeml/models/tcn/tcn_dropout.py b/src/aeml/models/tcn/tcn_dropout.py
index 38dab2f..ccf534a 100644
--- a/src/aeml/models/tcn/tcn_dropout.py
+++ b/src/aeml/models/tcn/tcn_dropout.py
@@ -177,7 +177,6 @@ def predict_from_dataset(
         num_loader_workers: int = 0,
         enable_mc_dropout: bool = False,
     ) -> Sequence[TimeSeries]:
-
         """
         This method allows for predicting with a specific :class:`darts.utils.data.InferenceDataset` instance.
         These datasets implement a PyTorch ``Dataset``, and specify how the target and covariates are sliced
@@ -315,7 +314,6 @@ def historical_forecasts(
         verbose: bool = False,
         enable_mc_dropout: bool = False,
     ) -> Union[TimeSeries, List[TimeSeries]]:
-
         """Compute the historical forecasts that would have been obtained by this model on the `series`.
         This method uses an expanding training window;
         it repeatedly builds a training set from the beginning of `series`. It trains the