diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7c81c917a..e83aadbcb 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -10,7 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 - 
 - 
-- 
+- Jupyter extension for black ([#742](https://github.com/tinkoff-ai/etna/pull/742))
 - 
 - 
 - 
diff --git a/Makefile b/Makefile
index a6b9c06f7..f3de60540 100644
--- a/Makefile
+++ b/Makefile
@@ -1,4 +1,4 @@
-lint: isort-check black-check flake8-check mypy-check spell-check imported-deps-check
+lint: isort-check black-check flake8-check mypy-check spell-check imported-deps-check notebooks-check
 
 isort-check:
 	isort --skip etna/libs --sl -c etna/
@@ -22,11 +22,15 @@ spell-check:
 imported-deps-check:
 	python -m scripts.check_imported_dependencies
 
+notebooks-check:
+	black --check examples/*.ipynb
+
 format:
 	isort --skip etna/libs --sl etna/
 	isort --skip etna/libs --sl tests/
 	black etna/
 	black tests/
+	black examples/*.ipynb
 	flake8 --exclude etna/libs etna/
 	flake8 --exclude etna/libs tests/ --select E,W,C,F401,N
 	mypy
diff --git a/etna/analysis/outliers/hist_outliers.py b/etna/analysis/outliers/hist_outliers.py
index fe7994f45..07cc4daaa 100644
--- a/etna/analysis/outliers/hist_outliers.py
+++ b/etna/analysis/outliers/hist_outliers.py
@@ -34,9 +34,9 @@ def optimal_sse(left: int, right: int, p: np.ndarray, pp: np.ndarray) -> float:
     """
     if left == 0:
         avg = p[right]
-        return pp[right] - avg ** 2 / (right - left + 1)
+        return pp[right] - avg**2 / (right - left + 1)
     avg = p[right] - p[left - 1]
-    return pp[right] - pp[left - 1] - avg ** 2 / (right - left + 1)
+    return pp[right] - pp[left - 1] - avg**2 / (right - left + 1)
 
 
 @numba.jit(nopython=True)
diff --git a/etna/clustering/distances/base.py b/etna/clustering/distances/base.py
index 11af39b21..8cc2faaf7 100644
--- a/etna/clustering/distances/base.py
+++ b/etna/clustering/distances/base.py
@@ -18,7 +18,7 @@
 class Distance(ABC, BaseMixin):
     """Base class for distances between series."""
 
-    def __init__(self, trim_series: bool = False, inf_value: float = sys.float_info.max // 10 ** 200):
+    def __init__(self, trim_series: bool = False, inf_value: float = sys.float_info.max // 10**200):
         """Init Distance.
 
         Parameters
diff --git a/etna/transforms/math/statistics.py b/etna/transforms/math/statistics.py
index 4cea15577..116c2b443 100644
--- a/etna/transforms/math/statistics.py
+++ b/etna/transforms/math/statistics.py
@@ -164,7 +164,7 @@ def transform(self, df: pd.DataFrame) -> pd.DataFrame:
             dataframe with results
         """
         window = self.window if self.window != -1 else len(df)
-        self._alpha_range = np.array([self.alpha ** i for i in range(window)])
+        self._alpha_range = np.array([self.alpha**i for i in range(window)])
         self._alpha_range = np.expand_dims(self._alpha_range, axis=0)  # (1, window)
         return super().transform(df)
 
diff --git a/examples/EDA.ipynb b/examples/EDA.ipynb
index 1dcfb6a9a..8bcb534d0 100644
--- a/examples/EDA.ipynb
+++ b/examples/EDA.ipynb
@@ -43,6 +43,7 @@
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
@@ -609,7 +610,7 @@
     "    distribution_plot,\n",
     "    sample_acf_plot,\n",
     "    sample_pacf_plot,\n",
-    "    plot_correlation_matrix\n",
+    "    plot_correlation_matrix,\n",
     ")"
    ]
   },
@@ -773,7 +774,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "lags = LagTransform(in_column=\"target\", lags=[1,7], out_column=\"lag\")\n",
+    "lags = LagTransform(in_column=\"target\", lags=[1, 7], out_column=\"lag\")\n",
     "ts.fit_transform([lags])"
    ]
   },
@@ -797,7 +798,7 @@
     }
    ],
    "source": [
-    "plot_correlation_matrix(ts, segments=[\"segment_a\",\"segment_b\"], method=\"spearman\", vmin=0.5, vmax=1)"
+    "plot_correlation_matrix(ts, segments=[\"segment_a\", \"segment_b\"], method=\"spearman\", vmin=0.5, vmax=1)"
    ]
   },
   {
diff --git a/examples/NN_examples.ipynb b/examples/NN_examples.ipynb
index 6fa4f6622..132439590 100644
--- a/examples/NN_examples.ipynb
+++ b/examples/NN_examples.ipynb
@@ -52,6 +52,7 @@
     "from etna.models import SeasonalMovingAverageModel\n",
     "\n",
     "import warnings\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
@@ -449,13 +450,17 @@
     "\n",
     "transform_date = DateFlagsTransform(day_number_in_week=True, day_number_in_month=False, out_column=\"dateflag\")\n",
     "num_lags = 10\n",
-    "transform_lag = LagTransform(in_column=\"target\", lags=[HORIZON+i for i in range(num_lags)], out_column=\"target_lag\")\n",
+    "transform_lag = LagTransform(\n",
+    "    in_column=\"target\",\n",
+    "    lags=[HORIZON + i for i in range(num_lags)],\n",
+    "    out_column=\"target_lag\",\n",
+    ")\n",
     "lag_columns = [f\"target_lag_{HORIZON+i}\" for i in range(num_lags)]\n",
     "\n",
     "transform_deepar = PytorchForecastingTransform(\n",
     "    max_encoder_length=HORIZON,\n",
     "    max_prediction_length=HORIZON,\n",
-    "    time_varying_known_reals=[\"time_idx\"]+lag_columns,\n",
+    "    time_varying_known_reals=[\"time_idx\"] + lag_columns,\n",
     "    time_varying_unknown_reals=[\"target\"],\n",
     "    time_varying_known_categoricals=[\"dateflag_day_number_in_week\"],\n",
     "    target_normalizer=GroupNormalizer(groups=[\"segment\"]),\n",
@@ -483,9 +488,11 @@
     "model_deepar = DeepARModel(max_epochs=150, learning_rate=[0.01], gpus=0, batch_size=64)\n",
     "metrics = [SMAPE(), MAPE(), MAE()]\n",
     "\n",
-    "pipeline_deepar = Pipeline(model=model_deepar,\n",
-    "                           horizon=HORIZON,\n",
-    "                           transforms=[transform_lag, transform_date, transform_deepar])"
+    "pipeline_deepar = Pipeline(\n",
+    "    model=model_deepar,\n",
+    "    horizon=HORIZON,\n",
+    "    transforms=[transform_lag, transform_date, transform_deepar],\n",
+    ")"
    ]
   },
   {
@@ -884,7 +891,11 @@
    "source": [
     "transform_date = DateFlagsTransform(day_number_in_week=True, day_number_in_month=False, out_column=\"dateflag\")\n",
     "num_lags = 10\n",
-    "transform_lag = LagTransform(in_column=\"target\", lags=[HORIZON+i for i in range(num_lags)], out_column=\"target_lag\")\n",
+    "transform_lag = LagTransform(\n",
+    "    in_column=\"target\",\n",
+    "    lags=[HORIZON + i for i in range(num_lags)],\n",
+    "    out_column=\"target_lag\",\n",
+    ")\n",
     "lag_columns = [f\"target_lag_{HORIZON+i}\" for i in range(num_lags)]\n",
     "\n",
     "transform_tft = PytorchForecastingTransform(\n",
@@ -909,9 +920,11 @@
     "\n",
     "model_tft = TFTModel(max_epochs=200, learning_rate=[0.01], gpus=0, batch_size=64)\n",
     "\n",
-    "pipeline_tft = Pipeline(model=model_tft,\n",
-    "                        horizon=HORIZON,\n",
-    "                        transforms=[transform_lag, transform_date, transform_tft])"
+    "pipeline_tft = Pipeline(\n",
+    "    model=model_tft,\n",
+    "    horizon=HORIZON,\n",
+    "    transforms=[transform_lag, transform_date, transform_tft],\n",
+    ")"
    ]
   },
   {
@@ -1326,11 +1339,9 @@
    "outputs": [],
    "source": [
     "model_sma = SeasonalMovingAverageModel(window=5, seasonality=7)\n",
-    "linear_trend_transform = LinearTrendTransform(in_column='target')\n",
+    "linear_trend_transform = LinearTrendTransform(in_column=\"target\")\n",
     "\n",
-    "pipeline_sma = Pipeline(model=model_sma,\n",
-    "                        horizon=HORIZON,\n",
-    "                        transforms=[linear_trend_transform])"
+    "pipeline_sma = Pipeline(model=model_sma, horizon=HORIZON, transforms=[linear_trend_transform])"
    ]
   },
   {
diff --git a/examples/backtest.ipynb b/examples/backtest.ipynb
index 72b4caa0b..c0b1f4463 100644
--- a/examples/backtest.ipynb
+++ b/examples/backtest.ipynb
@@ -102,7 +102,7 @@
    "source": [
     "img = plt.imread(\"./assets/backtest/backtest.jpg\")\n",
     "plt.figure(figsize=(15, 10))\n",
-    "plt.axis('off')\n",
+    "plt.axis(\"off\")\n",
     "_ = plt.imshow(img)"
    ]
   },
@@ -198,7 +198,7 @@
    "source": [
     "df = pd.read_csv(\"./data/example_dataset.csv\")\n",
     "df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n",
-    "df = df.loc[df.segment == 'segment_a']\n",
+    "df = df.loc[df.segment == \"segment_a\"]\n",
     "df.head()"
    ]
   },
@@ -218,7 +218,7 @@
    "outputs": [],
    "source": [
     "df = TSDataset.to_dataset(df)\n",
-    "ts = TSDataset(df, freq='D')"
+    "ts = TSDataset(df, freq=\"D\")"
    ]
   },
   {
@@ -249,7 +249,7 @@
     }
    ],
    "source": [
-    "ts.plot(segments=['segment_a'])"
+    "ts.plot(segments=[\"segment_a\"])"
    ]
   },
   {
@@ -275,9 +275,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "horizon = 31 # Set the horizon for predictions\n",
-    "model = ProphetModel() # Create a model\n",
-    "transforms = [] #A list of transforms -  we will not use any of them"
+    "horizon = 31  # Set the horizon for predictions\n",
+    "model = ProphetModel()  # Create a model\n",
+    "transforms = []  # A list of transforms -  we will not use any of them"
    ]
   },
   {
@@ -463,9 +463,7 @@
     }
    ],
    "source": [
-    "metrics_df, forecast_df, fold_info_df = pipeline.backtest(\n",
-    "    ts=ts, metrics=[MAE(), MSE(), SMAPE()]\n",
-    ")"
+    "metrics_df, forecast_df, fold_info_df = pipeline.backtest(ts=ts, metrics=[MAE(), MSE(), SMAPE()])"
    ]
   },
   {
@@ -931,9 +929,7 @@
    ],
    "source": [
     "metrics_df, forecast_df, fold_info_df = pipeline.backtest(\n",
-    "    ts=ts,\n",
-    "    metrics=[MAE(), MSE(), SMAPE()],\n",
-    "    aggregate_metrics=True\n",
+    "    ts=ts, metrics=[MAE(), MSE(), SMAPE()], aggregate_metrics=True\n",
     ")"
    ]
   },
diff --git a/examples/clustering.ipynb b/examples/clustering.ipynb
index f5736bd4e..47bbc1c98 100644
--- a/examples/clustering.ipynb
+++ b/examples/clustering.ipynb
@@ -41,6 +41,7 @@
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
@@ -80,7 +81,7 @@
     "        for j, sigma in enumerate([0.1, 0.3, 0.5, 0.8]):\n",
     "            tmp = pd.DataFrame({\"timestamp\": date_range})\n",
     "            tmp[\"segment\"] = f\"{2*i}{j}\"\n",
-    "            tmp[\"target\"] = np.random.normal(2*i, sigma, len(tmp))\n",
+    "            tmp[\"target\"] = np.random.normal(2 * i, sigma, len(tmp))\n",
     "            df = df.append(tmp, ignore_index=True)\n",
     "    ts = TSDataset(df=TSDataset.to_dataset(df), freq=\"D\")\n",
     "    return ts"
@@ -117,7 +118,7 @@
    ],
    "source": [
     "ts = gen_dataset()\n",
-    "ts.df.plot(figsize=(20,10),legend=False);"
+    "ts.df.plot(figsize=(20, 10), legend=False);"
    ]
   },
   {
@@ -156,10 +157,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "x1 = pd.Series(data = [0, 0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
-    "               index = pd.date_range(\"2020-01-01\", periods=10))\n",
-    "x2 = pd.Series(data = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
-    "               index = pd.date_range(\"2020-01-02\", periods=10))"
+    "x1 = pd.Series(\n",
+    "    data=[0, 0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
+    "    index=pd.date_range(\"2020-01-01\", periods=10),\n",
+    ")\n",
+    "x2 = pd.Series(\n",
+    "    data=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
+    "    index=pd.date_range(\"2020-01-02\", periods=10),\n",
+    ")"
    ]
   },
   {
@@ -190,7 +195,7 @@
    ],
    "source": [
     "distance = EuclideanDistance(trim_series=True)\n",
-    "distance(x1,x2) # Series are the same in the common part of the timestamps "
+    "distance(x1, x2)  # Series are the same in the common part of the timestamps"
    ]
   },
   {
@@ -220,7 +225,7 @@
    ],
    "source": [
     "distance = EuclideanDistance(trim_series=False)\n",
-    "distance(x1,x2) "
+    "distance(x1, x2)"
    ]
   },
   {
@@ -254,7 +259,8 @@
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "_ = plt.subplots(figsize=(15,10))\n",
+    "\n",
+    "_ = plt.subplots(figsize=(15, 10))\n",
     "img = plt.imread(\"./assets/clustering/trim_series.jpg\")\n",
     "plt.imshow(img);"
    ]
@@ -332,11 +338,17 @@
    ],
    "source": [
     "distances = pd.DataFrame()\n",
-    "distances[\"Euclidean\"] = [EuclideanDistance(trim_series=True)(x1,x2),EuclideanDistance(trim_series=False)(x1,x2)]\n",
-    "distances[\"DTW\"] = [DTWDistance(trim_series=True)(x1,x2),DTWDistance(trim_series=False)(x1,x2)]\n",
-    "distances[\"trim_series\"] = [True,False]\n",
-    "distances.set_index(\"trim_series\",inplace=True)\n",
-    "distances  "
+    "distances[\"Euclidean\"] = [\n",
+    "    EuclideanDistance(trim_series=True)(x1, x2),\n",
+    "    EuclideanDistance(trim_series=False)(x1, x2),\n",
+    "]\n",
+    "distances[\"DTW\"] = [\n",
+    "    DTWDistance(trim_series=True)(x1, x2),\n",
+    "    DTWDistance(trim_series=False)(x1, x2),\n",
+    "]\n",
+    "distances[\"trim_series\"] = [True, False]\n",
+    "distances.set_index(\"trim_series\", inplace=True)\n",
+    "distances"
    ]
   },
   {
@@ -467,7 +479,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.build_clustering_algo(n_clusters=4,linkage=\"average\")"
+    "model.build_clustering_algo(n_clusters=4, linkage=\"average\")"
    ]
   },
   {
@@ -545,9 +557,9 @@
     }
    ],
    "source": [
-    "colores = ['r','g','b','y']\n",
+    "colores = [\"r\", \"g\", \"b\", \"y\"]\n",
     "color = [colores[i] for i in segment2cluster.values()]\n",
-    "ts.df.plot(figsize=(20,10),color=color,legend=False);"
+    "ts.df.plot(figsize=(20, 10), color=color, legend=False);"
    ]
   },
   {
@@ -712,7 +724,7 @@
     }
    ],
    "source": [
-    "plot_clusters(ts,segment2cluster,centroids)"
+    "plot_clusters(ts, segment2cluster, centroids)"
    ]
   },
   {
@@ -742,18 +754,23 @@
    "source": [
     "from etna.clustering import Distance\n",
     "\n",
+    "\n",
     "class MyDistance(Distance):\n",
-    "    \n",
     "    def __init__(self, trim_series: bool = False):\n",
     "        super().__init__(trim_series=trim_series)\n",
     "\n",
     "    def _compute_distance(self, x1: np.ndarray, x2: np.ndarray) -> float:\n",
     "        \"\"\"Compute distance between x1 and x2.\"\"\"\n",
-    "        return np.max(np.abs(x1-x2))\n",
+    "        return np.max(np.abs(x1 - x2))\n",
     "\n",
     "    def _get_average(self, ts: \"TSDataset\") -> pd.DataFrame:\n",
     "        \"\"\"Get series that minimizes squared distance to given ones according to the distance.\"\"\"\n",
-    "        centroid = pd.DataFrame({\"timestamp\": ts.index.values, \"target\": ts.df.median(axis=1).values})\n",
+    "        centroid = pd.DataFrame(\n",
+    "            {\n",
+    "                \"timestamp\": ts.index.values,\n",
+    "                \"target\": ts.df.median(axis=1).values,\n",
+    "            }\n",
+    "        )\n",
     "        return centroid"
    ]
   },
@@ -818,10 +835,13 @@
    ],
    "source": [
     "distances = pd.DataFrame()\n",
-    "distances[\"MyDistance\"] = [MyDistance(trim_series=True)(x1,x2),MyDistance(trim_series=False)(x1,x2)]\n",
-    "distances[\"trim_series\"] = [True,False]\n",
-    "distances.set_index(\"trim_series\",inplace=True)\n",
-    "distances  "
+    "distances[\"MyDistance\"] = [\n",
+    "    MyDistance(trim_series=True)(x1, x2),\n",
+    "    MyDistance(trim_series=False)(x1, x2),\n",
+    "]\n",
+    "distances[\"trim_series\"] = [True, False]\n",
+    "distances.set_index(\"trim_series\", inplace=True)\n",
+    "distances"
    ]
   },
   {
diff --git a/examples/custom_transform_and_model.ipynb b/examples/custom_transform_and_model.ipynb
index f7d516db6..1853ad74f 100644
--- a/examples/custom_transform_and_model.ipynb
+++ b/examples/custom_transform_and_model.ipynb
@@ -54,6 +54,7 @@
     "from etna.transforms import DateFlagsTransform, LinearTrendTransform\n",
     "\n",
     "import warnings\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
@@ -719,14 +720,14 @@
     "        floor:\n",
     "            lower bound\n",
     "        ceil:\n",
-    "            upper bound \n",
+    "            upper bound\n",
     "        \"\"\"\n",
     "        self.in_column = in_column\n",
     "        self.floor = floor\n",
     "        self.ceil = ceil\n",
     "\n",
     "    # Provide the necessary training. For example calculates the coefficients of a linear trend.\n",
-    "    # In this case, we calculate the indices that need to be changed \n",
+    "    # In this case, we calculate the indices that need to be changed\n",
     "    # and remember the old values for inverse transform.\n",
     "    def fit(self, df: pd.DataFrame) -> \"_OneSegmentFloorCeilTransform\":\n",
     "        \"\"\"\n",
@@ -764,14 +765,13 @@
     "        result_df[self.in_column].iloc[self.floor_indices] = self.floor\n",
     "        result_df[self.in_column].iloc[self.ceil_indices] = self.ceil\n",
     "\n",
-    "\n",
     "        return result_df\n",
     "\n",
     "    # Do it all in one action. Base class requirement.\n",
     "    def fit_transform(self, df: pd.DataFrame) -> pd.DataFrame:\n",
     "        return self.fit(df).transform(df)\n",
     "\n",
-    "    # Returns back changed values. \n",
+    "    # Returns back changed values.\n",
     "    def inverse_transform(self, df: pd.DataFrame) -> pd.DataFrame:\n",
     "        \"\"\"\n",
     "        Inverse transformation for transform. Return back changed values.\n",
@@ -790,7 +790,7 @@
     "        result[self.in_column][self.floor_indices] = self.floor_values\n",
     "        result[self.in_column][self.ceil_indices] = self.ceil_values\n",
     "\n",
-    "        return result\n"
+    "        return result"
    ]
   },
   {
@@ -824,16 +824,14 @@
     "        floor:\n",
     "            lower bound\n",
     "        ceil:\n",
-    "            upper bound \n",
+    "            upper bound\n",
     "        \"\"\"\n",
     "        self.in_column = in_column\n",
     "        self.floor = floor\n",
     "        self.ceil = ceil\n",
     "        super().__init__(\n",
-    "            transform=_OneSegmentFloorCeilTransform(\n",
-    "                in_column=self.in_column, floor=self.floor, ceil=self.ceil\n",
-    "            )\n",
-    "        )\n"
+    "            transform=_OneSegmentFloorCeilTransform(in_column=self.in_column, floor=self.floor, ceil=self.ceil)\n",
+    "        )"
    ]
   },
   {
@@ -1094,7 +1092,7 @@
     "            max_depth=max_depth,\n",
     "            learning_rate=learning_rate,\n",
     "            n_estimators=n_estimators,\n",
-    "            **kwargs\n",
+    "            **kwargs,\n",
     "        )\n",
     "        self._categorical = None\n",
     "\n",
@@ -1109,7 +1107,7 @@
     "        features = df.drop(columns=[\"timestamp\", \"target\"])\n",
     "        pred = self.model.predict(features)\n",
     "        return pred\n",
-    "    \n",
+    "\n",
     "    def get_model(self) -> LGBMRegressor:\n",
     "        return self.model"
    ]
@@ -1150,13 +1148,13 @@
     "        self.n_estimators = n_estimators\n",
     "        self.kwargs = kwargs\n",
     "        model = _LGBMAdapter(\n",
-    "                boosting_type=boosting_type,\n",
-    "                num_leaves=num_leaves,\n",
-    "                max_depth=max_depth,\n",
-    "                learning_rate=learning_rate,\n",
-    "                n_estimators=n_estimators,\n",
-    "                **kwargs\n",
-    "            )\n",
+    "            boosting_type=boosting_type,\n",
+    "            num_leaves=num_leaves,\n",
+    "            max_depth=max_depth,\n",
+    "            learning_rate=learning_rate,\n",
+    "            n_estimators=n_estimators,\n",
+    "            **kwargs,\n",
+    "        )\n",
     "        super().__init__(base_model=model)"
    ]
   },
@@ -1194,13 +1192,13 @@
     "        self.n_estimators = n_estimators\n",
     "        self.kwargs = kwargs\n",
     "        model = _LGBMAdapter(\n",
-    "                boosting_type=boosting_type,\n",
-    "                num_leaves=num_leaves,\n",
-    "                max_depth=max_depth,\n",
-    "                learning_rate=learning_rate,\n",
-    "                n_estimators=n_estimators,\n",
-    "                **kwargs\n",
-    "            )\n",
+    "            boosting_type=boosting_type,\n",
+    "            num_leaves=num_leaves,\n",
+    "            max_depth=max_depth,\n",
+    "            learning_rate=learning_rate,\n",
+    "            n_estimators=n_estimators,\n",
+    "            **kwargs,\n",
+    "        )\n",
     "        super().__init__(base_model=model)"
    ]
   },
@@ -1223,10 +1221,12 @@
    "source": [
     "HORIZON = 31\n",
     "\n",
-    "train_ts, test_ts = ts.train_test_split(train_start=\"2019-01-01\",\n",
-    "                                        train_end=\"2019-11-30\",\n",
-    "                                        test_start=\"2019-12-01\",\n",
-    "                                        test_end=\"2019-12-31\")"
+    "train_ts, test_ts = ts.train_test_split(\n",
+    "    train_start=\"2019-01-01\",\n",
+    "    train_end=\"2019-11-30\",\n",
+    "    test_start=\"2019-12-01\",\n",
+    "    test_end=\"2019-12-31\",\n",
+    ")"
    ]
   },
   {
@@ -1237,7 +1237,7 @@
    },
    "outputs": [],
    "source": [
-    "from etna.transforms import LagTransform \n",
+    "from etna.transforms import LagTransform\n",
     "from etna.transforms import LogTransform\n",
     "from etna.transforms import SegmentEncoderTransform\n",
     "from etna.transforms import DateFlagsTransform\n",
@@ -1248,14 +1248,16 @@
     "seg = SegmentEncoderTransform()\n",
     "\n",
     "lags = LagTransform(in_column=\"target\", lags=list(range(31, 96, 1)), out_column=\"lag\")\n",
-    "d_flags = DateFlagsTransform(day_number_in_week=True,\n",
-    "                             day_number_in_month=True,\n",
-    "                             week_number_in_month=True,\n",
-    "                             week_number_in_year=True,\n",
-    "                             month_number_in_year=True,\n",
-    "                             year_number=True,\n",
-    "                             special_days_in_week=[5, 6], \n",
-    "                             out_column=\"date_feature\")\n",
+    "d_flags = DateFlagsTransform(\n",
+    "    day_number_in_week=True,\n",
+    "    day_number_in_month=True,\n",
+    "    week_number_in_month=True,\n",
+    "    week_number_in_year=True,\n",
+    "    month_number_in_year=True,\n",
+    "    year_number=True,\n",
+    "    special_days_in_week=[5, 6],\n",
+    "    out_column=\"date_feature\",\n",
+    ")\n",
     "\n",
     "transforms = [trend, lags, d_flags, seg]"
    ]
diff --git a/examples/ensembles.ipynb b/examples/ensembles.ipynb
index 5ac995647..ce4996962 100644
--- a/examples/ensembles.ipynb
+++ b/examples/ensembles.ipynb
@@ -37,6 +37,7 @@
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
@@ -110,9 +111,14 @@
    "outputs": [],
    "source": [
     "from etna.pipeline import Pipeline\n",
-    "from etna.models import NaiveModel, SeasonalMovingAverageModel, CatBoostModelMultiSegment\n",
+    "from etna.models import (\n",
+    "    NaiveModel,\n",
+    "    SeasonalMovingAverageModel,\n",
+    "    CatBoostModelMultiSegment,\n",
+    ")\n",
     "from etna.transforms import LagTransform\n",
     "from etna.metrics import MAE, MSE, SMAPE, MAPE\n",
+    "\n",
     "HORIZON = 3\n",
     "N_FOLDS = 5"
    ]
@@ -134,7 +140,9 @@
    "source": [
     "naive_pipeline = Pipeline(model=NaiveModel(lag=12), transforms=[], horizon=HORIZON)\n",
     "seasonalma_pipeline = Pipeline(\n",
-    "    model=SeasonalMovingAverageModel(window=5, seasonality=12), transforms=[], horizon=HORIZON\n",
+    "    model=SeasonalMovingAverageModel(window=5, seasonality=12),\n",
+    "    transforms=[],\n",
+    "    horizon=HORIZON,\n",
     ")\n",
     "catboost_pipeline = Pipeline(\n",
     "    model=CatBoostModelMultiSegment(),\n",
@@ -255,7 +263,11 @@
     "for pipeline in pipelines:\n",
     "    metrics.append(\n",
     "        pipeline.backtest(\n",
-    "            ts=ts, metrics=[MAE(), MSE(), SMAPE(), MAPE()], n_folds=N_FOLDS, aggregate_metrics=True, n_jobs=5\n",
+    "            ts=ts,\n",
+    "            metrics=[MAE(), MSE(), SMAPE(), MAPE()],\n",
+    "            n_folds=N_FOLDS,\n",
+    "            aggregate_metrics=True,\n",
+    "            n_jobs=5,\n",
     "        )[0].iloc[:, 1:]\n",
     "    )\n",
     "metrics = pd.concat(metrics)\n",
@@ -441,7 +453,11 @@
    ],
    "source": [
     "voting_ensamble_metrics = voting_ensemble.backtest(\n",
-    "    ts=ts, metrics=[MAE(), MSE(), SMAPE(), MAPE()], n_folds=N_FOLDS, aggregate_metrics=True, n_jobs=2\n",
+    "    ts=ts,\n",
+    "    metrics=[MAE(), MSE(), SMAPE(), MAPE()],\n",
+    "    n_folds=N_FOLDS,\n",
+    "    aggregate_metrics=True,\n",
+    "    n_jobs=2,\n",
     ")[0].iloc[:, 1:]\n",
     "voting_ensamble_metrics.index = [\"voting ensemble\"]\n",
     "voting_ensamble_metrics"
@@ -835,7 +851,11 @@
    ],
    "source": [
     "stacking_ensamble_metrics = stacking_ensemble_unfeatured.backtest(\n",
-    "    ts=ts, metrics=[MAE(), MSE(), SMAPE(), MAPE()], n_folds=N_FOLDS, aggregate_metrics=True, n_jobs=2\n",
+    "    ts=ts,\n",
+    "    metrics=[MAE(), MSE(), SMAPE(), MAPE()],\n",
+    "    n_folds=N_FOLDS,\n",
+    "    aggregate_metrics=True,\n",
+    "    n_jobs=2,\n",
     ")[0].iloc[:, 1:]\n",
     "stacking_ensamble_metrics.index = [\"stacking ensemble\"]\n",
     "stacking_ensamble_metrics"
@@ -946,13 +966,7 @@
     }
    ],
    "source": [
-    "metrics = pd.concat(\n",
-    "    [\n",
-    "        metrics,\n",
-    "        voting_ensamble_metrics,\n",
-    "        stacking_ensamble_metrics\n",
-    "    ]\n",
-    ")\n",
+    "metrics = pd.concat([metrics, voting_ensamble_metrics, stacking_ensamble_metrics])\n",
     "metrics"
    ]
   }
diff --git a/examples/exogenous_data.ipynb b/examples/exogenous_data.ipynb
index 6a7a5a219..768428329 100644
--- a/examples/exogenous_data.ipynb
+++ b/examples/exogenous_data.ipynb
@@ -1145,27 +1145,50 @@
    "source": [
     "from etna.transforms import FilterFeaturesTransform\n",
     "\n",
-    "from etna.transforms import MeanTransform # math\n",
-    "from etna.transforms import DateFlagsTransform, HolidayTransform # datetime\n",
-    "from etna.transforms import LagTransform # lags\n",
+    "from etna.transforms import MeanTransform  # math\n",
+    "from etna.transforms import DateFlagsTransform, HolidayTransform  # datetime\n",
+    "from etna.transforms import LagTransform  # lags\n",
     "\n",
     "transforms = [\n",
-    "              LagTransform(in_column=\"target\", lags=list(range(HORIZON, HORIZON+28)), out_column=\"target_lag\"),\n",
-    "              LagTransform(in_column=\"tavg\", lags=list(range(1, 3)), out_column=\"tavg_lag\"),\n",
-    "              MeanTransform(in_column=\"tavg\", window=7, out_column=\"tavg_mean\"),\n",
-    "              MeanTransform(in_column=\"target_lag_365\", out_column=\"target_mean\", window=104, seasonality=7),\n",
-    "              DateFlagsTransform(day_number_in_week=True,\n",
-    "                                 day_number_in_month=True,\n",
-    "                                 is_weekend=True,\n",
-    "                                 special_days_in_week=[4],\n",
-    "                                 out_column=\"date_flag\"),\n",
-    "              HolidayTransform(iso_code=\"SWE\", out_column=\"SWE_holidays\"),\n",
-    "              HolidayTransform(iso_code=\"NOR\", out_column=\"NOR_holidays\"),\n",
-    "              HolidayTransform(iso_code=\"FIN\", out_column=\"FIN_holidays\"),\n",
-    "              LagTransform(in_column=\"SWE_holidays\", lags=list(range(2,6)), out_column=\"SWE_holidays_lag\"),\n",
-    "              LagTransform(in_column=\"NOR_holidays\", lags=list(range(2,6)), out_column=\"NOR_holidays_lag\"),\n",
-    "              LagTransform(in_column=\"FIN_holidays\", lags=list(range(2,6)), out_column=\"FIN_holidays_lag\"),\n",
-    "              FilterFeaturesTransform(exclude=[\"precipitation\", \"snow_depth\", \"tmin\", \"tmax\"]),\n",
+    "    LagTransform(\n",
+    "        in_column=\"target\",\n",
+    "        lags=list(range(HORIZON, HORIZON + 28)),\n",
+    "        out_column=\"target_lag\",\n",
+    "    ),\n",
+    "    LagTransform(in_column=\"tavg\", lags=list(range(1, 3)), out_column=\"tavg_lag\"),\n",
+    "    MeanTransform(in_column=\"tavg\", window=7, out_column=\"tavg_mean\"),\n",
+    "    MeanTransform(\n",
+    "        in_column=\"target_lag_365\",\n",
+    "        out_column=\"target_mean\",\n",
+    "        window=104,\n",
+    "        seasonality=7,\n",
+    "    ),\n",
+    "    DateFlagsTransform(\n",
+    "        day_number_in_week=True,\n",
+    "        day_number_in_month=True,\n",
+    "        is_weekend=True,\n",
+    "        special_days_in_week=[4],\n",
+    "        out_column=\"date_flag\",\n",
+    "    ),\n",
+    "    HolidayTransform(iso_code=\"SWE\", out_column=\"SWE_holidays\"),\n",
+    "    HolidayTransform(iso_code=\"NOR\", out_column=\"NOR_holidays\"),\n",
+    "    HolidayTransform(iso_code=\"FIN\", out_column=\"FIN_holidays\"),\n",
+    "    LagTransform(\n",
+    "        in_column=\"SWE_holidays\",\n",
+    "        lags=list(range(2, 6)),\n",
+    "        out_column=\"SWE_holidays_lag\",\n",
+    "    ),\n",
+    "    LagTransform(\n",
+    "        in_column=\"NOR_holidays\",\n",
+    "        lags=list(range(2, 6)),\n",
+    "        out_column=\"NOR_holidays_lag\",\n",
+    "    ),\n",
+    "    LagTransform(\n",
+    "        in_column=\"FIN_holidays\",\n",
+    "        lags=list(range(2, 6)),\n",
+    "        out_column=\"FIN_holidays_lag\",\n",
+    "    ),\n",
+    "    FilterFeaturesTransform(exclude=[\"precipitation\", \"snow_depth\", \"tmin\", \"tmax\"]),\n",
     "]"
    ]
   },
@@ -1191,6 +1214,7 @@
    "outputs": [],
    "source": [
     "from etna.pipeline import Pipeline\n",
+    "\n",
     "pipeline = Pipeline(model=model, transforms=transforms, horizon=HORIZON)"
    ]
   },
@@ -1406,7 +1430,7 @@
    "source": [
     "from etna.analysis import plot_backtest\n",
     "\n",
-    "plot_backtest(forecasts, ts)\n"
+    "plot_backtest(forecasts, ts)"
    ]
   },
   {
diff --git a/examples/get_started.ipynb b/examples/get_started.ipynb
index 3d3d094a5..11c1cc112 100644
--- a/examples/get_started.ipynb
+++ b/examples/get_started.ipynb
@@ -605,10 +605,12 @@
    },
    "outputs": [],
    "source": [
-    "train_ts, test_ts = ts.train_test_split(train_start=\"1980-01-01\",\n",
-    "                                        train_end=\"1993-12-01\",\n",
-    "                                        test_start=\"1994-01-01\",\n",
-    "                                        test_end=\"1994-08-01\")"
+    "train_ts, test_ts = ts.train_test_split(\n",
+    "    train_start=\"1980-01-01\",\n",
+    "    train_end=\"1993-12-01\",\n",
+    "    test_start=\"1994-01-01\",\n",
+    "    test_end=\"1994-08-01\",\n",
+    ")"
    ]
   },
   {
@@ -624,11 +626,11 @@
     "HORIZON = 8\n",
     "from etna.models import NaiveModel\n",
     "\n",
-    "#Fit the model\n",
+    "# Fit the model\n",
     "model = NaiveModel(lag=12)\n",
     "model.fit(train_ts)\n",
     "\n",
-    "#Make the forecast\n",
+    "# Make the forecast\n",
     "future_ts = train_ts.make_future(HORIZON)\n",
     "forecast_ts = model.forecast(future_ts)"
    ]
@@ -782,7 +784,7 @@
     "model = ProphetModel()\n",
     "model.fit(train_ts)\n",
     "\n",
-    "#Make the forecast\n",
+    "# Make the forecast\n",
     "future_ts = train_ts.make_future(HORIZON)\n",
     "forecast_ts = model.forecast(future_ts)"
    ]
@@ -808,7 +810,7 @@
     }
    ],
    "source": [
-    "smape(y_true=test_ts, y_pred=forecast_ts)\n"
+    "smape(y_true=test_ts, y_pred=forecast_ts)"
    ]
   },
   {
@@ -1106,8 +1108,14 @@
    "source": [
     "import warnings\n",
     "\n",
-    "from etna.transforms import MeanTransform, LagTransform, LogTransform, \\\n",
-    "    SegmentEncoderTransform, DateFlagsTransform, LinearTrendTransform\n",
+    "from etna.transforms import (\n",
+    "    MeanTransform,\n",
+    "    LagTransform,\n",
+    "    LogTransform,\n",
+    "    SegmentEncoderTransform,\n",
+    "    DateFlagsTransform,\n",
+    "    LinearTrendTransform,\n",
+    ")\n",
     "\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
@@ -1116,13 +1124,15 @@
     "seg = SegmentEncoderTransform()\n",
     "\n",
     "lags = LagTransform(in_column=\"target\", lags=list(range(30, 96, 1)))\n",
-    "d_flags = DateFlagsTransform(day_number_in_week=True,\n",
-    "                             day_number_in_month=True,\n",
-    "                             week_number_in_month=True,\n",
-    "                             week_number_in_year=True,\n",
-    "                             month_number_in_year=True,\n",
-    "                             year_number=True,\n",
-    "                             special_days_in_week=[5, 6])\n",
+    "d_flags = DateFlagsTransform(\n",
+    "    day_number_in_week=True,\n",
+    "    day_number_in_month=True,\n",
+    "    week_number_in_month=True,\n",
+    "    week_number_in_year=True,\n",
+    "    month_number_in_year=True,\n",
+    "    year_number=True,\n",
+    "    special_days_in_week=[5, 6],\n",
+    ")\n",
     "mean30 = MeanTransform(in_column=\"target\", window=30)"
    ]
   },
@@ -1137,10 +1147,12 @@
    "outputs": [],
    "source": [
     "HORIZON = 30\n",
-    "train_ts, test_ts = ts.train_test_split(train_start=\"2019-01-01\",\n",
-    "                                        train_end=\"2019-10-31\",\n",
-    "                                        test_start=\"2019-11-01\",\n",
-    "                                        test_end=\"2019-11-30\")\n",
+    "train_ts, test_ts = ts.train_test_split(\n",
+    "    train_start=\"2019-01-01\",\n",
+    "    train_end=\"2019-10-31\",\n",
+    "    test_start=\"2019-11-01\",\n",
+    "    test_end=\"2019-11-30\",\n",
+    ")\n",
     "train_ts.fit_transform([log, trend, lags, d_flags, seg, mean30])"
    ]
   },
@@ -1269,10 +1281,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_ts, test_ts = ts.train_test_split(train_start=\"2019-01-01\",\n",
-    "                                        train_end=\"2019-10-31\",\n",
-    "                                        test_start=\"2019-11-01\",\n",
-    "                                        test_end=\"2019-11-30\")"
+    "train_ts, test_ts = ts.train_test_split(\n",
+    "    train_start=\"2019-01-01\",\n",
+    "    train_end=\"2019-10-31\",\n",
+    "    test_start=\"2019-11-01\",\n",
+    "    test_end=\"2019-11-30\",\n",
+    ")"
    ]
   },
   {
@@ -1288,9 +1302,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = Pipeline(model=CatBoostModelMultiSegment(),\n",
-    "                transforms=[log, trend, lags, d_flags, seg, mean30],\n",
-    "                horizon=HORIZON)\n",
+    "model = Pipeline(\n",
+    "    model=CatBoostModelMultiSegment(),\n",
+    "    transforms=[log, trend, lags, d_flags, seg, mean30],\n",
+    "    horizon=HORIZON,\n",
+    ")\n",
     "model.fit(train_ts)\n",
     "forecast_ts = model.forecast()"
    ]
@@ -1350,7 +1366,7 @@
     }
    ],
    "source": [
-    "plot_forecast(forecast_ts, test_ts, train_ts, n_train_samples=20)\n"
+    "plot_forecast(forecast_ts, test_ts, train_ts, n_train_samples=20)"
    ]
   }
  ],
diff --git a/examples/outliers.ipynb b/examples/outliers.ipynb
index 7840e9536..5f7445732 100644
--- a/examples/outliers.ipynb
+++ b/examples/outliers.ipynb
@@ -39,6 +39,7 @@
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
@@ -242,7 +243,8 @@
     "    get_anomalies_median,\n",
     "    get_anomalies_density,\n",
     "    get_anomalies_prediction_interval,\n",
-    "    get_anomalies_hist)\n",
+    "    get_anomalies_hist,\n",
+    ")\n",
     "from etna.analysis import plot_anomalies"
    ]
   },
@@ -496,8 +498,7 @@
    "source": [
     "segment = \"segment_c\"\n",
     "method = get_anomalies_median\n",
-    "params_bounds = {\"window_size\": (40, 70, 1),\n",
-    "                 \"alpha\": (0.1, 4, 0.25)}"
+    "params_bounds = {\"window_size\": (40, 70, 1), \"alpha\": (0.1, 4, 0.25)}"
    ]
   },
   {
@@ -530,7 +531,7 @@
     }
    ],
    "source": [
-    "plot_anomalies_interactive(ts=ts,segment=segment,method=method,params_bounds=params_bounds)"
+    "plot_anomalies_interactive(ts=ts, segment=segment, method=method, params_bounds=params_bounds)"
    ]
   },
   {
@@ -548,8 +549,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "best_params = {\"window_size\":60,\n",
-    "              \"alpha\":2.35}"
+    "best_params = {\"window_size\": 60, \"alpha\": 2.35}"
    ]
   },
   {
@@ -601,7 +601,7 @@
     }
    ],
    "source": [
-    "df = ts[:,segment,:]\n",
+    "df = ts[:, segment, :]\n",
     "ts = TSDataset(df, freq=\"D\")\n",
     "ts.plot()"
    ]
@@ -639,10 +639,10 @@
    ],
    "source": [
     "# Impute outliers with NaNs\n",
-    "outliers_remover = MedianOutliersTransform(in_column=\"target\",**best_params)\n",
+    "outliers_remover = MedianOutliersTransform(in_column=\"target\", **best_params)\n",
     "# Impute NaNs using the specified strategy\n",
-    "outliers_imputer = TimeSeriesImputerTransform(in_column=\"target\",strategy=\"running_mean\",window=30)\n",
-    "ts.fit_transform([outliers_remover,outliers_imputer])\n",
+    "outliers_imputer = TimeSeriesImputerTransform(in_column=\"target\", strategy=\"running_mean\", window=30)\n",
+    "ts.fit_transform([outliers_remover, outliers_imputer])\n",
     "ts.plot()"
    ]
   },
@@ -663,7 +663,7 @@
    "source": [
     "from etna.models import MovingAverageModel\n",
     "from etna.pipeline import Pipeline\n",
-    "from etna.metrics import MAE,MSE,SMAPE"
+    "from etna.metrics import MAE, MSE, SMAPE"
    ]
   },
   {
@@ -673,13 +673,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_metrics(forecast,test):\n",
+    "def get_metrics(forecast, test):\n",
     "    \"\"\"Compute the metrics on forecast\"\"\"\n",
-    "    metrics = {\"MAE\":MAE(),\"MSE\":MSE(),\"SMAPE\":SMAPE()}\n",
+    "    metrics = {\"MAE\": MAE(), \"MSE\": MSE(), \"SMAPE\": SMAPE()}\n",
     "    results = dict()\n",
-    "    for name,metric in metrics.items():\n",
+    "    for name, metric in metrics.items():\n",
     "        results[name] = metric(y_true=test, y_pred=forecast)[\"segment_c\"]\n",
-    "    return results  "
+    "    return results"
    ]
   },
   {
@@ -692,19 +692,20 @@
     "def test_transforms(transforms=[]):\n",
     "    \"\"\"Run the experiment on the list of transforms\"\"\"\n",
     "    classic_df = pd.read_csv(\"data/example_dataset.csv\")\n",
-    "    df = TSDataset.to_dataset(classic_df[classic_df[\"segment\"]==segment])\n",
+    "    df = TSDataset.to_dataset(classic_df[classic_df[\"segment\"] == segment])\n",
     "    ts = TSDataset(df, freq=\"D\")\n",
-    "    train,test = ts.train_test_split(train_start=\"2019-05-20\",\n",
-    "                                 train_end=\"2019-07-10\",\n",
-    "                                 test_start=\"2019-07-11\",\n",
-    "                                 test_end=\"2019-08-09\")\n",
-    "    \n",
-    "    model = Pipeline(model=MovingAverageModel(window=30),transforms=transforms,horizon=30)\n",
+    "    train, test = ts.train_test_split(\n",
+    "        train_start=\"2019-05-20\",\n",
+    "        train_end=\"2019-07-10\",\n",
+    "        test_start=\"2019-07-11\",\n",
+    "        test_end=\"2019-08-09\",\n",
+    "    )\n",
+    "\n",
+    "    model = Pipeline(model=MovingAverageModel(window=30), transforms=transforms, horizon=30)\n",
     "    model.fit(train)\n",
     "    forecast = model.forecast()\n",
-    "    metrics = get_metrics(forecast,test)\n",
-    "    return metrics\n",
-    "    "
+    "    metrics = get_metrics(forecast, test)\n",
+    "    return metrics"
    ]
   },
   {
@@ -766,7 +767,7 @@
     }
    ],
    "source": [
-    "transforms = [outliers_remover,outliers_imputer]\n",
+    "transforms = [outliers_remover, outliers_imputer]\n",
     "test_transforms(transforms)"
    ]
   },
diff --git a/poetry.lock b/poetry.lock
index a93229fac..e467874f2 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -163,7 +163,7 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "babel"
-version = "2.10.1"
+version = "2.10.2"
 description = "Internationalization utilities"
 category = "main"
 optional = true
@@ -197,27 +197,27 @@ lxml = ["lxml"]
 
 [[package]]
 name = "black"
-version = "21.9b0"
+version = "22.3.0"
 description = "The uncompromising code formatter."
 category = "main"
 optional = true
 python-versions = ">=3.6.2"
 
 [package.dependencies]
-click = ">=7.1.2"
+click = ">=8.0.0"
+ipython = {version = ">=7.8.0", optional = true, markers = "extra == \"jupyter\""}
 mypy-extensions = ">=0.4.3"
-pathspec = ">=0.9.0,<1"
+pathspec = ">=0.9.0"
 platformdirs = ">=2"
-regex = ">=2020.1.8"
-tomli = ">=0.2.6,<2.0.0"
-typed-ast = {version = ">=1.4.2", markers = "python_version < \"3.8\""}
-typing-extensions = ">=3.10.0.0"
+tokenize-rt = {version = ">=3.2.0", optional = true, markers = "extra == \"jupyter\""}
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+typed-ast = {version = ">=1.4.2", markers = "python_version < \"3.8\" and implementation_name == \"cpython\""}
+typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
-d = ["aiohttp (>=3.6.0)", "aiohttp-cors (>=0.4.0)"]
+d = ["aiohttp (>=3.7.4)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
-python2 = ["typed-ast (>=1.4.2)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
@@ -238,14 +238,14 @@ dev = ["pip-tools (==6.5.1)", "pytest (==7.1.1)", "flake8 (==4.0.1)", "tox (==3.
 
 [[package]]
 name = "boto3"
-version = "1.24.2"
+version = "1.24.8"
 description = "The AWS SDK for Python"
 category = "main"
 optional = false
 python-versions = ">= 3.7"
 
 [package.dependencies]
-botocore = ">=1.27.2,<1.28.0"
+botocore = ">=1.27.8,<1.28.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.6.0,<0.7.0"
 
@@ -254,7 +254,7 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.27.2"
+version = "1.27.8"
 description = "Low-level, data-driven core of boto 3."
 category = "main"
 optional = false
@@ -728,7 +728,7 @@ typing-extensions = {version = ">=3.7.4.3", markers = "python_version < \"3.8\""
 
 [[package]]
 name = "google-auth"
-version = "2.6.6"
+version = "2.7.0"
 description = "Google Authentication Library"
 category = "main"
 optional = true
@@ -742,6 +742,7 @@ six = ">=1.9.0"
 
 [package.extras]
 aiohttp = ["requests (>=2.20.0,<3.0.0dev)", "aiohttp (>=3.6.2,<4.0.0dev)"]
+enterprise_cert = ["cryptography (==36.0.2)", "pyopenssl (==22.0.0)"]
 pyopenssl = ["pyopenssl (>=20.0.0)"]
 reauth = ["pyu2f (>=0.1.5)"]
 
@@ -886,7 +887,7 @@ python-versions = "*"
 
 [[package]]
 name = "ipykernel"
-version = "6.13.0"
+version = "6.14.0"
 description = "IPython Kernel for Jupyter"
 category = "main"
 optional = true
@@ -905,7 +906,7 @@ tornado = ">=6.1"
 traitlets = ">=5.1.0"
 
 [package.extras]
-test = ["pytest (>=6.0)", "pytest-cov", "flaky", "ipyparallel", "pre-commit", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest-cov", "pytest-timeout", "pytest (>=6.0)"]
 
 [[package]]
 name = "ipython"
@@ -1063,7 +1064,7 @@ qtconsole = "*"
 
 [[package]]
 name = "jupyter-client"
-version = "7.3.1"
+version = "7.3.4"
 description = "Jupyter protocol implementation and client libraries"
 category = "main"
 optional = true
@@ -1074,12 +1075,12 @@ entrypoints = "*"
 jupyter-core = ">=4.9.2"
 nest-asyncio = ">=1.5.4"
 python-dateutil = ">=2.8.2"
-pyzmq = ">=22.3"
+pyzmq = ">=23.0"
 tornado = ">=6.0"
 traitlets = "*"
 
 [package.extras]
-doc = ["ipykernel", "myst-parser", "sphinx (>=1.3.6)", "sphinx-rtd-theme", "sphinxcontrib-github-alt"]
+doc = ["ipykernel", "myst-parser", "sphinx-rtd-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt"]
 test = ["codecov", "coverage", "ipykernel (>=6.5)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
@@ -1133,7 +1134,7 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "kiwisolver"
-version = "1.4.2"
+version = "1.4.3"
 description = "A fast implementation of the Cassowary constraint solver"
 category = "main"
 optional = false
@@ -1433,7 +1434,7 @@ test = ["check-manifest", "testpath", "pytest", "pre-commit"]
 
 [[package]]
 name = "nbsphinx"
-version = "0.8.8"
+version = "0.8.9"
 description = "Jupyter Notebook Tools for Sphinx"
 category = "main"
 optional = true
@@ -1445,7 +1446,7 @@ jinja2 = "*"
 nbconvert = "!=5.4"
 nbformat = "*"
 sphinx = ">=1.8"
-traitlets = "*"
+traitlets = ">=5"
 
 [[package]]
 name = "nest-asyncio"
@@ -1457,7 +1458,7 @@ python-versions = ">=3.5"
 
 [[package]]
 name = "notebook"
-version = "6.4.11"
+version = "6.4.12"
 description = "A web-based notebook environment for interactive computing"
 category = "main"
 optional = true
@@ -1507,7 +1508,7 @@ python-versions = ">=3.7,<3.11"
 
 [[package]]
 name = "numpydoc"
-version = "1.3.1"
+version = "1.4.0"
 description = "Sphinx extension to support docstrings in Numpy format"
 category = "main"
 optional = true
@@ -1547,7 +1548,7 @@ PyYAML = ">=5.1.0"
 
 [[package]]
 name = "optuna"
-version = "2.10.0"
+version = "2.10.1"
 description = "A hyperparameter optimization framework"
 category = "main"
 optional = true
@@ -1570,7 +1571,7 @@ benchmark = ["asv", "virtualenv"]
 checking = ["black", "hacking", "isort", "mypy (==0.790)", "blackdoc"]
 codecov = ["codecov", "pytest-cov"]
 doctest = ["cma", "matplotlib (>=3.0.0)", "pandas", "plotly (>=4.0.0)", "scikit-learn (>=0.24.2,<1.0.0)", "scikit-optimize", "mlflow"]
-document = ["sphinx (<4.0.0)", "sphinx-rtd-theme", "sphinx-copybutton", "sphinx-gallery", "sphinx-plotly-directive", "pillow", "matplotlib", "scikit-learn (<1.0.0)", "plotly (>=4.0.0)", "pandas", "lightgbm", "torch (==1.8.0)", "torchvision (==0.9.0)", "torchaudio (==0.8.0)", "thop"]
+document = ["Jinja2 (<3.0.0)", "MarkupSafe (<=2.0.1)", "sphinx (<=3.5.4)", "sphinx-rtd-theme (<=1.0.0)", "sphinx-copybutton (<=0.4.0)", "sphinx-gallery (<=0.10.0)", "sphinx-plotly-directive (<=0.1.3)", "pillow", "matplotlib", "scikit-learn (<1.0.0)", "plotly (>=4.0.0)", "pandas", "lightgbm", "torch (==1.8.0)", "torchvision (==0.9.0)", "torchaudio (==0.8.0)", "thop"]
 experimental = ["redis"]
 integration = ["chainer (>=5.0.0)", "cma", "lightgbm", "mlflow", "wandb", "mpi4py", "mxnet", "pandas", "scikit-learn (>=0.24.2,<1.0.0)", "scikit-optimize", "xgboost", "tensorflow", "tensorflow-datasets", "pytorch-ignite", "pytorch-lightning (>=1.0.2)", "skorch", "catalyst (>=21.3)", "torchaudio (==0.8.0)", "allennlp (>=2.2.0,<2.7.0)", "fastai", "botorch (>=0.4.0)", "torch (==1.8.0+cpu)", "torchvision (==0.9.0+cpu)", "torch (==1.8.0)", "torchvision (==0.9.0)"]
 optional = ["bokeh (<2.0.0)", "matplotlib (>=3.0.0)", "pandas", "plotly (>=4.0.0)", "redis", "scikit-learn (>=0.24.2,<1.0.0)"]
@@ -1724,7 +1725,7 @@ test = ["appdirs (==1.4.4)", "pytest-cov (>=2.7)", "pytest-mock (>=3.6)", "pytes
 
 [[package]]
 name = "plotly"
-version = "5.8.0"
+version = "5.8.2"
 description = "An open-source, interactive data visualization library for Python"
 category = "main"
 optional = false
@@ -1841,11 +1842,11 @@ tqdm = ">=4.36.1"
 
 [[package]]
 name = "protobuf"
-version = "3.20.1"
+version = "3.19.4"
 description = "Protocol Buffers"
 category = "main"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.5"
 
 [[package]]
 name = "psutil"
@@ -2152,7 +2153,7 @@ py = {version = "*", markers = "implementation_name == \"pypy\""}
 
 [[package]]
 name = "qtconsole"
-version = "5.3.0"
+version = "5.3.1"
 description = "Jupyter Qt console"
 category = "main"
 optional = true
@@ -2166,7 +2167,7 @@ jupyter-core = "*"
 pygments = "*"
 pyzmq = ">=17.1"
 qtpy = ">=2.0.1"
-traitlets = "*"
+traitlets = "<5.2.1 || >5.2.1,<5.2.2 || >5.2.2"
 
 [package.extras]
 doc = ["Sphinx (>=1.3)"]
@@ -2186,30 +2187,22 @@ packaging = "*"
 [package.extras]
 test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
 
-[[package]]
-name = "regex"
-version = "2022.6.2"
-description = "Alternative regular expression module, to replace re."
-category = "main"
-optional = true
-python-versions = ">=3.6"
-
 [[package]]
 name = "requests"
-version = "2.27.1"
+version = "2.28.0"
 description = "Python HTTP for Humans."
 category = "main"
 optional = true
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+python-versions = ">=3.7, <4"
 
 [package.dependencies]
 certifi = ">=2017.4.17"
-charset-normalizer = {version = ">=2.0.0,<2.1.0", markers = "python_version >= \"3\""}
-idna = {version = ">=2.5,<4", markers = "python_version >= \"3\""}
+charset-normalizer = ">=2.0.0,<2.1.0"
+idna = ">=2.5,<4"
 urllib3 = ">=1.21.1,<1.27"
 
 [package.extras]
-socks = ["PySocks (>=1.5.6,!=1.5.7)", "win-inet-pton"]
+socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use_chardet_on_py3 = ["chardet (>=3.0.2,<5)"]
 
 [[package]]
@@ -2665,7 +2658,7 @@ doc = ["reno", "sphinx", "tornado (>=4.5)"]
 
 [[package]]
 name = "tensorboard"
-version = "2.9.0"
+version = "2.9.1"
 description = "TensorBoard lets you watch Tensors Flow"
 category = "main"
 optional = true
@@ -2678,7 +2671,7 @@ google-auth-oauthlib = ">=0.4.1,<0.5"
 grpcio = ">=1.24.3"
 markdown = ">=2.6.8"
 numpy = ">=1.12.0"
-protobuf = ">=3.9.2"
+protobuf = ">=3.9.2,<3.20"
 requests = ">=2.21.0,<3"
 tensorboard-data-server = ">=0.6.0,<0.7.0"
 tensorboard-plugin-wit = ">=1.6.0"
@@ -2739,6 +2732,14 @@ webencodings = ">=0.4"
 doc = ["sphinx", "sphinx-rtd-theme"]
 test = ["pytest", "pytest-cov", "pytest-flake8", "pytest-isort", "coverage"]
 
+[[package]]
+name = "tokenize-rt"
+version = "4.2.1"
+description = "A wrapper around the stdlib `tokenize` which roundtrips."
+category = "main"
+optional = true
+python-versions = ">=3.6.1"
+
 [[package]]
 name = "toml"
 version = "0.10.2"
@@ -2749,11 +2750,11 @@ python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
 
 [[package]]
 name = "tomli"
-version = "1.2.3"
+version = "2.0.1"
 description = "A lil' TOML parser"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 
 [[package]]
 name = "torch"
@@ -2768,7 +2769,7 @@ typing-extensions = "*"
 
 [[package]]
 name = "torchmetrics"
-version = "0.9.0"
+version = "0.9.1"
 description = "PyTorch native Metrics"
 category = "main"
 optional = true
@@ -2781,14 +2782,14 @@ torch = ">=1.3.1"
 typing-extensions = {version = "*", markers = "python_version < \"3.8\""}
 
 [package.extras]
-all = ["pystoi", "pycocotools", "torchvision (>=0.8)", "sphinx-copybutton (>=0.3)", "myst-parser", "sphinxcontrib-fulltoc (>=1.0)", "sphinx (>=4.0)", "sphinxcontrib-mockautodoc", "sphinx-togglebutton (>=0.2)", "docutils (>=0.16)", "pandoc (>=1.0)", "nbsphinx (>=0.8)", "sphinx-paramlinks (>=0.5.1)", "sphinx-autodoc-typehints (>=1.0)", "torch-fidelity", "torchvision", "lpips", "scipy", "pytorch-lightning (>=1.5)", "pytest (>=6.0.0,<7.0.0)", "phmdoctest (>=1.1.1)", "psutil", "pytest-cov (>2.10)", "pypesq", "check-manifest", "sacrebleu (>=2.0.0)", "pytest-timeout", "mypy (>=0.790)", "scikit-image (>0.17.1)", "transformers (>=4.0)", "pytorch-msssim", "cloudpickle (>=1.3)", "twine (>=3.2)", "codecov (>=2.1)", "pre-commit (>=1.0)", "huggingface-hub (<0.7)", "coverage (>5.2)", "requests", "torch-complex", "jiwer (>=2.3.0)", "bert-score (==0.3.10)", "mir-eval (>=0.6)", "fast-bss-eval (>=0.1.0)", "fire", "rouge-score (>=0.0.4)", "scikit-learn (>1.0,<1.1.1)", "pytest-doctestplus (>=0.9.0)", "tqdm (>=4.41.0)", "nltk (>=3.6)", "regex (>=2021.9.24)"]
+all = ["pystoi", "torchvision (>=0.8)", "pycocotools", "myst-parser", "nbsphinx (>=0.8)", "sphinx-paramlinks (>=0.5.1)", "sphinxcontrib-mockautodoc", "sphinx-togglebutton (>=0.2)", "sphinxcontrib-fulltoc (>=1.0)", "docutils (>=0.16)", "sphinx (>=4.0,<5.0)", "sphinx-autodoc-typehints (>=1.0)", "sphinx-copybutton (>=0.3)", "pandoc (>=1.0)", "lpips", "torchvision", "torch-fidelity", "scipy", "pytorch-lightning (>=1.5)", "coverage (>5.2)", "scikit-learn (>1.0,<1.1.1)", "phmdoctest (>=1.1.1)", "codecov (>=2.1)", "sacrebleu (>=2.0.0)", "rouge-score (>=0.0.4)", "cloudpickle (>=1.3)", "pytest-timeout", "check-manifest", "jiwer (>=2.3.0)", "twine (>=3.2)", "pypesq", "pytest (>=6.0.0,<7.0.0)", "pre-commit (>=1.0)", "mir-eval (>=0.6)", "huggingface-hub (<0.7)", "scikit-image (>0.17.1)", "bert-score (==0.3.10)", "fire", "pytest-doctestplus (>=0.9.0)", "fast-bss-eval (>=0.1.0)", "requests", "pytorch-msssim", "mypy (>=0.790)", "transformers (>=4.0)", "psutil", "torch-complex", "pytest-cov (>2.10)", "nltk (>=3.6)", "regex (>=2021.9.24)", "tqdm (>=4.41.0)"]
 audio = ["pystoi"]
-detection = ["pycocotools", "torchvision (>=0.8)"]
-docs = ["sphinx-copybutton (>=0.3)", "myst-parser", "sphinxcontrib-fulltoc (>=1.0)", "sphinx (>=4.0)", "sphinxcontrib-mockautodoc", "sphinx-togglebutton (>=0.2)", "docutils (>=0.16)", "pandoc (>=1.0)", "nbsphinx (>=0.8)", "sphinx-paramlinks (>=0.5.1)", "sphinx-autodoc-typehints (>=1.0)"]
-image = ["torch-fidelity", "torchvision", "lpips", "scipy"]
+detection = ["torchvision (>=0.8)", "pycocotools"]
+docs = ["myst-parser", "nbsphinx (>=0.8)", "sphinx-paramlinks (>=0.5.1)", "sphinxcontrib-mockautodoc", "sphinx-togglebutton (>=0.2)", "sphinxcontrib-fulltoc (>=1.0)", "docutils (>=0.16)", "sphinx (>=4.0,<5.0)", "sphinx-autodoc-typehints (>=1.0)", "sphinx-copybutton (>=0.3)", "pandoc (>=1.0)"]
+image = ["lpips", "torchvision", "torch-fidelity", "scipy"]
 integrate = ["pytorch-lightning (>=1.5)"]
-test = ["pytest (>=6.0.0,<7.0.0)", "pycocotools", "phmdoctest (>=1.1.1)", "psutil", "pytest-cov (>2.10)", "pypesq", "check-manifest", "sacrebleu (>=2.0.0)", "pytest-timeout", "mypy (>=0.790)", "scikit-image (>0.17.1)", "transformers (>=4.0)", "pytorch-msssim", "cloudpickle (>=1.3)", "twine (>=3.2)", "codecov (>=2.1)", "pre-commit (>=1.0)", "huggingface-hub (<0.7)", "coverage (>5.2)", "requests", "torch-complex", "jiwer (>=2.3.0)", "bert-score (==0.3.10)", "mir-eval (>=0.6)", "fast-bss-eval (>=0.1.0)", "fire", "rouge-score (>=0.0.4)", "scikit-learn (>1.0,<1.1.1)", "pytest-doctestplus (>=0.9.0)"]
-text = ["tqdm (>=4.41.0)", "nltk (>=3.6)", "regex (>=2021.9.24)"]
+test = ["coverage (>5.2)", "scikit-learn (>1.0,<1.1.1)", "phmdoctest (>=1.1.1)", "codecov (>=2.1)", "sacrebleu (>=2.0.0)", "rouge-score (>=0.0.4)", "cloudpickle (>=1.3)", "pytest-timeout", "check-manifest", "jiwer (>=2.3.0)", "twine (>=3.2)", "pypesq", "pytest (>=6.0.0,<7.0.0)", "pre-commit (>=1.0)", "mir-eval (>=0.6)", "pycocotools", "huggingface-hub (<0.7)", "scikit-image (>0.17.1)", "bert-score (==0.3.10)", "fire", "pytest-doctestplus (>=0.9.0)", "fast-bss-eval (>=0.1.0)", "requests", "pytorch-msssim", "mypy (>=0.790)", "transformers (>=4.0)", "psutil", "torch-complex", "pytest-cov (>2.10)"]
+text = ["nltk (>=3.6)", "regex (>=2021.9.24)", "tqdm (>=4.41.0)"]
 
 [[package]]
 name = "tornado"
@@ -2890,7 +2891,7 @@ socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
 [[package]]
 name = "wandb"
-version = "0.12.17"
+version = "0.12.18"
 description = "A CLI and library for interacting with the Weights and Biases API."
 category = "main"
 optional = true
@@ -2904,7 +2905,6 @@ pathtools = "*"
 promise = ">=2.0,<3"
 protobuf = ">=3.12.0,<4.0dev"
 psutil = ">=5.0.0"
-python-dateutil = ">=2.6.1"
 PyYAML = "*"
 requests = ">=2.0.0,<3"
 sentry-sdk = ">=1.0.0"
@@ -2920,7 +2920,7 @@ grpc = ["grpcio (>=1.27.2)"]
 kubeflow = ["kubernetes", "minio", "google-cloud-storage", "sh"]
 launch = ["nbconvert", "nbformat", "chardet", "iso8601", "typing-extensions", "boto3", "google-cloud-storage", "kubernetes"]
 media = ["numpy", "moviepy", "pillow", "bokeh", "soundfile", "plotly", "rdkit-pypi"]
-sweeps = ["numpy (>=1.15,<1.21)", "scipy (>=1.5.4)", "pyyaml", "scikit-learn (==0.24.1)", "jsonschema (>=3.2.0)", "jsonref (>=0.2)", "pydantic (>=1.8.2)"]
+sweeps = ["sweeps (>=0.1.0)"]
 
 [[package]]
 name = "wcwidth"
@@ -3000,7 +3000,7 @@ testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-
 all = ["prophet", "torch", "pytorch-forecasting", "wandb"]
 all-dev = ["prophet", "torch", "pytorch-forecasting", "wandb", "click", "semver", "Sphinx", "numpydoc", "sphinx-rtd-theme", "nbsphinx", "sphinx-mathjax-offline", "myst-parser", "GitPython", "pytest-cov", "coverage", "pytest", "black", "isort", "flake8", "pep8-naming", "flake8-docstrings", "mypy", "types-PyYAML", "codespell", "flake8-bugbear", "flake8-comprehensions", "click", "semver", "jupyter", "nbconvert"]
 docs = ["Sphinx", "numpydoc", "sphinx-rtd-theme", "nbsphinx", "sphinx-mathjax-offline", "myst-parser", "GitPython"]
-jupyter = ["jupyter", "nbconvert"]
+jupyter = ["jupyter", "nbconvert", "black"]
 prophet = ["prophet"]
 release = ["click", "semver"]
 style = ["black", "isort", "flake8", "pep8-naming", "flake8-docstrings", "mypy", "types-PyYAML", "codespell", "flake8-bugbear", "flake8-comprehensions"]
@@ -3011,7 +3011,7 @@ wandb = ["wandb"]
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+content-hash = "dffaa4c08dc9d76330fc7e53beacc62120fdc178d06111c9e0df603193546b7a"
 
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@@ -5133,7 +5075,7 @@ tenacity = [
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@@ -5155,13 +5097,17 @@ tinycss2 = [
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@@ -5185,8 +5131,8 @@ torch = [
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@@ -5340,8 +5286,8 @@ urllib3 = [
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+    {file = "wandb-0.12.18.tar.gz", hash = "sha256:0db719cf2984f5805cfdde445962fae024ef43e77603eecc031c67fe475ec05f"},
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diff --git a/pyproject.toml b/pyproject.toml
index aec4f928f..9e0f4aebb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -86,7 +86,7 @@ pytest = {version = "^6.2", optional = true}
 coverage = {version = "^5.4", optional = true}
 pytest-cov = {version = "^2.11.1", optional = true}
 
-black = {version = "21.9b0", optional = true}
+black = {extras = ["jupyter"], version = "^22.3.0", optional = true}
 isort = {version = "^5.8.0", optional = true}
 flake8 = {version = "^3.9.2", optional = true}
 pep8-naming = {version = "^0.12.1", optional = true}
@@ -114,7 +114,7 @@ wandb = ["wandb"]
 release = ["click", "semver"]
 docs = ["Sphinx", "numpydoc", "sphinx-rtd-theme", "nbsphinx", "sphinx-mathjax-offline", "myst-parser", "GitPython"]
 tests = ["pytest-cov", "coverage", "pytest"]
-jupyter = ["jupyter", "nbconvert"]
+jupyter = ["jupyter", "nbconvert", "black"]
 style = ["black", "isort", "flake8", "pep8-naming", "flake8-docstrings", "mypy", "types-PyYAML", "codespell", "flake8-bugbear", "flake8-comprehensions"]
 
 all = [
diff --git a/tests/test_datasets/test_dataset.py b/tests/test_datasets/test_dataset.py
index 036beba9a..ed2fcf84d 100644
--- a/tests/test_datasets/test_dataset.py
+++ b/tests/test_datasets/test_dataset.py
@@ -23,9 +23,9 @@ def tsdf_with_exog(random_seed) -> TSDataset:
     df_1 = pd.DataFrame.from_dict({"timestamp": pd.date_range("2021-02-01", "2021-07-01", freq="1d")})
     df_2 = pd.DataFrame.from_dict({"timestamp": pd.date_range("2021-02-01", "2021-07-01", freq="1d")})
     df_1["segment"] = "Moscow"
-    df_1["target"] = [x ** 2 + np.random.uniform(-2, 2) for x in list(range(len(df_1)))]
+    df_1["target"] = [x**2 + np.random.uniform(-2, 2) for x in list(range(len(df_1)))]
     df_2["segment"] = "Omsk"
-    df_2["target"] = [x ** 0.5 + np.random.uniform(-2, 2) for x in list(range(len(df_2)))]
+    df_2["target"] = [x**0.5 + np.random.uniform(-2, 2) for x in list(range(len(df_2)))]
     classic_df = pd.concat([df_1, df_2], ignore_index=True)
 
     df = TSDataset.to_dataset(classic_df)
diff --git a/tests/test_transforms/test_encoders/test_categorical_transform.py b/tests/test_transforms/test_encoders/test_categorical_transform.py
index 1b6990515..c09ccca41 100644
--- a/tests/test_transforms/test_encoders/test_categorical_transform.py
+++ b/tests/test_transforms/test_encoders/test_categorical_transform.py
@@ -225,7 +225,7 @@ def ts_for_ohe_sanity():
     rng = np.random.default_rng(12345)
 
     def f(x):
-        return x ** 2 + rng.normal(0, 0.01)
+        return x**2 + rng.normal(0, 0.01)
 
     df_to_forecast["segment_0", "target"] = df_regressors["segment_0"]["regressor_0"][:100].apply(f)
     ts = TSDataset(df=df_to_forecast, freq="D", df_exog=df_regressors, known_future="all")