We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Refactor issue #1043
Fixes #1043 

Removed methods load_UCR_UEA_dataset & _load_dataset from datasets/base.py and moved them to utils/data_io.py

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `sktime/transformations/panel/signature_based/_signature_method.py`
Content:
```
1 # -*- coding: utf-8 -*-
2 from sklearn.pipeline import Pipeline
3 from sktime.transformations.base import _PanelToTabularTransformer
4 from sktime.transformations.panel.signature_based._compute import (
5     _WindowSignatureTransform,
6 )
7 from sktime.transformations.panel.signature_based._augmentations import (
8     _make_augmentation_pipeline,
9 )
10 from sktime.transformations.panel.signature_based._checks import (
11     _handle_sktime_signatures,
12 )
13 
14 
15 class SignatureTransformer(_PanelToTabularTransformer):
16     """Transformation class from the signature method.
17 
18     Follows the methodology laid out in the paper:
19         "A Generalised Signature Method for Multivariate Time Series"
20 
21     Parameters
22     ----------
23     augmentation_list: tuple of strings, contains the augmentations to be
24         applied before application of the signature transform.
25     window_name: str, The name of the window transform to apply.
26     window_depth: int, The depth of the dyadic window. (Active only if
27         `window_name == 'dyadic'`).
28     window_length: int, The length of the sliding/expanding window. (Active
29         only if `window_name in ['sliding, 'expanding']`.
30     window_step: int, The step of the sliding/expanding window. (Active
31         only if `window_name in ['sliding, 'expanding']`.
32     rescaling: str or None, The method of signature rescaling.
33     sig_tfm: str, String to specify the type of signature transform. One of:
34         ['signature', 'logsignature']).
35     depth: int, Signature truncation depth.
36 
37     Attributes
38     ----------
39     signature_method: sklearn.Pipeline, A sklearn pipeline object that contains
40         all the steps to extract the signature features.
41     """
42 
43     def __init__(
44         self,
45         augmentation_list=("basepoint", "addtime"),
46         window_name="dyadic",
47         window_depth=3,
48         window_length=None,
49         window_step=None,
50         rescaling=None,
51         sig_tfm="signature",
52         depth=4,
53     ):
54         super(SignatureTransformer, self).__init__()
55         self.augmentation_list = augmentation_list
56         self.window_name = window_name
57         self.window_depth = window_depth
58         self.window_length = window_length
59         self.window_step = window_step
60         self.rescaling = rescaling
61         self.sig_tfm = sig_tfm
62         self.depth = depth
63 
64         self.setup_feature_pipeline()
65 
66     def _assertions(self):
67         """Some assertions to run on initialisation."""
68         assert not all(
69             [self.sig_tfm == "logsignature", self.rescaling == "post"]
70         ), "Cannot have post rescaling with the logsignature."
71 
72     def setup_feature_pipeline(self):
73         """Sets up the signature method as an sklearn pipeline."""
74         augmentation_step = _make_augmentation_pipeline(self.augmentation_list)
75         transform_step = _WindowSignatureTransform(
76             window_name=self.window_name,
77             window_depth=self.window_depth,
78             window_length=self.window_length,
79             window_step=self.window_step,
80             sig_tfm=self.sig_tfm,
81             sig_depth=self.depth,
82             rescaling=self.rescaling,
83         )
84 
85         # The so-called 'signature method' as defined in the reference paper
86         self.signature_method = Pipeline(
87             [
88                 ("augmentations", augmentation_step),
89                 ("window_and_transform", transform_step),
90             ]
91         )
92 
93     @_handle_sktime_signatures(check_fitted=False)
94     def fit(self, data, labels=None):
95         self.signature_method.fit(data, labels)
96         self._is_fitted = True
97         return self
98 
99     @_handle_sktime_signatures(check_fitted=True)
100     def transform(self, data, labels=None):
101         return self.signature_method.transform(data)
102
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/sktime/transformations/panel/signature_based/_signature_method.py b/sktime/transformations/panel/signature_based/_signature_method.py
--- a/sktime/transformations/panel/signature_based/_signature_method.py
+++ b/sktime/transformations/panel/signature_based/_signature_method.py
@@ -1,15 +1,16 @@
 # -*- coding: utf-8 -*-
 from sklearn.pipeline import Pipeline
+
 from sktime.transformations.base import _PanelToTabularTransformer
-from sktime.transformations.panel.signature_based._compute import (
-    _WindowSignatureTransform,
-)
 from sktime.transformations.panel.signature_based._augmentations import (
     _make_augmentation_pipeline,
 )
 from sktime.transformations.panel.signature_based._checks import (
     _handle_sktime_signatures,
 )
+from sktime.transformations.panel.signature_based._compute import (
+    _WindowSignatureTransform,
+)
 
 
 class SignatureTransformer(_PanelToTabularTransformer):
@@ -63,14 +64,8 @@
 
         self.setup_feature_pipeline()
 
-    def _assertions(self):
-        """Some assertions to run on initialisation."""
-        assert not all(
-            [self.sig_tfm == "logsignature", self.rescaling == "post"]
-        ), "Cannot have post rescaling with the logsignature."
-
     def setup_feature_pipeline(self):
-        """Sets up the signature method as an sklearn pipeline."""
+        """Set up the signature method as an sklearn pipeline."""
         augmentation_step = _make_augmentation_pipeline(self.augmentation_list)
         transform_step = _WindowSignatureTransform(
             window_name=self.window_name,
@@ -92,10 +87,38 @@
 
     @_handle_sktime_signatures(check_fitted=False)
     def fit(self, data, labels=None):
+        """Fit to data, then transform it.
+
+        Parameters
+        ----------
+        data: pd.Dataframe or np.ndarray (3d array)
+            Data to transform.
+        labels: np.ndarray (1d array) or pd.series or list
+            Labels for the data.
+
+        Returns
+        -------
+        pd.Dataframe or np.ndarray or pd.series
+        Transformed data.
+        """
         self.signature_method.fit(data, labels)
         self._is_fitted = True
         return self
 
     @_handle_sktime_signatures(check_fitted=True)
     def transform(self, data, labels=None):
+        """Transform the class from the signature method.
+
+        Parameters
+        ----------
+        data: pd.Dataframe or np.ndarray (3d array)
+            Data to transform.
+        labels: np.ndarray (1d array) or pd.series or list
+            Labels for the data.
+
+        Returns
+        -------
+        pd.Dataframe or np.ndarray or pd.series
+            Transformed data.
+        """
         return self.signature_method.transform(data)

{"golden_diff": "diff --git a/sktime/transformations/panel/signature_based/_signature_method.py b/sktime/transformations/panel/signature_based/_signature_method.py\n--- a/sktime/transformations/panel/signature_based/_signature_method.py\n+++ b/sktime/transformations/panel/signature_based/_signature_method.py\n@@ -1,15 +1,16 @@\n # -*- coding: utf-8 -*-\n from sklearn.pipeline import Pipeline\n+\n from sktime.transformations.base import _PanelToTabularTransformer\n-from sktime.transformations.panel.signature_based._compute import (\n-    _WindowSignatureTransform,\n-)\n from sktime.transformations.panel.signature_based._augmentations import (\n     _make_augmentation_pipeline,\n )\n from sktime.transformations.panel.signature_based._checks import (\n     _handle_sktime_signatures,\n )\n+from sktime.transformations.panel.signature_based._compute import (\n+    _WindowSignatureTransform,\n+)\n \n \n class SignatureTransformer(_PanelToTabularTransformer):\n@@ -63,14 +64,8 @@\n \n         self.setup_feature_pipeline()\n \n-    def _assertions(self):\n-        \"\"\"Some assertions to run on initialisation.\"\"\"\n-        assert not all(\n-            [self.sig_tfm == \"logsignature\", self.rescaling == \"post\"]\n-        ), \"Cannot have post rescaling with the logsignature.\"\n-\n     def setup_feature_pipeline(self):\n-        \"\"\"Sets up the signature method as an sklearn pipeline.\"\"\"\n+        \"\"\"Set up the signature method as an sklearn pipeline.\"\"\"\n         augmentation_step = _make_augmentation_pipeline(self.augmentation_list)\n         transform_step = _WindowSignatureTransform(\n             window_name=self.window_name,\n@@ -92,10 +87,38 @@\n \n     @_handle_sktime_signatures(check_fitted=False)\n     def fit(self, data, labels=None):\n+        \"\"\"Fit to data, then transform it.\n+\n+        Parameters\n+        ----------\n+        data: pd.Dataframe or np.ndarray (3d array)\n+            Data to transform.\n+        labels: np.ndarray (1d array) or pd.series or list\n+            Labels for the data.\n+\n+        Returns\n+        -------\n+        pd.Dataframe or np.ndarray or pd.series\n+        Transformed data.\n+        \"\"\"\n         self.signature_method.fit(data, labels)\n         self._is_fitted = True\n         return self\n \n     @_handle_sktime_signatures(check_fitted=True)\n     def transform(self, data, labels=None):\n+        \"\"\"Transform the class from the signature method.\n+\n+        Parameters\n+        ----------\n+        data: pd.Dataframe or np.ndarray (3d array)\n+            Data to transform.\n+        labels: np.ndarray (1d array) or pd.series or list\n+            Labels for the data.\n+\n+        Returns\n+        -------\n+        pd.Dataframe or np.ndarray or pd.series\n+            Transformed data.\n+        \"\"\"\n         return self.signature_method.transform(data)\n", "issue": "Refactor issue #1043\nFixes #1043 \r\n\r\nRemoved methods load_UCR_UEA_dataset & _load_dataset from datasets/base.py and moved them to utils/data_io.py\n", "before_files": [{"content": "# -*- coding: utf-8 -*-\nfrom sklearn.pipeline import Pipeline\nfrom sktime.transformations.base import _PanelToTabularTransformer\nfrom sktime.transformations.panel.signature_based._compute import (\n    _WindowSignatureTransform,\n)\nfrom sktime.transformations.panel.signature_based._augmentations import (\n    _make_augmentation_pipeline,\n)\nfrom sktime.transformations.panel.signature_based._checks import (\n    _handle_sktime_signatures,\n)\n\n\nclass SignatureTransformer(_PanelToTabularTransformer):\n    \"\"\"Transformation class from the signature method.\n\n    Follows the methodology laid out in the paper:\n        \"A Generalised Signature Method for Multivariate Time Series\"\n\n    Parameters\n    ----------\n    augmentation_list: tuple of strings, contains the augmentations to be\n        applied before application of the signature transform.\n    window_name: str, The name of the window transform to apply.\n    window_depth: int, The depth of the dyadic window. (Active only if\n        `window_name == 'dyadic'`).\n    window_length: int, The length of the sliding/expanding window. (Active\n        only if `window_name in ['sliding, 'expanding']`.\n    window_step: int, The step of the sliding/expanding window. (Active\n        only if `window_name in ['sliding, 'expanding']`.\n    rescaling: str or None, The method of signature rescaling.\n    sig_tfm: str, String to specify the type of signature transform. One of:\n        ['signature', 'logsignature']).\n    depth: int, Signature truncation depth.\n\n    Attributes\n    ----------\n    signature_method: sklearn.Pipeline, A sklearn pipeline object that contains\n        all the steps to extract the signature features.\n    \"\"\"\n\n    def __init__(\n        self,\n        augmentation_list=(\"basepoint\", \"addtime\"),\n        window_name=\"dyadic\",\n        window_depth=3,\n        window_length=None,\n        window_step=None,\n        rescaling=None,\n        sig_tfm=\"signature\",\n        depth=4,\n    ):\n        super(SignatureTransformer, self).__init__()\n        self.augmentation_list = augmentation_list\n        self.window_name = window_name\n        self.window_depth = window_depth\n        self.window_length = window_length\n        self.window_step = window_step\n        self.rescaling = rescaling\n        self.sig_tfm = sig_tfm\n        self.depth = depth\n\n        self.setup_feature_pipeline()\n\n    def _assertions(self):\n        \"\"\"Some assertions to run on initialisation.\"\"\"\n        assert not all(\n            [self.sig_tfm == \"logsignature\", self.rescaling == \"post\"]\n        ), \"Cannot have post rescaling with the logsignature.\"\n\n    def setup_feature_pipeline(self):\n        \"\"\"Sets up the signature method as an sklearn pipeline.\"\"\"\n        augmentation_step = _make_augmentation_pipeline(self.augmentation_list)\n        transform_step = _WindowSignatureTransform(\n            window_name=self.window_name,\n            window_depth=self.window_depth,\n            window_length=self.window_length,\n            window_step=self.window_step,\n            sig_tfm=self.sig_tfm,\n            sig_depth=self.depth,\n            rescaling=self.rescaling,\n        )\n\n        # The so-called 'signature method' as defined in the reference paper\n        self.signature_method = Pipeline(\n            [\n                (\"augmentations\", augmentation_step),\n                (\"window_and_transform\", transform_step),\n            ]\n        )\n\n    @_handle_sktime_signatures(check_fitted=False)\n    def fit(self, data, labels=None):\n        self.signature_method.fit(data, labels)\n        self._is_fitted = True\n        return self\n\n    @_handle_sktime_signatures(check_fitted=True)\n    def transform(self, data, labels=None):\n        return self.signature_method.transform(data)\n", "path": "sktime/transformations/panel/signature_based/_signature_method.py"}], "after_files": [{"content": "# -*- coding: utf-8 -*-\nfrom sklearn.pipeline import Pipeline\n\nfrom sktime.transformations.base import _PanelToTabularTransformer\nfrom sktime.transformations.panel.signature_based._augmentations import (\n    _make_augmentation_pipeline,\n)\nfrom sktime.transformations.panel.signature_based._checks import (\n    _handle_sktime_signatures,\n)\nfrom sktime.transformations.panel.signature_based._compute import (\n    _WindowSignatureTransform,\n)\n\n\nclass SignatureTransformer(_PanelToTabularTransformer):\n    \"\"\"Transformation class from the signature method.\n\n    Follows the methodology laid out in the paper:\n        \"A Generalised Signature Method for Multivariate Time Series\"\n\n    Parameters\n    ----------\n    augmentation_list: tuple of strings, contains the augmentations to be\n        applied before application of the signature transform.\n    window_name: str, The name of the window transform to apply.\n    window_depth: int, The depth of the dyadic window. (Active only if\n        `window_name == 'dyadic'`).\n    window_length: int, The length of the sliding/expanding window. (Active\n        only if `window_name in ['sliding, 'expanding']`.\n    window_step: int, The step of the sliding/expanding window. (Active\n        only if `window_name in ['sliding, 'expanding']`.\n    rescaling: str or None, The method of signature rescaling.\n    sig_tfm: str, String to specify the type of signature transform. One of:\n        ['signature', 'logsignature']).\n    depth: int, Signature truncation depth.\n\n    Attributes\n    ----------\n    signature_method: sklearn.Pipeline, A sklearn pipeline object that contains\n        all the steps to extract the signature features.\n    \"\"\"\n\n    def __init__(\n        self,\n        augmentation_list=(\"basepoint\", \"addtime\"),\n        window_name=\"dyadic\",\n        window_depth=3,\n        window_length=None,\n        window_step=None,\n        rescaling=None,\n        sig_tfm=\"signature\",\n        depth=4,\n    ):\n        super(SignatureTransformer, self).__init__()\n        self.augmentation_list = augmentation_list\n        self.window_name = window_name\n        self.window_depth = window_depth\n        self.window_length = window_length\n        self.window_step = window_step\n        self.rescaling = rescaling\n        self.sig_tfm = sig_tfm\n        self.depth = depth\n\n        self.setup_feature_pipeline()\n\n    def setup_feature_pipeline(self):\n        \"\"\"Set up the signature method as an sklearn pipeline.\"\"\"\n        augmentation_step = _make_augmentation_pipeline(self.augmentation_list)\n        transform_step = _WindowSignatureTransform(\n            window_name=self.window_name,\n            window_depth=self.window_depth,\n            window_length=self.window_length,\n            window_step=self.window_step,\n            sig_tfm=self.sig_tfm,\n            sig_depth=self.depth,\n            rescaling=self.rescaling,\n        )\n\n        # The so-called 'signature method' as defined in the reference paper\n        self.signature_method = Pipeline(\n            [\n                (\"augmentations\", augmentation_step),\n                (\"window_and_transform\", transform_step),\n            ]\n        )\n\n    @_handle_sktime_signatures(check_fitted=False)\n    def fit(self, data, labels=None):\n        \"\"\"Fit to data, then transform it.\n\n        Parameters\n        ----------\n        data: pd.Dataframe or np.ndarray (3d array)\n            Data to transform.\n        labels: np.ndarray (1d array) or pd.series or list\n            Labels for the data.\n\n        Returns\n        -------\n        pd.Dataframe or np.ndarray or pd.series\n        Transformed data.\n        \"\"\"\n        self.signature_method.fit(data, labels)\n        self._is_fitted = True\n        return self\n\n    @_handle_sktime_signatures(check_fitted=True)\n    def transform(self, data, labels=None):\n        \"\"\"Transform the class from the signature method.\n\n        Parameters\n        ----------\n        data: pd.Dataframe or np.ndarray (3d array)\n            Data to transform.\n        labels: np.ndarray (1d array) or pd.series or list\n            Labels for the data.\n\n        Returns\n        -------\n        pd.Dataframe or np.ndarray or pd.series\n            Transformed data.\n        \"\"\"\n        return self.signature_method.transform(data)\n", "path": "sktime/transformations/panel/signature_based/_signature_method.py"}]}