We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
MAINT: Remove warning message
<!--
Thanks for contributing a pull request!

Please follow these standard acronyms to start the commit message:

- ENH: enhancement
- BUG: bug fix
- DOC: documentation
- TYP: type annotations
- TST: addition or modification of tests
- MAINT: maintenance commit (refactoring, typos, etc.)
- BLD: change related to building
- REL: related to releasing
- API: an (incompatible) API change
- DEP: deprecate something, or remove a deprecated object
- DEV: development tool or utility
- REV: revert an earlier commit
- PERF: performance improvement
- BOT: always commit via a bot
- CI: related to CI or CD
- CLN: Code cleanup
-->

- [ ] closes #xxxx
- [ ] whatsnew entry


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `dtoolkit/accessor/dataframe/values_to_dict.py`
Content:
```
1 from __future__ import annotations
2 
3 import pandas as pd
4 
5 from dtoolkit.accessor.register import register_dataframe_method
6 from dtoolkit.accessor.series import values_to_dict as s_values_to_dict  # noqa
7 from dtoolkit.util._decorator import deprecated_alias
8 
9 
10 @register_dataframe_method
11 @deprecated_alias(
12     warning_msg=(
13         "{func_name}'s parameter '{old_alias}' is deprecated and will be removed in "
14         "0.0.15. Please use the parameter '{new_alias}'. "
15         "(Warning added DToolKit 0.0.14)"
16     ),
17     few_as_key="ascending",
18 )
19 def values_to_dict(
20     df: pd.DataFrame,
21     order: list | tuple = None,
22     ascending: bool = True,
23     to_list: bool = True,
24 ) -> dict:
25     """
26     Convert :attr:`~pandas.DataFrame.values` to :class:`dict`.
27 
28     Parameters
29     ----------
30     order : list or tuple, optional
31         The order of keys via given columns. If ``order`` is set, ``ascending``
32         will not work.
33 
34     ascending : bool, default True
35         If True the key would use the few unique of column values first.
36 
37     to_list : bool, default True
38         If True one element value will return :keyword:`list`.
39 
40     Returns
41     -------
42     dict
43 
44     See Also
45     --------
46     dtoolkit.accessor.series.values_to_dict
47 
48     Notes
49     -----
50     The same key of values would be merged into :class:`list`.
51 
52     Examples
53     --------
54     >>> import json
55     >>> import dtoolkit.accessor
56     >>> import pandas as pd
57     >>> df = pd.DataFrame(
58     ...     {
59     ...         "x" : ["A", "A", "B", "B", "B"],
60     ...         "y" : ["a", "b", "c", "d", "d"],
61     ...         "z" : ["1", "2", "3", "3", "4"],
62     ...     }
63     ... )
64     >>> df
65        x  y  z
66     0  A  a  1
67     1  A  b  2
68     2  B  c  3
69     3  B  d  3
70     4  B  d  4
71 
72     Use few unique of column values as key first. The order of column unique values
73     number is `x` < `y` < `z`. So the result will be ``{x: {y: [z]} }``.
74 
75     >>> print(json.dumps(df.values_to_dict(), indent=4))
76     {
77         "A": {
78             "a": [
79                 "1"
80             ],
81             "b": [
82                 "2"
83             ]
84         },
85         "B": {
86             "c": [
87                 "3"
88             ],
89             "d": [
90                 "3",
91                 "4"
92             ]
93         }
94     }
95 
96     Use many unique of column values as key first, the result will be
97     ``{y: {z: [x]} }``.
98 
99     >>> print(json.dumps(df.values_to_dict(ascending=False), indent=4))
100     {
101         "a": {
102             "1": [
103                 "A"
104             ]
105         },
106         "b": {
107             "2": [
108                 "A"
109             ]
110         },
111         "c": {
112             "3": [
113                 "B"
114             ]
115         },
116         "d": {
117             "3": [
118                 "B"
119             ],
120             "4": [
121                 "B"
122             ]
123         }
124     }
125 
126     Output the arbitrary order like ``{z: x}  or ``{x: {z: [y]} }``,
127     via ``order`` argument.
128 
129     >>> print(json.dumps(df.values_to_dict(order=["x", "z"]), indent=4))
130     {
131         "A": [
132             "1",
133             "2"
134         ],
135         "B": [
136             "3",
137             "3",
138             "4"
139         ]
140     }
141     >>> print(json.dumps(df.values_to_dict(order=["x", "z", "y"]), indent=4))
142     {
143         "A": {
144             "1": [
145                 "a"
146             ],
147             "2": [
148                 "b"
149             ]
150         },
151         "B": {
152             "3": [
153                 "c",
154                 "d"
155             ],
156             "4": [
157                 "d"
158             ]
159         }
160     }
161 
162     It also could convert one column DataFrame. But ``ascending`` wouldn' work.
163     The result would be ``{index: [values]}``.
164 
165     >>> print(json.dumps(df[["x"]].values_to_dict(), indent=4))
166     {
167         "0": [
168             "A"
169         ],
170         "1": [
171             "A"
172         ],
173         "2": [
174             "B"
175         ],
176         "3": [
177             "B"
178         ],
179         "4": [
180             "B"
181         ]
182     }
183 
184     Unpack one element value list.
185 
186     >>> print(json.dumps(df.values_to_dict(to_list=False), indent=4))
187     {
188         "A": {
189             "a": "1",
190             "b": "2"
191         },
192         "B": {
193             "c": "3",
194             "d": [
195                 "3",
196                 "4"
197             ]
198         }
199     }
200     """
201 
202     if df.columns.__len__() == 1:  # one columns DataFrame
203         return df.to_series().values_to_dict(to_list=to_list)
204 
205     columns = order or (
206         df.nunique()
207         .sort_values(
208             ascending=ascending,
209         )
210         .index
211     )
212     return _dict(df[columns], to_list=to_list)
213 
214 
215 def _dict(df: pd.DataFrame, to_list: bool) -> dict:
216     key_column, *value_column = df.columns
217 
218     if df.columns.__len__() == 2:  # two column DataFrame
219         return df.to_series(
220             index_column=key_column,
221             value_column=value_column[0],
222         ).values_to_dict(to_list=to_list)
223 
224     return {
225         key: _dict(
226             df.loc[df[key_column] == key, value_column],
227             to_list=to_list,
228         )
229         for key in df[key_column].unique()
230     }
231
```
--- 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/dtoolkit/accessor/dataframe/values_to_dict.py b/dtoolkit/accessor/dataframe/values_to_dict.py
--- a/dtoolkit/accessor/dataframe/values_to_dict.py
+++ b/dtoolkit/accessor/dataframe/values_to_dict.py
@@ -4,18 +4,9 @@
 
 from dtoolkit.accessor.register import register_dataframe_method
 from dtoolkit.accessor.series import values_to_dict as s_values_to_dict  # noqa
-from dtoolkit.util._decorator import deprecated_alias
 
 
 @register_dataframe_method
-@deprecated_alias(
-    warning_msg=(
-        "{func_name}'s parameter '{old_alias}' is deprecated and will be removed in "
-        "0.0.15. Please use the parameter '{new_alias}'. "
-        "(Warning added DToolKit 0.0.14)"
-    ),
-    few_as_key="ascending",
-)
 def values_to_dict(
     df: pd.DataFrame,
     order: list | tuple = None,

{"golden_diff": "diff --git a/dtoolkit/accessor/dataframe/values_to_dict.py b/dtoolkit/accessor/dataframe/values_to_dict.py\n--- a/dtoolkit/accessor/dataframe/values_to_dict.py\n+++ b/dtoolkit/accessor/dataframe/values_to_dict.py\n@@ -4,18 +4,9 @@\n \n from dtoolkit.accessor.register import register_dataframe_method\n from dtoolkit.accessor.series import values_to_dict as s_values_to_dict  # noqa\n-from dtoolkit.util._decorator import deprecated_alias\n \n \n @register_dataframe_method\n-@deprecated_alias(\n-    warning_msg=(\n-        \"{func_name}'s parameter '{old_alias}' is deprecated and will be removed in \"\n-        \"0.0.15. Please use the parameter '{new_alias}'. \"\n-        \"(Warning added DToolKit 0.0.14)\"\n-    ),\n-    few_as_key=\"ascending\",\n-)\n def values_to_dict(\n     df: pd.DataFrame,\n     order: list | tuple = None,\n", "issue": "MAINT: Remove warning message\n<!--\r\nThanks for contributing a pull request!\r\n\r\nPlease follow these standard acronyms to start the commit message:\r\n\r\n- ENH: enhancement\r\n- BUG: bug fix\r\n- DOC: documentation\r\n- TYP: type annotations\r\n- TST: addition or modification of tests\r\n- MAINT: maintenance commit (refactoring, typos, etc.)\r\n- BLD: change related to building\r\n- REL: related to releasing\r\n- API: an (incompatible) API change\r\n- DEP: deprecate something, or remove a deprecated object\r\n- DEV: development tool or utility\r\n- REV: revert an earlier commit\r\n- PERF: performance improvement\r\n- BOT: always commit via a bot\r\n- CI: related to CI or CD\r\n- CLN: Code cleanup\r\n-->\r\n\r\n- [ ] closes #xxxx\r\n- [ ] whatsnew entry\r\n\n", "before_files": [{"content": "from __future__ import annotations\n\nimport pandas as pd\n\nfrom dtoolkit.accessor.register import register_dataframe_method\nfrom dtoolkit.accessor.series import values_to_dict as s_values_to_dict  # noqa\nfrom dtoolkit.util._decorator import deprecated_alias\n\n\n@register_dataframe_method\n@deprecated_alias(\n    warning_msg=(\n        \"{func_name}'s parameter '{old_alias}' is deprecated and will be removed in \"\n        \"0.0.15. Please use the parameter '{new_alias}'. \"\n        \"(Warning added DToolKit 0.0.14)\"\n    ),\n    few_as_key=\"ascending\",\n)\ndef values_to_dict(\n    df: pd.DataFrame,\n    order: list | tuple = None,\n    ascending: bool = True,\n    to_list: bool = True,\n) -> dict:\n    \"\"\"\n    Convert :attr:`~pandas.DataFrame.values` to :class:`dict`.\n\n    Parameters\n    ----------\n    order : list or tuple, optional\n        The order of keys via given columns. If ``order`` is set, ``ascending``\n        will not work.\n\n    ascending : bool, default True\n        If True the key would use the few unique of column values first.\n\n    to_list : bool, default True\n        If True one element value will return :keyword:`list`.\n\n    Returns\n    -------\n    dict\n\n    See Also\n    --------\n    dtoolkit.accessor.series.values_to_dict\n\n    Notes\n    -----\n    The same key of values would be merged into :class:`list`.\n\n    Examples\n    --------\n    >>> import json\n    >>> import dtoolkit.accessor\n    >>> import pandas as pd\n    >>> df = pd.DataFrame(\n    ...     {\n    ...         \"x\" : [\"A\", \"A\", \"B\", \"B\", \"B\"],\n    ...         \"y\" : [\"a\", \"b\", \"c\", \"d\", \"d\"],\n    ...         \"z\" : [\"1\", \"2\", \"3\", \"3\", \"4\"],\n    ...     }\n    ... )\n    >>> df\n       x  y  z\n    0  A  a  1\n    1  A  b  2\n    2  B  c  3\n    3  B  d  3\n    4  B  d  4\n\n    Use few unique of column values as key first. The order of column unique values\n    number is `x` < `y` < `z`. So the result will be ``{x: {y: [z]} }``.\n\n    >>> print(json.dumps(df.values_to_dict(), indent=4))\n    {\n        \"A\": {\n            \"a\": [\n                \"1\"\n            ],\n            \"b\": [\n                \"2\"\n            ]\n        },\n        \"B\": {\n            \"c\": [\n                \"3\"\n            ],\n            \"d\": [\n                \"3\",\n                \"4\"\n            ]\n        }\n    }\n\n    Use many unique of column values as key first, the result will be\n    ``{y: {z: [x]} }``.\n\n    >>> print(json.dumps(df.values_to_dict(ascending=False), indent=4))\n    {\n        \"a\": {\n            \"1\": [\n                \"A\"\n            ]\n        },\n        \"b\": {\n            \"2\": [\n                \"A\"\n            ]\n        },\n        \"c\": {\n            \"3\": [\n                \"B\"\n            ]\n        },\n        \"d\": {\n            \"3\": [\n                \"B\"\n            ],\n            \"4\": [\n                \"B\"\n            ]\n        }\n    }\n\n    Output the arbitrary order like ``{z: x}  or ``{x: {z: [y]} }``,\n    via ``order`` argument.\n\n    >>> print(json.dumps(df.values_to_dict(order=[\"x\", \"z\"]), indent=4))\n    {\n        \"A\": [\n            \"1\",\n            \"2\"\n        ],\n        \"B\": [\n            \"3\",\n            \"3\",\n            \"4\"\n        ]\n    }\n    >>> print(json.dumps(df.values_to_dict(order=[\"x\", \"z\", \"y\"]), indent=4))\n    {\n        \"A\": {\n            \"1\": [\n                \"a\"\n            ],\n            \"2\": [\n                \"b\"\n            ]\n        },\n        \"B\": {\n            \"3\": [\n                \"c\",\n                \"d\"\n            ],\n            \"4\": [\n                \"d\"\n            ]\n        }\n    }\n\n    It also could convert one column DataFrame. But ``ascending`` wouldn' work.\n    The result would be ``{index: [values]}``.\n\n    >>> print(json.dumps(df[[\"x\"]].values_to_dict(), indent=4))\n    {\n        \"0\": [\n            \"A\"\n        ],\n        \"1\": [\n            \"A\"\n        ],\n        \"2\": [\n            \"B\"\n        ],\n        \"3\": [\n            \"B\"\n        ],\n        \"4\": [\n            \"B\"\n        ]\n    }\n\n    Unpack one element value list.\n\n    >>> print(json.dumps(df.values_to_dict(to_list=False), indent=4))\n    {\n        \"A\": {\n            \"a\": \"1\",\n            \"b\": \"2\"\n        },\n        \"B\": {\n            \"c\": \"3\",\n            \"d\": [\n                \"3\",\n                \"4\"\n            ]\n        }\n    }\n    \"\"\"\n\n    if df.columns.__len__() == 1:  # one columns DataFrame\n        return df.to_series().values_to_dict(to_list=to_list)\n\n    columns = order or (\n        df.nunique()\n        .sort_values(\n            ascending=ascending,\n        )\n        .index\n    )\n    return _dict(df[columns], to_list=to_list)\n\n\ndef _dict(df: pd.DataFrame, to_list: bool) -> dict:\n    key_column, *value_column = df.columns\n\n    if df.columns.__len__() == 2:  # two column DataFrame\n        return df.to_series(\n            index_column=key_column,\n            value_column=value_column[0],\n        ).values_to_dict(to_list=to_list)\n\n    return {\n        key: _dict(\n            df.loc[df[key_column] == key, value_column],\n            to_list=to_list,\n        )\n        for key in df[key_column].unique()\n    }\n", "path": "dtoolkit/accessor/dataframe/values_to_dict.py"}], "after_files": [{"content": "from __future__ import annotations\n\nimport pandas as pd\n\nfrom dtoolkit.accessor.register import register_dataframe_method\nfrom dtoolkit.accessor.series import values_to_dict as s_values_to_dict  # noqa\n\n\n@register_dataframe_method\ndef values_to_dict(\n    df: pd.DataFrame,\n    order: list | tuple = None,\n    ascending: bool = True,\n    to_list: bool = True,\n) -> dict:\n    \"\"\"\n    Convert :attr:`~pandas.DataFrame.values` to :class:`dict`.\n\n    Parameters\n    ----------\n    order : list or tuple, optional\n        The order of keys via given columns. If ``order`` is set, ``ascending``\n        will not work.\n\n    ascending : bool, default True\n        If True the key would use the few unique of column values first.\n\n    to_list : bool, default True\n        If True one element value will return :keyword:`list`.\n\n    Returns\n    -------\n    dict\n\n    See Also\n    --------\n    dtoolkit.accessor.series.values_to_dict\n\n    Notes\n    -----\n    The same key of values would be merged into :class:`list`.\n\n    Examples\n    --------\n    >>> import json\n    >>> import dtoolkit.accessor\n    >>> import pandas as pd\n    >>> df = pd.DataFrame(\n    ...     {\n    ...         \"x\" : [\"A\", \"A\", \"B\", \"B\", \"B\"],\n    ...         \"y\" : [\"a\", \"b\", \"c\", \"d\", \"d\"],\n    ...         \"z\" : [\"1\", \"2\", \"3\", \"3\", \"4\"],\n    ...     }\n    ... )\n    >>> df\n       x  y  z\n    0  A  a  1\n    1  A  b  2\n    2  B  c  3\n    3  B  d  3\n    4  B  d  4\n\n    Use few unique of column values as key first. The order of column unique values\n    number is `x` < `y` < `z`. So the result will be ``{x: {y: [z]} }``.\n\n    >>> print(json.dumps(df.values_to_dict(), indent=4))\n    {\n        \"A\": {\n            \"a\": [\n                \"1\"\n            ],\n            \"b\": [\n                \"2\"\n            ]\n        },\n        \"B\": {\n            \"c\": [\n                \"3\"\n            ],\n            \"d\": [\n                \"3\",\n                \"4\"\n            ]\n        }\n    }\n\n    Use many unique of column values as key first, the result will be\n    ``{y: {z: [x]} }``.\n\n    >>> print(json.dumps(df.values_to_dict(ascending=False), indent=4))\n    {\n        \"a\": {\n            \"1\": [\n                \"A\"\n            ]\n        },\n        \"b\": {\n            \"2\": [\n                \"A\"\n            ]\n        },\n        \"c\": {\n            \"3\": [\n                \"B\"\n            ]\n        },\n        \"d\": {\n            \"3\": [\n                \"B\"\n            ],\n            \"4\": [\n                \"B\"\n            ]\n        }\n    }\n\n    Output the arbitrary order like ``{z: x}  or ``{x: {z: [y]} }``,\n    via ``order`` argument.\n\n    >>> print(json.dumps(df.values_to_dict(order=[\"x\", \"z\"]), indent=4))\n    {\n        \"A\": [\n            \"1\",\n            \"2\"\n        ],\n        \"B\": [\n            \"3\",\n            \"3\",\n            \"4\"\n        ]\n    }\n    >>> print(json.dumps(df.values_to_dict(order=[\"x\", \"z\", \"y\"]), indent=4))\n    {\n        \"A\": {\n            \"1\": [\n                \"a\"\n            ],\n            \"2\": [\n                \"b\"\n            ]\n        },\n        \"B\": {\n            \"3\": [\n                \"c\",\n                \"d\"\n            ],\n            \"4\": [\n                \"d\"\n            ]\n        }\n    }\n\n    It also could convert one column DataFrame. But ``ascending`` wouldn' work.\n    The result would be ``{index: [values]}``.\n\n    >>> print(json.dumps(df[[\"x\"]].values_to_dict(), indent=4))\n    {\n        \"0\": [\n            \"A\"\n        ],\n        \"1\": [\n            \"A\"\n        ],\n        \"2\": [\n            \"B\"\n        ],\n        \"3\": [\n            \"B\"\n        ],\n        \"4\": [\n            \"B\"\n        ]\n    }\n\n    Unpack one element value list.\n\n    >>> print(json.dumps(df.values_to_dict(to_list=False), indent=4))\n    {\n        \"A\": {\n            \"a\": \"1\",\n            \"b\": \"2\"\n        },\n        \"B\": {\n            \"c\": \"3\",\n            \"d\": [\n                \"3\",\n                \"4\"\n            ]\n        }\n    }\n    \"\"\"\n\n    if df.columns.__len__() == 1:  # one columns DataFrame\n        return df.to_series().values_to_dict(to_list=to_list)\n\n    columns = order or (\n        df.nunique()\n        .sort_values(\n            ascending=ascending,\n        )\n        .index\n    )\n    return _dict(df[columns], to_list=to_list)\n\n\ndef _dict(df: pd.DataFrame, to_list: bool) -> dict:\n    key_column, *value_column = df.columns\n\n    if df.columns.__len__() == 2:  # two column DataFrame\n        return df.to_series(\n            index_column=key_column,\n            value_column=value_column[0],\n        ).values_to_dict(to_list=to_list)\n\n    return {\n        key: _dict(\n            df.loc[df[key_column] == key, value_column],\n            to_list=to_list,\n        )\n        for key in df[key_column].unique()\n    }\n", "path": "dtoolkit/accessor/dataframe/values_to_dict.py"}]}