We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
BUG: Modin fails to read a feather file if it contains index metadata
### Modin version checks

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the latest released version of Modin.

- [X] I have confirmed this bug exists on the main branch of Modin. (In order to do this you can follow [this guide](https://modin.readthedocs.io/en/stable/getting_started/installation.html#installing-from-the-github-master-branch).)


### Reproducible Example

```python
import tempfile
import pandas
import modin.pandas as pd

with tempfile.NamedTemporaryFile() as fl:
    pandas.DataFrame({"a": [1, 2, 3]}, index=pandas.Index([0, 1, 2])).to_feather(fl.name)

    pandas.read_feather(fl.name) # reads okay
    pd.read_feather(fl.name) # ValueError
```


### Issue Description

Feather format actually should only allow writing dataframes with default RangeIndexes as [other indices are considered to be unsupported so far](https://github.com/pandas-dev/pandas/issues/28208#issuecomment-526002530), however, it appears that a dataframe can also be written if its index <i>matches</i> the values of default index, [there are no requirements in the writer's code for this to be an actual RangeIndex.](https://github.com/pandas-dev/pandas/blob/37ea63d540fd27274cad6585082c91b1283f963d/pandas/io/feather_format.py#L75-L79) This extra index metadata confuses modin and makes its `.read_feather()` to fail on such fails.

### Expected Behavior

We should filter out the index columns here: https://github.com/modin-project/modin/blob/d14abf463548dff147a12cd59727cbf0dbee7721/modin/core/io/column_stores/feather_dispatcher.py#L65

### Error Logs

<details>

```python-traceback

Traceback (most recent call last):
  File "srt.py", line 9, in <module>
    pd.read_feather(fl.name) # Error
  File "repos/modin/modin/logging/logger_decorator.py", line 128, in run_and_log
    return obj(*args, **kwargs)
  File "repos/modin/modin/pandas/io.py", line 509, in read_feather
    return DataFrame(query_compiler=FactoryDispatcher.read_feather(**kwargs))
  File "repos/modin/modin/core/execution/dispatching/factories/dispatcher.py", line 235, in read_feather
    return cls.get_factory()._read_feather(**kwargs)
  File "repos/modin/modin/core/execution/dispatching/factories/factories.py", line 293, in _read_feather
    return cls.io_cls.read_feather(**kwargs)
  File "repos/modin/modin/logging/logger_decorator.py", line 128, in run_and_log
    return obj(*args, **kwargs)
  File "repos/modin/modin/core/io/file_dispatcher.py", line 157, in read
    query_compiler = cls._read(*args, **kwargs)
  File "repos/modin/modin/logging/logger_decorator.py", line 128, in run_and_log
    return obj(*args, **kwargs)
  File "repos/modin/modin/core/io/column_stores/feather_dispatcher.py", line 66, in _read
    return cls.build_query_compiler(path, columns, use_threads=False)
  File "repos/modin/modin/logging/logger_decorator.py", line 128, in run_and_log
    return obj(*args, **kwargs)
  File "repos/modin/modin/core/io/column_stores/column_store_dispatcher.py", line 224, in build_query_compiler
    cls.build_dtypes(partition_ids[-1], columns)
  File "repos/modin/modin/logging/logger_decorator.py", line 128, in run_and_log
    return obj(*args, **kwargs)
  File "repos/modin/modin/core/io/column_stores/column_store_dispatcher.py", line 197, in build_dtypes
    dtypes.index = columns
  File "miniconda3/envs/modinc/lib/python3.8/site-packages/pandas/core/generic.py", line 5915, in __setattr__
    return object.__setattr__(self, name, value)
  File "pandas/_libs/properties.pyx", line 69, in pandas._libs.properties.AxisProperty.__set__
  File "miniconda3/envs/modinc/lib/python3.8/site-packages/pandas/core/series.py", line 593, in _set_axis
    self._mgr.set_axis(axis, labels)
  File "miniconda3/envs/modinc/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 230, in set_axis
    self._validate_set_axis(axis, new_labels)
  File "miniconda3/envs/modinc/lib/python3.8/site-packages/pandas/core/internals/base.py", line 70, in _validate_set_axis
    raise ValueError(
ValueError: Length mismatch: Expected axis has 1 elements, new values have 2 elements


```

</details>


### Installed Versions

<details>

Replace this line with the output of pd.show_versions()

</details>


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `modin/core/io/column_stores/feather_dispatcher.py`
Content:
```
1 # Licensed to Modin Development Team under one or more contributor license agreements.
2 # See the NOTICE file distributed with this work for additional information regarding
3 # copyright ownership.  The Modin Development Team licenses this file to you under the
4 # Apache License, Version 2.0 (the "License"); you may not use this file except in
5 # compliance with the License.  You may obtain a copy of the License at
6 #
7 #     http://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software distributed under
10 # the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
11 # ANY KIND, either express or implied. See the License for the specific language
12 # governing permissions and limitations under the License.
13 
14 """Module houses `FeatherDispatcher` class, that is used for reading `.feather` files."""
15 
16 from modin.core.io.column_stores.column_store_dispatcher import ColumnStoreDispatcher
17 from modin.utils import import_optional_dependency
18 from modin.core.io.file_dispatcher import OpenFile
19 
20 
21 class FeatherDispatcher(ColumnStoreDispatcher):
22     """Class handles utils for reading `.feather` files."""
23 
24     @classmethod
25     def _read(cls, path, columns=None, **kwargs):
26         """
27         Read data from the file path, returning a query compiler.
28 
29         Parameters
30         ----------
31         path : str or file-like object
32             The filepath of the feather file.
33         columns : array-like, optional
34             Columns to read from file. If not provided, all columns are read.
35         **kwargs : dict
36             `read_feather` function kwargs.
37 
38         Returns
39         -------
40         BaseQueryCompiler
41             Query compiler with imported data for further processing.
42 
43         Notes
44         -----
45         `PyArrow` engine and local files only are supported for now,
46         multi threading is set to False by default.
47         PyArrow feather is used. Please refer to the documentation here
48         https://arrow.apache.org/docs/python/api.html#feather-format
49         """
50         path = cls.get_path(path)
51         if columns is None:
52             import_optional_dependency(
53                 "pyarrow", "pyarrow is required to read feather files."
54             )
55             from pyarrow import ipc
56 
57             with OpenFile(
58                 path,
59                 **(kwargs.get("storage_options", None) or {}),
60             ) as file:
61                 # Opens the file to extract its metadata
62                 reader = ipc.open_file(file)
63             # TODO: pyarrow's schema contains much more metadata than just column names, it also
64             # has dtypes and index information that we could use when building a dataframe
65             columns = reader.schema.names
66         return cls.build_query_compiler(path, columns, use_threads=False)
67
```
--- 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/modin/core/io/column_stores/feather_dispatcher.py b/modin/core/io/column_stores/feather_dispatcher.py
--- a/modin/core/io/column_stores/feather_dispatcher.py
+++ b/modin/core/io/column_stores/feather_dispatcher.py
@@ -62,5 +62,13 @@
                 reader = ipc.open_file(file)
             # TODO: pyarrow's schema contains much more metadata than just column names, it also
             # has dtypes and index information that we could use when building a dataframe
-            columns = reader.schema.names
+            index_cols = frozenset(
+                col
+                for col in reader.schema.pandas_metadata["index_columns"]
+                # 'index_columns' field may also contain dictionary fields describing actual
+                # RangeIndices, so we're only filtering here for string column names
+                if isinstance(col, str)
+            )
+            # Filtering out the columns that describe the frame's index
+            columns = [col for col in reader.schema.names if col not in index_cols]
         return cls.build_query_compiler(path, columns, use_threads=False)

{"golden_diff": "diff --git a/modin/core/io/column_stores/feather_dispatcher.py b/modin/core/io/column_stores/feather_dispatcher.py\n--- a/modin/core/io/column_stores/feather_dispatcher.py\n+++ b/modin/core/io/column_stores/feather_dispatcher.py\n@@ -62,5 +62,13 @@\n                 reader = ipc.open_file(file)\n             # TODO: pyarrow's schema contains much more metadata than just column names, it also\n             # has dtypes and index information that we could use when building a dataframe\n-            columns = reader.schema.names\n+            index_cols = frozenset(\n+                col\n+                for col in reader.schema.pandas_metadata[\"index_columns\"]\n+                # 'index_columns' field may also contain dictionary fields describing actual\n+                # RangeIndices, so we're only filtering here for string column names\n+                if isinstance(col, str)\n+            )\n+            # Filtering out the columns that describe the frame's index\n+            columns = [col for col in reader.schema.names if col not in index_cols]\n         return cls.build_query_compiler(path, columns, use_threads=False)\n", "issue": "BUG: Modin fails to read a feather file if it contains index metadata\n### Modin version checks\n\n- [X] I have checked that this issue has not already been reported.\n\n- [X] I have confirmed this bug exists on the latest released version of Modin.\n\n- [X] I have confirmed this bug exists on the main branch of Modin. (In order to do this you can follow [this guide](https://modin.readthedocs.io/en/stable/getting_started/installation.html#installing-from-the-github-master-branch).)\n\n\n### Reproducible Example\n\n```python\nimport tempfile\r\nimport pandas\r\nimport modin.pandas as pd\r\n\r\nwith tempfile.NamedTemporaryFile() as fl:\r\n    pandas.DataFrame({\"a\": [1, 2, 3]}, index=pandas.Index([0, 1, 2])).to_feather(fl.name)\r\n\r\n    pandas.read_feather(fl.name) # reads okay\r\n    pd.read_feather(fl.name) # ValueError\n```\n\n\n### Issue Description\n\nFeather format actually should only allow writing dataframes with default RangeIndexes as [other indices are considered to be unsupported so far](https://github.com/pandas-dev/pandas/issues/28208#issuecomment-526002530), however, it appears that a dataframe can also be written if its index <i>matches</i> the values of default index, [there are no requirements in the writer's code for this to be an actual RangeIndex.](https://github.com/pandas-dev/pandas/blob/37ea63d540fd27274cad6585082c91b1283f963d/pandas/io/feather_format.py#L75-L79) This extra index metadata confuses modin and makes its `.read_feather()` to fail on such fails.\n\n### Expected Behavior\n\nWe should filter out the index columns here: https://github.com/modin-project/modin/blob/d14abf463548dff147a12cd59727cbf0dbee7721/modin/core/io/column_stores/feather_dispatcher.py#L65\n\n### Error Logs\n\n<details>\r\n\r\n```python-traceback\r\n\r\nTraceback (most recent call last):\r\n  File \"srt.py\", line 9, in <module>\r\n    pd.read_feather(fl.name) # Error\r\n  File \"repos/modin/modin/logging/logger_decorator.py\", line 128, in run_and_log\r\n    return obj(*args, **kwargs)\r\n  File \"repos/modin/modin/pandas/io.py\", line 509, in read_feather\r\n    return DataFrame(query_compiler=FactoryDispatcher.read_feather(**kwargs))\r\n  File \"repos/modin/modin/core/execution/dispatching/factories/dispatcher.py\", line 235, in read_feather\r\n    return cls.get_factory()._read_feather(**kwargs)\r\n  File \"repos/modin/modin/core/execution/dispatching/factories/factories.py\", line 293, in _read_feather\r\n    return cls.io_cls.read_feather(**kwargs)\r\n  File \"repos/modin/modin/logging/logger_decorator.py\", line 128, in run_and_log\r\n    return obj(*args, **kwargs)\r\n  File \"repos/modin/modin/core/io/file_dispatcher.py\", line 157, in read\r\n    query_compiler = cls._read(*args, **kwargs)\r\n  File \"repos/modin/modin/logging/logger_decorator.py\", line 128, in run_and_log\r\n    return obj(*args, **kwargs)\r\n  File \"repos/modin/modin/core/io/column_stores/feather_dispatcher.py\", line 66, in _read\r\n    return cls.build_query_compiler(path, columns, use_threads=False)\r\n  File \"repos/modin/modin/logging/logger_decorator.py\", line 128, in run_and_log\r\n    return obj(*args, **kwargs)\r\n  File \"repos/modin/modin/core/io/column_stores/column_store_dispatcher.py\", line 224, in build_query_compiler\r\n    cls.build_dtypes(partition_ids[-1], columns)\r\n  File \"repos/modin/modin/logging/logger_decorator.py\", line 128, in run_and_log\r\n    return obj(*args, **kwargs)\r\n  File \"repos/modin/modin/core/io/column_stores/column_store_dispatcher.py\", line 197, in build_dtypes\r\n    dtypes.index = columns\r\n  File \"miniconda3/envs/modinc/lib/python3.8/site-packages/pandas/core/generic.py\", line 5915, in __setattr__\r\n    return object.__setattr__(self, name, value)\r\n  File \"pandas/_libs/properties.pyx\", line 69, in pandas._libs.properties.AxisProperty.__set__\r\n  File \"miniconda3/envs/modinc/lib/python3.8/site-packages/pandas/core/series.py\", line 593, in _set_axis\r\n    self._mgr.set_axis(axis, labels)\r\n  File \"miniconda3/envs/modinc/lib/python3.8/site-packages/pandas/core/internals/managers.py\", line 230, in set_axis\r\n    self._validate_set_axis(axis, new_labels)\r\n  File \"miniconda3/envs/modinc/lib/python3.8/site-packages/pandas/core/internals/base.py\", line 70, in _validate_set_axis\r\n    raise ValueError(\r\nValueError: Length mismatch: Expected axis has 1 elements, new values have 2 elements\r\n\r\n\r\n```\r\n\r\n</details>\r\n\n\n### Installed Versions\n\n<details>\r\n\r\nReplace this line with the output of pd.show_versions()\r\n\r\n</details>\r\n\n", "before_files": [{"content": "# Licensed to Modin Development Team under one or more contributor license agreements.\n# See the NOTICE file distributed with this work for additional information regarding\n# copyright ownership.  The Modin Development Team licenses this file to you under the\n# Apache License, Version 2.0 (the \"License\"); you may not use this file except in\n# compliance with the License.  You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software distributed under\n# the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF\n# ANY KIND, either express or implied. See the License for the specific language\n# governing permissions and limitations under the License.\n\n\"\"\"Module houses `FeatherDispatcher` class, that is used for reading `.feather` files.\"\"\"\n\nfrom modin.core.io.column_stores.column_store_dispatcher import ColumnStoreDispatcher\nfrom modin.utils import import_optional_dependency\nfrom modin.core.io.file_dispatcher import OpenFile\n\n\nclass FeatherDispatcher(ColumnStoreDispatcher):\n    \"\"\"Class handles utils for reading `.feather` files.\"\"\"\n\n    @classmethod\n    def _read(cls, path, columns=None, **kwargs):\n        \"\"\"\n        Read data from the file path, returning a query compiler.\n\n        Parameters\n        ----------\n        path : str or file-like object\n            The filepath of the feather file.\n        columns : array-like, optional\n            Columns to read from file. If not provided, all columns are read.\n        **kwargs : dict\n            `read_feather` function kwargs.\n\n        Returns\n        -------\n        BaseQueryCompiler\n            Query compiler with imported data for further processing.\n\n        Notes\n        -----\n        `PyArrow` engine and local files only are supported for now,\n        multi threading is set to False by default.\n        PyArrow feather is used. Please refer to the documentation here\n        https://arrow.apache.org/docs/python/api.html#feather-format\n        \"\"\"\n        path = cls.get_path(path)\n        if columns is None:\n            import_optional_dependency(\n                \"pyarrow\", \"pyarrow is required to read feather files.\"\n            )\n            from pyarrow import ipc\n\n            with OpenFile(\n                path,\n                **(kwargs.get(\"storage_options\", None) or {}),\n            ) as file:\n                # Opens the file to extract its metadata\n                reader = ipc.open_file(file)\n            # TODO: pyarrow's schema contains much more metadata than just column names, it also\n            # has dtypes and index information that we could use when building a dataframe\n            columns = reader.schema.names\n        return cls.build_query_compiler(path, columns, use_threads=False)\n", "path": "modin/core/io/column_stores/feather_dispatcher.py"}], "after_files": [{"content": "# Licensed to Modin Development Team under one or more contributor license agreements.\n# See the NOTICE file distributed with this work for additional information regarding\n# copyright ownership.  The Modin Development Team licenses this file to you under the\n# Apache License, Version 2.0 (the \"License\"); you may not use this file except in\n# compliance with the License.  You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software distributed under\n# the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF\n# ANY KIND, either express or implied. See the License for the specific language\n# governing permissions and limitations under the License.\n\n\"\"\"Module houses `FeatherDispatcher` class, that is used for reading `.feather` files.\"\"\"\n\nfrom modin.core.io.column_stores.column_store_dispatcher import ColumnStoreDispatcher\nfrom modin.utils import import_optional_dependency\nfrom modin.core.io.file_dispatcher import OpenFile\n\n\nclass FeatherDispatcher(ColumnStoreDispatcher):\n    \"\"\"Class handles utils for reading `.feather` files.\"\"\"\n\n    @classmethod\n    def _read(cls, path, columns=None, **kwargs):\n        \"\"\"\n        Read data from the file path, returning a query compiler.\n\n        Parameters\n        ----------\n        path : str or file-like object\n            The filepath of the feather file.\n        columns : array-like, optional\n            Columns to read from file. If not provided, all columns are read.\n        **kwargs : dict\n            `read_feather` function kwargs.\n\n        Returns\n        -------\n        BaseQueryCompiler\n            Query compiler with imported data for further processing.\n\n        Notes\n        -----\n        `PyArrow` engine and local files only are supported for now,\n        multi threading is set to False by default.\n        PyArrow feather is used. Please refer to the documentation here\n        https://arrow.apache.org/docs/python/api.html#feather-format\n        \"\"\"\n        path = cls.get_path(path)\n        if columns is None:\n            import_optional_dependency(\n                \"pyarrow\", \"pyarrow is required to read feather files.\"\n            )\n            from pyarrow import ipc\n\n            with OpenFile(\n                path,\n                **(kwargs.get(\"storage_options\", None) or {}),\n            ) as file:\n                # Opens the file to extract its metadata\n                reader = ipc.open_file(file)\n            # TODO: pyarrow's schema contains much more metadata than just column names, it also\n            # has dtypes and index information that we could use when building a dataframe\n            index_cols = frozenset(\n                col\n                for col in reader.schema.pandas_metadata[\"index_columns\"]\n                # 'index_columns' field may also contain dictionary fields describing actual\n                # RangeIndices, so we're only filtering here for string column names\n                if isinstance(col, str)\n            )\n            # Filtering out the columns that describe the frame's index\n            columns = [col for col in reader.schema.names if col not in index_cols]\n        return cls.build_query_compiler(path, columns, use_threads=False)\n", "path": "modin/core/io/column_stores/feather_dispatcher.py"}]}