We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
client.time_series.get_time_series does not return metadata
**Describe the bug**
When executing `client.time_series.get_time_series()` with `include_metadata = True` no metadata is returned.

**To Reproduce**
Runnable code reproducing the error.
```
import cognite
import requests
import os
import numpy as np
import pandas as pd
from datetime import datetime, timedelta

from cognite.client.stable.time_series import TimeSeries
sm_api = os.environ['SM_API_KEY']
client = cognite.CogniteClient(api_key = sm_api)
ts_name = 'Test_tssssss'
my_time_series = [TimeSeries(name=ts_name, 
                             description = 'test_description',
                             metadata = { 'ASSETSCOPENAME' : 'meta_test_1' })]
client.time_series.post_time_series(my_time_series)

# create dummy data
np.random.seed(1338)
start_time = int((datetime.now()-timedelta(1)).strftime("%s"))
timestamps = [(start_time + i * 10)*1000 for i in np.arange(11)]
df = pd.DataFrame({'timestamp' : timestamps})
df[ts_name] = np.random.random(df.shape[0])
client.datapoints.post_datapoints_frame(df)
# get time_series
ts1 = client.time_series.get_time_series(name = ts_name,
                                         include_metadata = True).to_pandas()
ts1_id = ts1['id'].loc[0] 
print(ts1.loc[0])
# no meta data
# requests:
# first with no metadata
r1 = requests.get(url = 'https://api.cognitedata.com/api/0.5/projects/smart-maintenance-sandbox/timeseries/' + str(ts1_id) ,
                  headers= { 'Api-Key' : sm_api} , params = {"includeMetadata" : False})
print(r1.text.split('\n'))
# then with metadata
r1 = requests.get(url = 'https://api.cognitedata.com/api/0.5/projects/smart-maintenance-sandbox/timeseries/' + str(ts1_id) ,
                  headers= { 'Api-Key' : sm_api} , params = {"includeMetadata" : True})
print(r1.text.split('\n'))
```
**Expected behavior**
The `client.time_series.get_time_series(name = ts_name,include_metadata = True)` should return the metadata.



--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `cognite/client/stable/time_series.py`
Content:
```
1 # -*- coding: utf-8 -*-
2 from copy import deepcopy
3 from typing import List
4 from urllib.parse import quote
5 
6 import pandas as pd
7 
8 from cognite.client._api_client import APIClient, CogniteCollectionResponse, CogniteResource, CogniteResponse
9 
10 
11 class TimeSeriesResponse(CogniteResponse):
12     """Time series Response Object"""
13 
14     def __init__(self, internal_representation):
15         super().__init__(internal_representation)
16         item = self.to_json()
17         self.id = item.get("id")
18         self.name = item.get("name")
19         self.unit = item.get("unit")
20         self.is_step = item.get("isStep")
21         self.is_string = item.get("isString")
22         self.created_time = item.get("createdTime")
23         self.last_updated_time = item.get("lastUpdatedTime")
24         self.metadata = item.get("metadata")
25         self.asset_id = item.get("assetId")
26         self.description = item.get("description")
27 
28     def to_pandas(self):
29         """Returns data as a pandas dataframe"""
30         if len(self.to_json()) > 0:
31             ts = self.to_json().copy()
32             if "metadata" in ts:
33                 # Hack to avoid path ending up as first element in dict as from_dict will fail
34                 metadata = ts.pop("metadata")
35                 df = pd.DataFrame.from_dict(ts, orient="index")
36                 df.loc["metadata"] = [metadata]
37             else:
38                 df = pd.DataFrame.from_dict(ts, orient="index")
39             return df
40         return pd.DataFrame()
41 
42 
43 class TimeSeriesListResponse(CogniteCollectionResponse):
44     """Time series Response Object"""
45 
46     _RESPONSE_CLASS = TimeSeriesResponse
47 
48     def to_pandas(self, include_metadata: bool = False):
49         """Returns data as a pandas dataframe
50 
51         Args:
52             include_metadata (bool): Whether or not to include metadata fields in the resulting dataframe
53         """
54         items = deepcopy(self.internal_representation["data"]["items"])
55         if items and items[0].get("metadata") is None:
56             return pd.DataFrame(items)
57         for d in items:
58             if d.get("metadata"):
59                 metadata = d.pop("metadata")
60                 if include_metadata:
61                     d.update(metadata)
62         return pd.DataFrame(items)
63 
64 
65 class TimeSeries(CogniteResource):
66     """Data Transfer Object for a time series.
67 
68     Args:
69         name (str):       Unique name of time series.
70         is_string (bool):    Whether the time series is string valued or not.
71         metadata (dict):    Metadata.
72         unit (str):         Physical unit of the time series.
73         asset_id (int):     Asset that this time series belongs to.
74         description (str):  Description of the time series.
75         security_categories (list(int)): Security categories required in order to access this time series.
76         is_step (bool):        Whether or not the time series is a step series.
77 
78     """
79 
80     def __init__(
81         self,
82         name,
83         is_string=False,
84         metadata=None,
85         unit=None,
86         asset_id=None,
87         description=None,
88         security_categories=None,
89         is_step=None,
90     ):
91         self.name = name
92         self.is_string = is_string
93         self.metadata = metadata
94         self.unit = unit
95         self.asset_id = asset_id
96         self.description = description
97         self.security_categories = security_categories
98         self.is_step = is_step
99 
100 
101 class TimeSeriesClient(APIClient):
102     def __init__(self, **kwargs):
103         super().__init__(version="0.5", **kwargs)
104 
105     def get_time_series(
106         self, prefix=None, description=None, include_metadata=False, asset_id=None, path=None, **kwargs
107     ) -> TimeSeriesListResponse:
108         """Returns an object containing the requested timeseries.
109 
110         Args:
111             prefix (str):           List timeseries with this prefix in the name.
112 
113             description (str):      Filter timeseries taht contains this string in its description.
114 
115             include_metadata (bool):    Decide if the metadata field should be returned or not. Defaults to False.
116 
117             asset_id (int):        Get timeseries related to this asset.
118 
119             path (List[int]):             Get timeseries under this asset path branch.
120 
121         Keyword Arguments:
122             limit (int):            Number of results to return.
123 
124             autopaging (bool):      Whether or not to automatically page through results. If set to true, limit will be
125                                     disregarded. Defaults to False.
126 
127         Returns:
128             stable.time_series.TimeSeriesListResponse: A data object containing the requested timeseries with several getter methods with different
129             output formats.
130 
131         Examples:
132             Get all time series for a given asset::
133 
134                 client = CogniteClient()
135                 res = client.time_series.get_time_series(asset_id=123, autopaging=True)
136                 print(res.to_pandas())
137         """
138         autopaging = kwargs.get("autopaging", False)
139         url = "/timeseries"
140         params = {
141             "q": prefix,
142             "description": description,
143             "includeMetadata": include_metadata,
144             "assetId": asset_id,
145             "path": str(path) if path else None,
146             "limit": kwargs.get("limit", self._LIMIT) if not autopaging else self._LIMIT,
147         }
148 
149         res = self._get(url=url, params=params, autopaging=autopaging)
150         return TimeSeriesListResponse(res.json())
151 
152     def post_time_series(self, time_series: List[TimeSeries]) -> None:
153         """Create a new time series.
154 
155         Args:
156             time_series (list[stable.time_series.TimeSeries]):   List of time series data transfer objects to create.
157 
158         Returns:
159             None
160 
161         Examples:
162             Create a new time series::
163 
164                 from cognite.client.stable.time_series import TimeSeries
165                 client = CogniteClient()
166 
167                 my_time_series = [TimeSeries(name="my_ts_1")]
168 
169                 client.time_series.post_time_series(my_time_series)
170         """
171         url = "/timeseries"
172         items = [ts.camel_case_dict() for ts in time_series]
173         body = {"items": items}
174         self._post(url, body=body)
175 
176     def update_time_series(self, time_series: List[TimeSeries]) -> None:
177         """Update an existing time series.
178 
179         For each field that can be updated, a null value indicates that nothing should be done.
180 
181         Args:
182             time_series (list[stable.time_series.TimeSeries]):   List of time series data transfer objects to update.
183 
184         Returns:
185             None
186 
187         Examples:
188             Update the unit of a time series::
189 
190                 from cognite.client.stable.time_series import TimeSeries
191                 client = CogniteClient()
192 
193                 my_time_series = [TimeSeries(name="my_ts_1", unit="celsius")]
194 
195                 client.time_series.update_time_series(my_time_series)
196         """
197         url = "/timeseries"
198         items = [ts.camel_case_dict() for ts in time_series]
199         body = {"items": items}
200         self._put(url, body=body)
201 
202     def delete_time_series(self, name) -> None:
203         """Delete a timeseries.
204 
205         Args:
206             name (str):   Name of timeseries to delete.
207 
208         Returns:
209             None
210 
211         Examples:
212             Delete a time series by name::
213 
214                 client = CogniteClient()
215 
216                 client.time_series.delete_time_series(name="my_ts_1")
217         """
218         url = "/timeseries/{}".format(quote(name, safe=""))
219         self._delete(url)
220
```
--- 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/cognite/client/stable/time_series.py b/cognite/client/stable/time_series.py
--- a/cognite/client/stable/time_series.py
+++ b/cognite/client/stable/time_series.py
@@ -45,7 +45,7 @@
 
     _RESPONSE_CLASS = TimeSeriesResponse
 
-    def to_pandas(self, include_metadata: bool = False):
+    def to_pandas(self, include_metadata: bool = True):
         """Returns data as a pandas dataframe
 
         Args:

{"golden_diff": "diff --git a/cognite/client/stable/time_series.py b/cognite/client/stable/time_series.py\n--- a/cognite/client/stable/time_series.py\n+++ b/cognite/client/stable/time_series.py\n@@ -45,7 +45,7 @@\n \n     _RESPONSE_CLASS = TimeSeriesResponse\n \n-    def to_pandas(self, include_metadata: bool = False):\n+    def to_pandas(self, include_metadata: bool = True):\n         \"\"\"Returns data as a pandas dataframe\n \n         Args:\n", "issue": "client.time_series.get_time_series does not return metadata\n**Describe the bug**\r\nWhen executing `client.time_series.get_time_series()` with `include_metadata = True` no metadata is returned.\r\n\r\n**To Reproduce**\r\nRunnable code reproducing the error.\r\n```\r\nimport cognite\r\nimport requests\r\nimport os\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom datetime import datetime, timedelta\r\n\r\nfrom cognite.client.stable.time_series import TimeSeries\r\nsm_api = os.environ['SM_API_KEY']\r\nclient = cognite.CogniteClient(api_key = sm_api)\r\nts_name = 'Test_tssssss'\r\nmy_time_series = [TimeSeries(name=ts_name, \r\n                             description = 'test_description',\r\n                             metadata = { 'ASSETSCOPENAME' : 'meta_test_1' })]\r\nclient.time_series.post_time_series(my_time_series)\r\n\r\n# create dummy data\r\nnp.random.seed(1338)\r\nstart_time = int((datetime.now()-timedelta(1)).strftime(\"%s\"))\r\ntimestamps = [(start_time + i * 10)*1000 for i in np.arange(11)]\r\ndf = pd.DataFrame({'timestamp' : timestamps})\r\ndf[ts_name] = np.random.random(df.shape[0])\r\nclient.datapoints.post_datapoints_frame(df)\r\n# get time_series\r\nts1 = client.time_series.get_time_series(name = ts_name,\r\n                                         include_metadata = True).to_pandas()\r\nts1_id = ts1['id'].loc[0] \r\nprint(ts1.loc[0])\r\n# no meta data\r\n# requests:\r\n# first with no metadata\r\nr1 = requests.get(url = 'https://api.cognitedata.com/api/0.5/projects/smart-maintenance-sandbox/timeseries/' + str(ts1_id) ,\r\n                  headers= { 'Api-Key' : sm_api} , params = {\"includeMetadata\" : False})\r\nprint(r1.text.split('\\n'))\r\n# then with metadata\r\nr1 = requests.get(url = 'https://api.cognitedata.com/api/0.5/projects/smart-maintenance-sandbox/timeseries/' + str(ts1_id) ,\r\n                  headers= { 'Api-Key' : sm_api} , params = {\"includeMetadata\" : True})\r\nprint(r1.text.split('\\n'))\r\n```\r\n**Expected behavior**\r\nThe `client.time_series.get_time_series(name = ts_name,include_metadata = True)` should return the metadata.\r\n\r\n\n", "before_files": [{"content": "# -*- coding: utf-8 -*-\nfrom copy import deepcopy\nfrom typing import List\nfrom urllib.parse import quote\n\nimport pandas as pd\n\nfrom cognite.client._api_client import APIClient, CogniteCollectionResponse, CogniteResource, CogniteResponse\n\n\nclass TimeSeriesResponse(CogniteResponse):\n    \"\"\"Time series Response Object\"\"\"\n\n    def __init__(self, internal_representation):\n        super().__init__(internal_representation)\n        item = self.to_json()\n        self.id = item.get(\"id\")\n        self.name = item.get(\"name\")\n        self.unit = item.get(\"unit\")\n        self.is_step = item.get(\"isStep\")\n        self.is_string = item.get(\"isString\")\n        self.created_time = item.get(\"createdTime\")\n        self.last_updated_time = item.get(\"lastUpdatedTime\")\n        self.metadata = item.get(\"metadata\")\n        self.asset_id = item.get(\"assetId\")\n        self.description = item.get(\"description\")\n\n    def to_pandas(self):\n        \"\"\"Returns data as a pandas dataframe\"\"\"\n        if len(self.to_json()) > 0:\n            ts = self.to_json().copy()\n            if \"metadata\" in ts:\n                # Hack to avoid path ending up as first element in dict as from_dict will fail\n                metadata = ts.pop(\"metadata\")\n                df = pd.DataFrame.from_dict(ts, orient=\"index\")\n                df.loc[\"metadata\"] = [metadata]\n            else:\n                df = pd.DataFrame.from_dict(ts, orient=\"index\")\n            return df\n        return pd.DataFrame()\n\n\nclass TimeSeriesListResponse(CogniteCollectionResponse):\n    \"\"\"Time series Response Object\"\"\"\n\n    _RESPONSE_CLASS = TimeSeriesResponse\n\n    def to_pandas(self, include_metadata: bool = False):\n        \"\"\"Returns data as a pandas dataframe\n\n        Args:\n            include_metadata (bool): Whether or not to include metadata fields in the resulting dataframe\n        \"\"\"\n        items = deepcopy(self.internal_representation[\"data\"][\"items\"])\n        if items and items[0].get(\"metadata\") is None:\n            return pd.DataFrame(items)\n        for d in items:\n            if d.get(\"metadata\"):\n                metadata = d.pop(\"metadata\")\n                if include_metadata:\n                    d.update(metadata)\n        return pd.DataFrame(items)\n\n\nclass TimeSeries(CogniteResource):\n    \"\"\"Data Transfer Object for a time series.\n\n    Args:\n        name (str):       Unique name of time series.\n        is_string (bool):    Whether the time series is string valued or not.\n        metadata (dict):    Metadata.\n        unit (str):         Physical unit of the time series.\n        asset_id (int):     Asset that this time series belongs to.\n        description (str):  Description of the time series.\n        security_categories (list(int)): Security categories required in order to access this time series.\n        is_step (bool):        Whether or not the time series is a step series.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        name,\n        is_string=False,\n        metadata=None,\n        unit=None,\n        asset_id=None,\n        description=None,\n        security_categories=None,\n        is_step=None,\n    ):\n        self.name = name\n        self.is_string = is_string\n        self.metadata = metadata\n        self.unit = unit\n        self.asset_id = asset_id\n        self.description = description\n        self.security_categories = security_categories\n        self.is_step = is_step\n\n\nclass TimeSeriesClient(APIClient):\n    def __init__(self, **kwargs):\n        super().__init__(version=\"0.5\", **kwargs)\n\n    def get_time_series(\n        self, prefix=None, description=None, include_metadata=False, asset_id=None, path=None, **kwargs\n    ) -> TimeSeriesListResponse:\n        \"\"\"Returns an object containing the requested timeseries.\n\n        Args:\n            prefix (str):           List timeseries with this prefix in the name.\n\n            description (str):      Filter timeseries taht contains this string in its description.\n\n            include_metadata (bool):    Decide if the metadata field should be returned or not. Defaults to False.\n\n            asset_id (int):        Get timeseries related to this asset.\n\n            path (List[int]):             Get timeseries under this asset path branch.\n\n        Keyword Arguments:\n            limit (int):            Number of results to return.\n\n            autopaging (bool):      Whether or not to automatically page through results. If set to true, limit will be\n                                    disregarded. Defaults to False.\n\n        Returns:\n            stable.time_series.TimeSeriesListResponse: A data object containing the requested timeseries with several getter methods with different\n            output formats.\n\n        Examples:\n            Get all time series for a given asset::\n\n                client = CogniteClient()\n                res = client.time_series.get_time_series(asset_id=123, autopaging=True)\n                print(res.to_pandas())\n        \"\"\"\n        autopaging = kwargs.get(\"autopaging\", False)\n        url = \"/timeseries\"\n        params = {\n            \"q\": prefix,\n            \"description\": description,\n            \"includeMetadata\": include_metadata,\n            \"assetId\": asset_id,\n            \"path\": str(path) if path else None,\n            \"limit\": kwargs.get(\"limit\", self._LIMIT) if not autopaging else self._LIMIT,\n        }\n\n        res = self._get(url=url, params=params, autopaging=autopaging)\n        return TimeSeriesListResponse(res.json())\n\n    def post_time_series(self, time_series: List[TimeSeries]) -> None:\n        \"\"\"Create a new time series.\n\n        Args:\n            time_series (list[stable.time_series.TimeSeries]):   List of time series data transfer objects to create.\n\n        Returns:\n            None\n\n        Examples:\n            Create a new time series::\n\n                from cognite.client.stable.time_series import TimeSeries\n                client = CogniteClient()\n\n                my_time_series = [TimeSeries(name=\"my_ts_1\")]\n\n                client.time_series.post_time_series(my_time_series)\n        \"\"\"\n        url = \"/timeseries\"\n        items = [ts.camel_case_dict() for ts in time_series]\n        body = {\"items\": items}\n        self._post(url, body=body)\n\n    def update_time_series(self, time_series: List[TimeSeries]) -> None:\n        \"\"\"Update an existing time series.\n\n        For each field that can be updated, a null value indicates that nothing should be done.\n\n        Args:\n            time_series (list[stable.time_series.TimeSeries]):   List of time series data transfer objects to update.\n\n        Returns:\n            None\n\n        Examples:\n            Update the unit of a time series::\n\n                from cognite.client.stable.time_series import TimeSeries\n                client = CogniteClient()\n\n                my_time_series = [TimeSeries(name=\"my_ts_1\", unit=\"celsius\")]\n\n                client.time_series.update_time_series(my_time_series)\n        \"\"\"\n        url = \"/timeseries\"\n        items = [ts.camel_case_dict() for ts in time_series]\n        body = {\"items\": items}\n        self._put(url, body=body)\n\n    def delete_time_series(self, name) -> None:\n        \"\"\"Delete a timeseries.\n\n        Args:\n            name (str):   Name of timeseries to delete.\n\n        Returns:\n            None\n\n        Examples:\n            Delete a time series by name::\n\n                client = CogniteClient()\n\n                client.time_series.delete_time_series(name=\"my_ts_1\")\n        \"\"\"\n        url = \"/timeseries/{}\".format(quote(name, safe=\"\"))\n        self._delete(url)\n", "path": "cognite/client/stable/time_series.py"}], "after_files": [{"content": "# -*- coding: utf-8 -*-\nfrom copy import deepcopy\nfrom typing import List\nfrom urllib.parse import quote\n\nimport pandas as pd\n\nfrom cognite.client._api_client import APIClient, CogniteCollectionResponse, CogniteResource, CogniteResponse\n\n\nclass TimeSeriesResponse(CogniteResponse):\n    \"\"\"Time series Response Object\"\"\"\n\n    def __init__(self, internal_representation):\n        super().__init__(internal_representation)\n        item = self.to_json()\n        self.id = item.get(\"id\")\n        self.name = item.get(\"name\")\n        self.unit = item.get(\"unit\")\n        self.is_step = item.get(\"isStep\")\n        self.is_string = item.get(\"isString\")\n        self.created_time = item.get(\"createdTime\")\n        self.last_updated_time = item.get(\"lastUpdatedTime\")\n        self.metadata = item.get(\"metadata\")\n        self.asset_id = item.get(\"assetId\")\n        self.description = item.get(\"description\")\n\n    def to_pandas(self):\n        \"\"\"Returns data as a pandas dataframe\"\"\"\n        if len(self.to_json()) > 0:\n            ts = self.to_json().copy()\n            if \"metadata\" in ts:\n                # Hack to avoid path ending up as first element in dict as from_dict will fail\n                metadata = ts.pop(\"metadata\")\n                df = pd.DataFrame.from_dict(ts, orient=\"index\")\n                df.loc[\"metadata\"] = [metadata]\n            else:\n                df = pd.DataFrame.from_dict(ts, orient=\"index\")\n            return df\n        return pd.DataFrame()\n\n\nclass TimeSeriesListResponse(CogniteCollectionResponse):\n    \"\"\"Time series Response Object\"\"\"\n\n    _RESPONSE_CLASS = TimeSeriesResponse\n\n    def to_pandas(self, include_metadata: bool = True):\n        \"\"\"Returns data as a pandas dataframe\n\n        Args:\n            include_metadata (bool): Whether or not to include metadata fields in the resulting dataframe\n        \"\"\"\n        items = deepcopy(self.internal_representation[\"data\"][\"items\"])\n        if items and items[0].get(\"metadata\") is None:\n            return pd.DataFrame(items)\n        for d in items:\n            if d.get(\"metadata\"):\n                metadata = d.pop(\"metadata\")\n                if include_metadata:\n                    d.update(metadata)\n        return pd.DataFrame(items)\n\n\nclass TimeSeries(CogniteResource):\n    \"\"\"Data Transfer Object for a time series.\n\n    Args:\n        name (str):       Unique name of time series.\n        is_string (bool):    Whether the time series is string valued or not.\n        metadata (dict):    Metadata.\n        unit (str):         Physical unit of the time series.\n        asset_id (int):     Asset that this time series belongs to.\n        description (str):  Description of the time series.\n        security_categories (list(int)): Security categories required in order to access this time series.\n        is_step (bool):        Whether or not the time series is a step series.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        name,\n        is_string=False,\n        metadata=None,\n        unit=None,\n        asset_id=None,\n        description=None,\n        security_categories=None,\n        is_step=None,\n    ):\n        self.name = name\n        self.is_string = is_string\n        self.metadata = metadata\n        self.unit = unit\n        self.asset_id = asset_id\n        self.description = description\n        self.security_categories = security_categories\n        self.is_step = is_step\n\n\nclass TimeSeriesClient(APIClient):\n    def __init__(self, **kwargs):\n        super().__init__(version=\"0.5\", **kwargs)\n\n    def get_time_series(\n        self, prefix=None, description=None, include_metadata=False, asset_id=None, path=None, **kwargs\n    ) -> TimeSeriesListResponse:\n        \"\"\"Returns an object containing the requested timeseries.\n\n        Args:\n            prefix (str):           List timeseries with this prefix in the name.\n\n            description (str):      Filter timeseries taht contains this string in its description.\n\n            include_metadata (bool):    Decide if the metadata field should be returned or not. Defaults to False.\n\n            asset_id (int):        Get timeseries related to this asset.\n\n            path (List[int]):             Get timeseries under this asset path branch.\n\n        Keyword Arguments:\n            limit (int):            Number of results to return.\n\n            autopaging (bool):      Whether or not to automatically page through results. If set to true, limit will be\n                                    disregarded. Defaults to False.\n\n        Returns:\n            stable.time_series.TimeSeriesListResponse: A data object containing the requested timeseries with several getter methods with different\n            output formats.\n\n        Examples:\n            Get all time series for a given asset::\n\n                client = CogniteClient()\n                res = client.time_series.get_time_series(asset_id=123, autopaging=True)\n                print(res.to_pandas())\n        \"\"\"\n        autopaging = kwargs.get(\"autopaging\", False)\n        url = \"/timeseries\"\n        params = {\n            \"q\": prefix,\n            \"description\": description,\n            \"includeMetadata\": include_metadata,\n            \"assetId\": asset_id,\n            \"path\": str(path) if path else None,\n            \"limit\": kwargs.get(\"limit\", self._LIMIT) if not autopaging else self._LIMIT,\n        }\n\n        res = self._get(url=url, params=params, autopaging=autopaging)\n        return TimeSeriesListResponse(res.json())\n\n    def post_time_series(self, time_series: List[TimeSeries]) -> None:\n        \"\"\"Create a new time series.\n\n        Args:\n            time_series (list[stable.time_series.TimeSeries]):   List of time series data transfer objects to create.\n\n        Returns:\n            None\n\n        Examples:\n            Create a new time series::\n\n                from cognite.client.stable.time_series import TimeSeries\n                client = CogniteClient()\n\n                my_time_series = [TimeSeries(name=\"my_ts_1\")]\n\n                client.time_series.post_time_series(my_time_series)\n        \"\"\"\n        url = \"/timeseries\"\n        items = [ts.camel_case_dict() for ts in time_series]\n        body = {\"items\": items}\n        self._post(url, body=body)\n\n    def update_time_series(self, time_series: List[TimeSeries]) -> None:\n        \"\"\"Update an existing time series.\n\n        For each field that can be updated, a null value indicates that nothing should be done.\n\n        Args:\n            time_series (list[stable.time_series.TimeSeries]):   List of time series data transfer objects to update.\n\n        Returns:\n            None\n\n        Examples:\n            Update the unit of a time series::\n\n                from cognite.client.stable.time_series import TimeSeries\n                client = CogniteClient()\n\n                my_time_series = [TimeSeries(name=\"my_ts_1\", unit=\"celsius\")]\n\n                client.time_series.update_time_series(my_time_series)\n        \"\"\"\n        url = \"/timeseries\"\n        items = [ts.camel_case_dict() for ts in time_series]\n        body = {\"items\": items}\n        self._put(url, body=body)\n\n    def delete_time_series(self, name) -> None:\n        \"\"\"Delete a timeseries.\n\n        Args:\n            name (str):   Name of timeseries to delete.\n\n        Returns:\n            None\n\n        Examples:\n            Delete a time series by name::\n\n                client = CogniteClient()\n\n                client.time_series.delete_time_series(name=\"my_ts_1\")\n        \"\"\"\n        url = \"/timeseries/{}\".format(quote(name, safe=\"\"))\n        self._delete(url)\n", "path": "cognite/client/stable/time_series.py"}]}