We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[bigquery] Delegate parameters handling to the library
See https://github.com/ToucanToco/toucan-connectors/pull/594#discussion_r870425994

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `toucan_connectors/google_big_query/google_big_query_connector.py`
Content:
```
1 import logging
2 from enum import Enum
3 from timeit import default_timer as timer
4 from typing import Any, Dict, List, Optional, Union
5 
6 import pandas
7 import pandas as pd
8 from google.cloud import bigquery
9 from google.oauth2.service_account import Credentials
10 from pydantic import Field
11 
12 from toucan_connectors.google_credentials import GoogleCredentials, get_google_oauth2_credentials
13 from toucan_connectors.toucan_connector import ToucanConnector, ToucanDataSource
14 
15 
16 class Dialect(str, Enum):
17     legacy = 'legacy'
18     standard = 'standard'
19 
20 
21 class GoogleBigQueryDataSource(ToucanDataSource):
22     query: str = Field(
23         ...,
24         description='You can find details on the query syntax '
25         '<a href="https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax">here</a>',
26         widget='sql',
27     )
28 
29 
30 BigQueryParam = Union[bigquery.ScalarQueryParameter, bigquery.ArrayQueryParameter]
31 
32 
33 # NOTE: This does not play nicely with dates. They're a bit tricky
34 # though, as we'd have to try and parse dates from strings to
35 # determine if something is a date or not. Until then, we can just
36 # use a cast. eg: SELECT * FROM table WHERE STRING(date_col) IN UNNEST({{my_dates}})
37 def _define_scalar_type(value: Any) -> str:
38     if isinstance(value, bool):
39         return 'BOOL'
40     elif isinstance(value, int):
41         return 'NUMERIC'
42     elif isinstance(value, float):
43         return 'FLOAT64'
44     elif isinstance(value, str):
45         return 'STRING'
46     # TODO - check bad return type
47     return 'STRING'
48 
49 
50 def _define_array_type(name: str, values: List[Any]) -> BigQueryParam:
51     return bigquery.ArrayQueryParameter(
52         name, _define_scalar_type(values[0] if len(values) > 0 else ''), values
53     )
54 
55 
56 def _define_query_param(name: str, value: Any) -> BigQueryParam:
57     if isinstance(value, list):
58         return _define_array_type(name, value)
59     else:
60         return bigquery.ScalarQueryParameter(name, _define_scalar_type(value), value)
61 
62 
63 class GoogleBigQueryConnector(ToucanConnector):
64     data_source_model: GoogleBigQueryDataSource
65 
66     credentials: GoogleCredentials = Field(
67         ...,
68         title='Google Credentials',
69         description='For authentication, download an authentication file from your '
70         '<a href="https://console.developers.google.com/apis/credentials" target="_blank">Google Console</a> and '
71         'use the values here. This is an oauth2 credential file. For more information see this '
72         '<a href="https://gspread.readthedocs.io/en/latest/oauth2.html" target="_blank" >documentation</a>. '
73         'You should use "service_account" credentials, which is the preferred type of credentials '
74         'to use when authenticating on behalf of a service or application',
75     )
76     dialect: Dialect = Field(
77         Dialect.standard,
78         description='BigQuery allows you to choose between standard and legacy SQL as query syntax. '
79         'The preferred query syntax is the default standard SQL. You can find more information on this '
80         '<a href="https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax" target="_blank" >documentation</a>',
81     )
82     scopes: List[str] = Field(
83         ['https://www.googleapis.com/auth/bigquery'],
84         title='OAuth scopes',
85         description='OAuth 2.0 scopes define the level of access you need to request '
86         'the Google APIs. For more information, see this '
87         '<a href="https://developers.google.com/identity/protocols/googlescopes" target="_blank" >documentation</a>',
88     )
89 
90     @staticmethod
91     def _get_google_credentials(credentials: GoogleCredentials, scopes: List[str]) -> Credentials:
92         credentials = get_google_oauth2_credentials(credentials).with_scopes(scopes)
93         return credentials
94 
95     @staticmethod
96     def _connect(credentials: Credentials) -> bigquery.Client:
97         start = timer()
98         client = bigquery.Client(credentials=credentials)
99         end = timer()
100         logging.getLogger(__name__).info(
101             f'[benchmark][google_big_query] - connect {end - start} seconds',
102             extra={
103                 'benchmark': {
104                     'operation': 'connect',
105                     'execution_time': end - start,
106                     'connector': 'google_big_query',
107                 }
108             },
109         )
110         return client
111 
112     @staticmethod
113     def _execute_query(client: bigquery.Client, query: str, parameters: List) -> pandas.DataFrame:
114         try:
115             start = timer()
116             result = (
117                 client.query(query, job_config=bigquery.QueryJobConfig(query_parameters=parameters))
118                 .result()
119                 .to_dataframe(
120                     create_bqstorage_client=True,
121                 )  # Use to generate directly a dataframe pandas
122             )
123             end = timer()
124             logging.getLogger(__name__).info(
125                 f'[benchmark][google_big_query] - execute {end - start} seconds',
126                 extra={
127                     'benchmark': {
128                         'operation': 'execute',
129                         'execution_time': end - start,
130                         'connector': 'google_big_query',
131                     }
132                 },
133             )
134             return result
135         except TypeError as e:
136             logging.getLogger(__name__).error(f'Error to execute request {query} - {e}')
137             raise e
138 
139     @staticmethod
140     def _prepare_parameters(query: str, parameters: Optional[Dict]) -> List:
141         """replace ToucanToco variable definitions by Google Big Query variable definition"""
142         query_parameters = []
143         for param_name, param_value in (parameters or {}).items():
144             if query.find('@' + param_name) > -1:
145                 # set all parameters with a type defined and necessary for Big Query
146                 query_parameters.append(_define_query_param(param_name, param_value))
147         return query_parameters
148 
149     @staticmethod
150     def _prepare_query(query: str) -> str:
151         """replace ToucanToco variable definition by Google Big Query variable definition"""
152         new_query = query.replace('{{', '@').replace('}}', '')
153         return new_query
154 
155     def _retrieve_data(self, data_source: GoogleBigQueryDataSource) -> pd.DataFrame:
156         logging.getLogger(__name__).debug(
157             f'Play request {data_source.query} with parameters {data_source.parameters}'
158         )
159 
160         credentials = GoogleBigQueryConnector._get_google_credentials(self.credentials, self.scopes)
161         query = GoogleBigQueryConnector._prepare_query(data_source.query)
162         parameters = GoogleBigQueryConnector._prepare_parameters(query, data_source.parameters)
163 
164         client = GoogleBigQueryConnector._connect(credentials)
165         result = GoogleBigQueryConnector._execute_query(client, query, parameters)
166 
167         return result
168
```
--- 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/toucan_connectors/google_big_query/google_big_query_connector.py b/toucan_connectors/google_big_query/google_big_query_connector.py
--- a/toucan_connectors/google_big_query/google_big_query_connector.py
+++ b/toucan_connectors/google_big_query/google_big_query_connector.py
@@ -6,6 +6,7 @@
 import pandas
 import pandas as pd
 from google.cloud import bigquery
+from google.cloud.bigquery.dbapi import _helpers as bigquery_helpers
 from google.oauth2.service_account import Credentials
 from pydantic import Field
 
@@ -30,34 +31,16 @@
 BigQueryParam = Union[bigquery.ScalarQueryParameter, bigquery.ArrayQueryParameter]
 
 
-# NOTE: This does not play nicely with dates. They're a bit tricky
-# though, as we'd have to try and parse dates from strings to
-# determine if something is a date or not. Until then, we can just
-# use a cast. eg: SELECT * FROM table WHERE STRING(date_col) IN UNNEST({{my_dates}})
-def _define_scalar_type(value: Any) -> str:
-    if isinstance(value, bool):
-        return 'BOOL'
-    elif isinstance(value, int):
-        return 'NUMERIC'
-    elif isinstance(value, float):
-        return 'FLOAT64'
-    elif isinstance(value, str):
-        return 'STRING'
-    # TODO - check bad return type
-    return 'STRING'
-
-
-def _define_array_type(name: str, values: List[Any]) -> BigQueryParam:
-    return bigquery.ArrayQueryParameter(
-        name, _define_scalar_type(values[0] if len(values) > 0 else ''), values
-    )
-
-
 def _define_query_param(name: str, value: Any) -> BigQueryParam:
     if isinstance(value, list):
-        return _define_array_type(name, value)
+        return (
+            bigquery_helpers.array_to_query_parameter(value=value, name=name)
+            if len(value) > 0
+            # array_to_query_parameter raises an exception in case of an empty list
+            else bigquery.ArrayQueryParameter(name=name, array_type='STRING', values=value)
+        )
     else:
-        return bigquery.ScalarQueryParameter(name, _define_scalar_type(value), value)
+        return bigquery_helpers.scalar_to_query_parameter(value=value, name=name)
 
 
 class GoogleBigQueryConnector(ToucanConnector):

{"golden_diff": "diff --git a/toucan_connectors/google_big_query/google_big_query_connector.py b/toucan_connectors/google_big_query/google_big_query_connector.py\n--- a/toucan_connectors/google_big_query/google_big_query_connector.py\n+++ b/toucan_connectors/google_big_query/google_big_query_connector.py\n@@ -6,6 +6,7 @@\n import pandas\n import pandas as pd\n from google.cloud import bigquery\n+from google.cloud.bigquery.dbapi import _helpers as bigquery_helpers\n from google.oauth2.service_account import Credentials\n from pydantic import Field\n \n@@ -30,34 +31,16 @@\n BigQueryParam = Union[bigquery.ScalarQueryParameter, bigquery.ArrayQueryParameter]\n \n \n-# NOTE: This does not play nicely with dates. They're a bit tricky\n-# though, as we'd have to try and parse dates from strings to\n-# determine if something is a date or not. Until then, we can just\n-# use a cast. eg: SELECT * FROM table WHERE STRING(date_col) IN UNNEST({{my_dates}})\n-def _define_scalar_type(value: Any) -> str:\n-    if isinstance(value, bool):\n-        return 'BOOL'\n-    elif isinstance(value, int):\n-        return 'NUMERIC'\n-    elif isinstance(value, float):\n-        return 'FLOAT64'\n-    elif isinstance(value, str):\n-        return 'STRING'\n-    # TODO - check bad return type\n-    return 'STRING'\n-\n-\n-def _define_array_type(name: str, values: List[Any]) -> BigQueryParam:\n-    return bigquery.ArrayQueryParameter(\n-        name, _define_scalar_type(values[0] if len(values) > 0 else ''), values\n-    )\n-\n-\n def _define_query_param(name: str, value: Any) -> BigQueryParam:\n     if isinstance(value, list):\n-        return _define_array_type(name, value)\n+        return (\n+            bigquery_helpers.array_to_query_parameter(value=value, name=name)\n+            if len(value) > 0\n+            # array_to_query_parameter raises an exception in case of an empty list\n+            else bigquery.ArrayQueryParameter(name=name, array_type='STRING', values=value)\n+        )\n     else:\n-        return bigquery.ScalarQueryParameter(name, _define_scalar_type(value), value)\n+        return bigquery_helpers.scalar_to_query_parameter(value=value, name=name)\n \n \n class GoogleBigQueryConnector(ToucanConnector):\n", "issue": "[bigquery] Delegate parameters handling to the library\nSee https://github.com/ToucanToco/toucan-connectors/pull/594#discussion_r870425994\n", "before_files": [{"content": "import logging\nfrom enum import Enum\nfrom timeit import default_timer as timer\nfrom typing import Any, Dict, List, Optional, Union\n\nimport pandas\nimport pandas as pd\nfrom google.cloud import bigquery\nfrom google.oauth2.service_account import Credentials\nfrom pydantic import Field\n\nfrom toucan_connectors.google_credentials import GoogleCredentials, get_google_oauth2_credentials\nfrom toucan_connectors.toucan_connector import ToucanConnector, ToucanDataSource\n\n\nclass Dialect(str, Enum):\n    legacy = 'legacy'\n    standard = 'standard'\n\n\nclass GoogleBigQueryDataSource(ToucanDataSource):\n    query: str = Field(\n        ...,\n        description='You can find details on the query syntax '\n        '<a href=\"https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax\">here</a>',\n        widget='sql',\n    )\n\n\nBigQueryParam = Union[bigquery.ScalarQueryParameter, bigquery.ArrayQueryParameter]\n\n\n# NOTE: This does not play nicely with dates. They're a bit tricky\n# though, as we'd have to try and parse dates from strings to\n# determine if something is a date or not. Until then, we can just\n# use a cast. eg: SELECT * FROM table WHERE STRING(date_col) IN UNNEST({{my_dates}})\ndef _define_scalar_type(value: Any) -> str:\n    if isinstance(value, bool):\n        return 'BOOL'\n    elif isinstance(value, int):\n        return 'NUMERIC'\n    elif isinstance(value, float):\n        return 'FLOAT64'\n    elif isinstance(value, str):\n        return 'STRING'\n    # TODO - check bad return type\n    return 'STRING'\n\n\ndef _define_array_type(name: str, values: List[Any]) -> BigQueryParam:\n    return bigquery.ArrayQueryParameter(\n        name, _define_scalar_type(values[0] if len(values) > 0 else ''), values\n    )\n\n\ndef _define_query_param(name: str, value: Any) -> BigQueryParam:\n    if isinstance(value, list):\n        return _define_array_type(name, value)\n    else:\n        return bigquery.ScalarQueryParameter(name, _define_scalar_type(value), value)\n\n\nclass GoogleBigQueryConnector(ToucanConnector):\n    data_source_model: GoogleBigQueryDataSource\n\n    credentials: GoogleCredentials = Field(\n        ...,\n        title='Google Credentials',\n        description='For authentication, download an authentication file from your '\n        '<a href=\"https://console.developers.google.com/apis/credentials\" target=\"_blank\">Google Console</a> and '\n        'use the values here. This is an oauth2 credential file. For more information see this '\n        '<a href=\"https://gspread.readthedocs.io/en/latest/oauth2.html\" target=\"_blank\" >documentation</a>. '\n        'You should use \"service_account\" credentials, which is the preferred type of credentials '\n        'to use when authenticating on behalf of a service or application',\n    )\n    dialect: Dialect = Field(\n        Dialect.standard,\n        description='BigQuery allows you to choose between standard and legacy SQL as query syntax. '\n        'The preferred query syntax is the default standard SQL. You can find more information on this '\n        '<a href=\"https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax\" target=\"_blank\" >documentation</a>',\n    )\n    scopes: List[str] = Field(\n        ['https://www.googleapis.com/auth/bigquery'],\n        title='OAuth scopes',\n        description='OAuth 2.0 scopes define the level of access you need to request '\n        'the Google APIs. For more information, see this '\n        '<a href=\"https://developers.google.com/identity/protocols/googlescopes\" target=\"_blank\" >documentation</a>',\n    )\n\n    @staticmethod\n    def _get_google_credentials(credentials: GoogleCredentials, scopes: List[str]) -> Credentials:\n        credentials = get_google_oauth2_credentials(credentials).with_scopes(scopes)\n        return credentials\n\n    @staticmethod\n    def _connect(credentials: Credentials) -> bigquery.Client:\n        start = timer()\n        client = bigquery.Client(credentials=credentials)\n        end = timer()\n        logging.getLogger(__name__).info(\n            f'[benchmark][google_big_query] - connect {end - start} seconds',\n            extra={\n                'benchmark': {\n                    'operation': 'connect',\n                    'execution_time': end - start,\n                    'connector': 'google_big_query',\n                }\n            },\n        )\n        return client\n\n    @staticmethod\n    def _execute_query(client: bigquery.Client, query: str, parameters: List) -> pandas.DataFrame:\n        try:\n            start = timer()\n            result = (\n                client.query(query, job_config=bigquery.QueryJobConfig(query_parameters=parameters))\n                .result()\n                .to_dataframe(\n                    create_bqstorage_client=True,\n                )  # Use to generate directly a dataframe pandas\n            )\n            end = timer()\n            logging.getLogger(__name__).info(\n                f'[benchmark][google_big_query] - execute {end - start} seconds',\n                extra={\n                    'benchmark': {\n                        'operation': 'execute',\n                        'execution_time': end - start,\n                        'connector': 'google_big_query',\n                    }\n                },\n            )\n            return result\n        except TypeError as e:\n            logging.getLogger(__name__).error(f'Error to execute request {query} - {e}')\n            raise e\n\n    @staticmethod\n    def _prepare_parameters(query: str, parameters: Optional[Dict]) -> List:\n        \"\"\"replace ToucanToco variable definitions by Google Big Query variable definition\"\"\"\n        query_parameters = []\n        for param_name, param_value in (parameters or {}).items():\n            if query.find('@' + param_name) > -1:\n                # set all parameters with a type defined and necessary for Big Query\n                query_parameters.append(_define_query_param(param_name, param_value))\n        return query_parameters\n\n    @staticmethod\n    def _prepare_query(query: str) -> str:\n        \"\"\"replace ToucanToco variable definition by Google Big Query variable definition\"\"\"\n        new_query = query.replace('{{', '@').replace('}}', '')\n        return new_query\n\n    def _retrieve_data(self, data_source: GoogleBigQueryDataSource) -> pd.DataFrame:\n        logging.getLogger(__name__).debug(\n            f'Play request {data_source.query} with parameters {data_source.parameters}'\n        )\n\n        credentials = GoogleBigQueryConnector._get_google_credentials(self.credentials, self.scopes)\n        query = GoogleBigQueryConnector._prepare_query(data_source.query)\n        parameters = GoogleBigQueryConnector._prepare_parameters(query, data_source.parameters)\n\n        client = GoogleBigQueryConnector._connect(credentials)\n        result = GoogleBigQueryConnector._execute_query(client, query, parameters)\n\n        return result\n", "path": "toucan_connectors/google_big_query/google_big_query_connector.py"}], "after_files": [{"content": "import logging\nfrom enum import Enum\nfrom timeit import default_timer as timer\nfrom typing import Any, Dict, List, Optional, Union\n\nimport pandas\nimport pandas as pd\nfrom google.cloud import bigquery\nfrom google.cloud.bigquery.dbapi import _helpers as bigquery_helpers\nfrom google.oauth2.service_account import Credentials\nfrom pydantic import Field\n\nfrom toucan_connectors.google_credentials import GoogleCredentials, get_google_oauth2_credentials\nfrom toucan_connectors.toucan_connector import ToucanConnector, ToucanDataSource\n\n\nclass Dialect(str, Enum):\n    legacy = 'legacy'\n    standard = 'standard'\n\n\nclass GoogleBigQueryDataSource(ToucanDataSource):\n    query: str = Field(\n        ...,\n        description='You can find details on the query syntax '\n        '<a href=\"https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax\">here</a>',\n        widget='sql',\n    )\n\n\nBigQueryParam = Union[bigquery.ScalarQueryParameter, bigquery.ArrayQueryParameter]\n\n\ndef _define_query_param(name: str, value: Any) -> BigQueryParam:\n    if isinstance(value, list):\n        return (\n            bigquery_helpers.array_to_query_parameter(value=value, name=name)\n            if len(value) > 0\n            # array_to_query_parameter raises an exception in case of an empty list\n            else bigquery.ArrayQueryParameter(name=name, array_type='STRING', values=value)\n        )\n    else:\n        return bigquery_helpers.scalar_to_query_parameter(value=value, name=name)\n\n\nclass GoogleBigQueryConnector(ToucanConnector):\n    data_source_model: GoogleBigQueryDataSource\n\n    credentials: GoogleCredentials = Field(\n        ...,\n        title='Google Credentials',\n        description='For authentication, download an authentication file from your '\n        '<a href=\"https://console.developers.google.com/apis/credentials\" target=\"_blank\">Google Console</a> and '\n        'use the values here. This is an oauth2 credential file. For more information see this '\n        '<a href=\"https://gspread.readthedocs.io/en/latest/oauth2.html\" target=\"_blank\" >documentation</a>. '\n        'You should use \"service_account\" credentials, which is the preferred type of credentials '\n        'to use when authenticating on behalf of a service or application',\n    )\n    dialect: Dialect = Field(\n        Dialect.standard,\n        description='BigQuery allows you to choose between standard and legacy SQL as query syntax. '\n        'The preferred query syntax is the default standard SQL. You can find more information on this '\n        '<a href=\"https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax\" target=\"_blank\" >documentation</a>',\n    )\n    scopes: List[str] = Field(\n        ['https://www.googleapis.com/auth/bigquery'],\n        title='OAuth scopes',\n        description='OAuth 2.0 scopes define the level of access you need to request '\n        'the Google APIs. For more information, see this '\n        '<a href=\"https://developers.google.com/identity/protocols/googlescopes\" target=\"_blank\" >documentation</a>',\n    )\n\n    @staticmethod\n    def _get_google_credentials(credentials: GoogleCredentials, scopes: List[str]) -> Credentials:\n        credentials = get_google_oauth2_credentials(credentials).with_scopes(scopes)\n        return credentials\n\n    @staticmethod\n    def _connect(credentials: Credentials) -> bigquery.Client:\n        start = timer()\n        client = bigquery.Client(credentials=credentials)\n        end = timer()\n        logging.getLogger(__name__).info(\n            f'[benchmark][google_big_query] - connect {end - start} seconds',\n            extra={\n                'benchmark': {\n                    'operation': 'connect',\n                    'execution_time': end - start,\n                    'connector': 'google_big_query',\n                }\n            },\n        )\n        return client\n\n    @staticmethod\n    def _execute_query(client: bigquery.Client, query: str, parameters: List) -> pandas.DataFrame:\n        try:\n            start = timer()\n            result = (\n                client.query(query, job_config=bigquery.QueryJobConfig(query_parameters=parameters))\n                .result()\n                .to_dataframe(\n                    create_bqstorage_client=True,\n                )  # Use to generate directly a dataframe pandas\n            )\n            end = timer()\n            logging.getLogger(__name__).info(\n                f'[benchmark][google_big_query] - execute {end - start} seconds',\n                extra={\n                    'benchmark': {\n                        'operation': 'execute',\n                        'execution_time': end - start,\n                        'connector': 'google_big_query',\n                    }\n                },\n            )\n            return result\n        except TypeError as e:\n            logging.getLogger(__name__).error(f'Error to execute request {query} - {e}')\n            raise e\n\n    @staticmethod\n    def _prepare_parameters(query: str, parameters: Optional[Dict]) -> List:\n        \"\"\"replace ToucanToco variable definitions by Google Big Query variable definition\"\"\"\n        query_parameters = []\n        for param_name, param_value in (parameters or {}).items():\n            if query.find('@' + param_name) > -1:\n                # set all parameters with a type defined and necessary for Big Query\n                query_parameters.append(_define_query_param(param_name, param_value))\n        return query_parameters\n\n    @staticmethod\n    def _prepare_query(query: str) -> str:\n        \"\"\"replace ToucanToco variable definition by Google Big Query variable definition\"\"\"\n        new_query = query.replace('{{', '@').replace('}}', '')\n        return new_query\n\n    def _retrieve_data(self, data_source: GoogleBigQueryDataSource) -> pd.DataFrame:\n        logging.getLogger(__name__).debug(\n            f'Play request {data_source.query} with parameters {data_source.parameters}'\n        )\n\n        credentials = GoogleBigQueryConnector._get_google_credentials(self.credentials, self.scopes)\n        query = GoogleBigQueryConnector._prepare_query(data_source.query)\n        parameters = GoogleBigQueryConnector._prepare_parameters(query, data_source.parameters)\n\n        client = GoogleBigQueryConnector._connect(credentials)\n        result = GoogleBigQueryConnector._execute_query(client, query, parameters)\n\n        return result\n", "path": "toucan_connectors/google_big_query/google_big_query_connector.py"}]}