We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Use cleaner solution for non-truncating division in python 2
Prefer `from __future__ import division` to `1.*x/y`

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_values_not_to_be_all_null.py`
Content:
```
1 from typing import Optional
2 
3 import numpy as np
4 
5 from great_expectations.core.expectation_configuration import ExpectationConfiguration
6 from great_expectations.execution_engine import PandasExecutionEngine
7 from great_expectations.expectations.expectation import MulticolumnMapExpectation
8 from great_expectations.expectations.metrics.map_metric_provider import (
9     MulticolumnMapMetricProvider,
10     multicolumn_condition_partial,
11 )
12 
13 
14 # This class defines a Metric to support your Expectation.
15 # For most MulticolumnMapExpectations, the main business logic for calculation will live in this class.
16 class MulticolumnValuesNotAllNull(MulticolumnMapMetricProvider):
17 
18     # This is the id string that will be used to reference your metric.
19 
20     condition_metric_name = "multicolumn_values.not_all_null"
21 
22     # These point your metric at the provided keys to facilitate calculation
23     condition_domain_keys = (
24         "batch_id",
25         "table",
26         "column_list",
27         "row_condition",
28         "condition_parser",
29         "ignore_row_if",
30     )
31     condition_value_keys = ()
32 
33     # This method implements the core logic for the PandasExecutionEngine
34 
35     @multicolumn_condition_partial(engine=PandasExecutionEngine)
36     def _pandas(cls, column_list, **kwargs):
37         row_wise_cond = column_list.isna().sum(axis=1) < len(column_list)
38         return row_wise_cond
39 
40     # This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine
41     # @multicolumn_condition_partial(engine=SqlAlchemyExecutionEngine)
42     # def _sqlalchemy(cls, column_list, **kwargs):
43     #     raise NotImplementedError
44 
45     # This method defines the business logic for evaluating your metric when using a SparkDFExecutionEngine
46     # @multicolumn_condition_partial(engine=SparkDFExecutionEngine)
47     # def _spark(cls, column_list, **kwargs):
48     #     raise NotImplementedError
49 
50 
51 # This class defines the Expectation itself
52 class ExpectMulticolumnValuesNotToBeAllNull(MulticolumnMapExpectation):
53     """Expect the certain set of columns not to be null at the same time."""
54 
55     # These examples will be shown in the public gallery.
56     # They will also be executed as unit tests for your Expectation.
57     examples = [
58         {
59             "data": {
60                 "no_nulls": [5, 6, 5, 12, -3],
61                 "some_nulls": [np.nan, -3, np.nan, np.nan, -9],
62                 "one_non_null": [np.nan, 2, np.nan, np.nan, np.nan],
63                 "all_nulls": [np.nan, np.nan, np.nan, np.nan, np.nan],
64             },
65             "tests": [
66                 {
67                     "title": "basic_positive_test",
68                     "exact_match_out": False,
69                     "include_in_gallery": True,
70                     "in": {"column_list": ["no_nulls", "some_nulls"]},
71                     "out": {
72                         "success": True,
73                     },
74                 },
75                 {
76                     "title": "basic_positive_test",
77                     "exact_match_out": False,
78                     "include_in_gallery": True,
79                     "in": {
80                         "column_list": ["some_nulls", "one_non_null"],
81                         "mostly": 0.4,
82                     },
83                     "out": {
84                         "success": True,
85                     },
86                 },
87                 {
88                     "title": "basic_negative_test",
89                     "exact_match_out": False,
90                     "include_in_gallery": True,
91                     "in": {
92                         "column_list": ["some_nulls", "one_non_null", "all_nulls"],
93                         "mostly": 1,
94                     },
95                     "out": {
96                         "success": False,
97                     },
98                 },
99             ],
100             "test_backends": [
101                 {
102                     "backend": "pandas",
103                     "dialects": None,
104                 },
105             ],
106         }
107     ]
108 
109     # This is the id string of the Metric used by this Expectation.
110     # For most Expectations, it will be the same as the `condition_metric_name` defined in your Metric class above.
111 
112     map_metric = "multicolumn_values.not_all_null"
113 
114     # This is a list of parameter names that can affect whether the Expectation evaluates to True or False
115     success_keys = (
116         "column_list",
117         "mostly",
118     )
119 
120     # This dictionary contains default values for any parameters that should have default values
121     default_kwarg_values = {}
122 
123     def validate_configuration(
124         self, configuration: Optional[ExpectationConfiguration] = None
125     ) -> None:
126         """
127         Validates that a configuration has been set, and sets a configuration if it has yet to be set. Ensures that
128         necessary configuration arguments have been provided for the validation of the expectation.
129 
130         Args:
131             configuration (OPTIONAL[ExpectationConfiguration]): \
132                 An optional Expectation Configuration entry that will be used to configure the expectation
133         Returns:
134             None. Raises InvalidExpectationConfigurationError if the config is not validated successfully
135         """
136 
137         super().validate_configuration(configuration)
138         configuration = configuration or self.configuration
139 
140         # # Check other things in configuration.kwargs and raise Exceptions if needed
141         # try:
142         #     assert (
143         #         ...
144         #     ), "message"
145         #     assert (
146         #         ...
147         #     ), "message"
148         # except AssertionError as e:
149         #     raise InvalidExpectationConfigurationError(str(e))
150 
151     # This object contains metadata for display in the public Gallery
152 
153     library_metadata = {
154         "tags": ["null_check"],  # Tags for this Expectation in the Gallery
155         "contributors": [  # Github handles for all contributors to this Expectation.
156             "@liyusa",  # Don't forget to add your github handle here!
157         ],
158     }
159 
160 
161 if __name__ == "__main__":
162 
163     ExpectMulticolumnValuesNotToBeAllNull().print_diagnostic_checklist()
164
```
--- 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/contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_values_not_to_be_all_null.py b/contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_values_not_to_be_all_null.py
--- a/contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_values_not_to_be_all_null.py
+++ b/contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_values_not_to_be_all_null.py
@@ -34,8 +34,7 @@
 
     @multicolumn_condition_partial(engine=PandasExecutionEngine)
     def _pandas(cls, column_list, **kwargs):
-        row_wise_cond = column_list.isna().sum(axis=1) < len(column_list)
-        return row_wise_cond
+        return column_list.notna().any(axis=1)
 
     # This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine
     # @multicolumn_condition_partial(engine=SqlAlchemyExecutionEngine)
@@ -118,7 +117,7 @@
     )
 
     # This dictionary contains default values for any parameters that should have default values
-    default_kwarg_values = {}
+    default_kwarg_values = {"ignore_row_if": "never"}
 
     def validate_configuration(
         self, configuration: Optional[ExpectationConfiguration] = None

{"golden_diff": "diff --git a/contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_values_not_to_be_all_null.py b/contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_values_not_to_be_all_null.py\n--- a/contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_values_not_to_be_all_null.py\n+++ b/contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_values_not_to_be_all_null.py\n@@ -34,8 +34,7 @@\n \n     @multicolumn_condition_partial(engine=PandasExecutionEngine)\n     def _pandas(cls, column_list, **kwargs):\n-        row_wise_cond = column_list.isna().sum(axis=1) < len(column_list)\n-        return row_wise_cond\n+        return column_list.notna().any(axis=1)\n \n     # This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine\n     # @multicolumn_condition_partial(engine=SqlAlchemyExecutionEngine)\n@@ -118,7 +117,7 @@\n     )\n \n     # This dictionary contains default values for any parameters that should have default values\n-    default_kwarg_values = {}\n+    default_kwarg_values = {\"ignore_row_if\": \"never\"}\n \n     def validate_configuration(\n         self, configuration: Optional[ExpectationConfiguration] = None\n", "issue": "Use cleaner solution for non-truncating division in python 2\nPrefer `from __future__ import division` to `1.*x/y`\n", "before_files": [{"content": "from typing import Optional\n\nimport numpy as np\n\nfrom great_expectations.core.expectation_configuration import ExpectationConfiguration\nfrom great_expectations.execution_engine import PandasExecutionEngine\nfrom great_expectations.expectations.expectation import MulticolumnMapExpectation\nfrom great_expectations.expectations.metrics.map_metric_provider import (\n    MulticolumnMapMetricProvider,\n    multicolumn_condition_partial,\n)\n\n\n# This class defines a Metric to support your Expectation.\n# For most MulticolumnMapExpectations, the main business logic for calculation will live in this class.\nclass MulticolumnValuesNotAllNull(MulticolumnMapMetricProvider):\n\n    # This is the id string that will be used to reference your metric.\n\n    condition_metric_name = \"multicolumn_values.not_all_null\"\n\n    # These point your metric at the provided keys to facilitate calculation\n    condition_domain_keys = (\n        \"batch_id\",\n        \"table\",\n        \"column_list\",\n        \"row_condition\",\n        \"condition_parser\",\n        \"ignore_row_if\",\n    )\n    condition_value_keys = ()\n\n    # This method implements the core logic for the PandasExecutionEngine\n\n    @multicolumn_condition_partial(engine=PandasExecutionEngine)\n    def _pandas(cls, column_list, **kwargs):\n        row_wise_cond = column_list.isna().sum(axis=1) < len(column_list)\n        return row_wise_cond\n\n    # This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine\n    # @multicolumn_condition_partial(engine=SqlAlchemyExecutionEngine)\n    # def _sqlalchemy(cls, column_list, **kwargs):\n    #     raise NotImplementedError\n\n    # This method defines the business logic for evaluating your metric when using a SparkDFExecutionEngine\n    # @multicolumn_condition_partial(engine=SparkDFExecutionEngine)\n    # def _spark(cls, column_list, **kwargs):\n    #     raise NotImplementedError\n\n\n# This class defines the Expectation itself\nclass ExpectMulticolumnValuesNotToBeAllNull(MulticolumnMapExpectation):\n    \"\"\"Expect the certain set of columns not to be null at the same time.\"\"\"\n\n    # These examples will be shown in the public gallery.\n    # They will also be executed as unit tests for your Expectation.\n    examples = [\n        {\n            \"data\": {\n                \"no_nulls\": [5, 6, 5, 12, -3],\n                \"some_nulls\": [np.nan, -3, np.nan, np.nan, -9],\n                \"one_non_null\": [np.nan, 2, np.nan, np.nan, np.nan],\n                \"all_nulls\": [np.nan, np.nan, np.nan, np.nan, np.nan],\n            },\n            \"tests\": [\n                {\n                    \"title\": \"basic_positive_test\",\n                    \"exact_match_out\": False,\n                    \"include_in_gallery\": True,\n                    \"in\": {\"column_list\": [\"no_nulls\", \"some_nulls\"]},\n                    \"out\": {\n                        \"success\": True,\n                    },\n                },\n                {\n                    \"title\": \"basic_positive_test\",\n                    \"exact_match_out\": False,\n                    \"include_in_gallery\": True,\n                    \"in\": {\n                        \"column_list\": [\"some_nulls\", \"one_non_null\"],\n                        \"mostly\": 0.4,\n                    },\n                    \"out\": {\n                        \"success\": True,\n                    },\n                },\n                {\n                    \"title\": \"basic_negative_test\",\n                    \"exact_match_out\": False,\n                    \"include_in_gallery\": True,\n                    \"in\": {\n                        \"column_list\": [\"some_nulls\", \"one_non_null\", \"all_nulls\"],\n                        \"mostly\": 1,\n                    },\n                    \"out\": {\n                        \"success\": False,\n                    },\n                },\n            ],\n            \"test_backends\": [\n                {\n                    \"backend\": \"pandas\",\n                    \"dialects\": None,\n                },\n            ],\n        }\n    ]\n\n    # This is the id string of the Metric used by this Expectation.\n    # For most Expectations, it will be the same as the `condition_metric_name` defined in your Metric class above.\n\n    map_metric = \"multicolumn_values.not_all_null\"\n\n    # This is a list of parameter names that can affect whether the Expectation evaluates to True or False\n    success_keys = (\n        \"column_list\",\n        \"mostly\",\n    )\n\n    # This dictionary contains default values for any parameters that should have default values\n    default_kwarg_values = {}\n\n    def validate_configuration(\n        self, configuration: Optional[ExpectationConfiguration] = None\n    ) -> None:\n        \"\"\"\n        Validates that a configuration has been set, and sets a configuration if it has yet to be set. Ensures that\n        necessary configuration arguments have been provided for the validation of the expectation.\n\n        Args:\n            configuration (OPTIONAL[ExpectationConfiguration]): \\\n                An optional Expectation Configuration entry that will be used to configure the expectation\n        Returns:\n            None. Raises InvalidExpectationConfigurationError if the config is not validated successfully\n        \"\"\"\n\n        super().validate_configuration(configuration)\n        configuration = configuration or self.configuration\n\n        # # Check other things in configuration.kwargs and raise Exceptions if needed\n        # try:\n        #     assert (\n        #         ...\n        #     ), \"message\"\n        #     assert (\n        #         ...\n        #     ), \"message\"\n        # except AssertionError as e:\n        #     raise InvalidExpectationConfigurationError(str(e))\n\n    # This object contains metadata for display in the public Gallery\n\n    library_metadata = {\n        \"tags\": [\"null_check\"],  # Tags for this Expectation in the Gallery\n        \"contributors\": [  # Github handles for all contributors to this Expectation.\n            \"@liyusa\",  # Don't forget to add your github handle here!\n        ],\n    }\n\n\nif __name__ == \"__main__\":\n\n    ExpectMulticolumnValuesNotToBeAllNull().print_diagnostic_checklist()\n", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_values_not_to_be_all_null.py"}], "after_files": [{"content": "from typing import Optional\n\nimport numpy as np\n\nfrom great_expectations.core.expectation_configuration import ExpectationConfiguration\nfrom great_expectations.execution_engine import PandasExecutionEngine\nfrom great_expectations.expectations.expectation import MulticolumnMapExpectation\nfrom great_expectations.expectations.metrics.map_metric_provider import (\n    MulticolumnMapMetricProvider,\n    multicolumn_condition_partial,\n)\n\n\n# This class defines a Metric to support your Expectation.\n# For most MulticolumnMapExpectations, the main business logic for calculation will live in this class.\nclass MulticolumnValuesNotAllNull(MulticolumnMapMetricProvider):\n\n    # This is the id string that will be used to reference your metric.\n\n    condition_metric_name = \"multicolumn_values.not_all_null\"\n\n    # These point your metric at the provided keys to facilitate calculation\n    condition_domain_keys = (\n        \"batch_id\",\n        \"table\",\n        \"column_list\",\n        \"row_condition\",\n        \"condition_parser\",\n        \"ignore_row_if\",\n    )\n    condition_value_keys = ()\n\n    # This method implements the core logic for the PandasExecutionEngine\n\n    @multicolumn_condition_partial(engine=PandasExecutionEngine)\n    def _pandas(cls, column_list, **kwargs):\n        return column_list.notna().any(axis=1)\n\n    # This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine\n    # @multicolumn_condition_partial(engine=SqlAlchemyExecutionEngine)\n    # def _sqlalchemy(cls, column_list, **kwargs):\n    #     raise NotImplementedError\n\n    # This method defines the business logic for evaluating your metric when using a SparkDFExecutionEngine\n    # @multicolumn_condition_partial(engine=SparkDFExecutionEngine)\n    # def _spark(cls, column_list, **kwargs):\n    #     raise NotImplementedError\n\n\n# This class defines the Expectation itself\nclass ExpectMulticolumnValuesNotToBeAllNull(MulticolumnMapExpectation):\n    \"\"\"Expect the certain set of columns not to be null at the same time.\"\"\"\n\n    # These examples will be shown in the public gallery.\n    # They will also be executed as unit tests for your Expectation.\n    examples = [\n        {\n            \"data\": {\n                \"no_nulls\": [5, 6, 5, 12, -3],\n                \"some_nulls\": [np.nan, -3, np.nan, np.nan, -9],\n                \"one_non_null\": [np.nan, 2, np.nan, np.nan, np.nan],\n                \"all_nulls\": [np.nan, np.nan, np.nan, np.nan, np.nan],\n            },\n            \"tests\": [\n                {\n                    \"title\": \"basic_positive_test\",\n                    \"exact_match_out\": False,\n                    \"include_in_gallery\": True,\n                    \"in\": {\"column_list\": [\"no_nulls\", \"some_nulls\"]},\n                    \"out\": {\n                        \"success\": True,\n                    },\n                },\n                {\n                    \"title\": \"basic_positive_test\",\n                    \"exact_match_out\": False,\n                    \"include_in_gallery\": True,\n                    \"in\": {\n                        \"column_list\": [\"some_nulls\", \"one_non_null\"],\n                        \"mostly\": 0.4,\n                    },\n                    \"out\": {\n                        \"success\": True,\n                    },\n                },\n                {\n                    \"title\": \"basic_negative_test\",\n                    \"exact_match_out\": False,\n                    \"include_in_gallery\": True,\n                    \"in\": {\n                        \"column_list\": [\"some_nulls\", \"one_non_null\", \"all_nulls\"],\n                        \"mostly\": 1,\n                    },\n                    \"out\": {\n                        \"success\": False,\n                    },\n                },\n            ],\n            \"test_backends\": [\n                {\n                    \"backend\": \"pandas\",\n                    \"dialects\": None,\n                },\n            ],\n        }\n    ]\n\n    # This is the id string of the Metric used by this Expectation.\n    # For most Expectations, it will be the same as the `condition_metric_name` defined in your Metric class above.\n\n    map_metric = \"multicolumn_values.not_all_null\"\n\n    # This is a list of parameter names that can affect whether the Expectation evaluates to True or False\n    success_keys = (\n        \"column_list\",\n        \"mostly\",\n    )\n\n    # This dictionary contains default values for any parameters that should have default values\n    default_kwarg_values = {\"ignore_row_if\": \"never\"}\n\n    def validate_configuration(\n        self, configuration: Optional[ExpectationConfiguration] = None\n    ) -> None:\n        \"\"\"\n        Validates that a configuration has been set, and sets a configuration if it has yet to be set. Ensures that\n        necessary configuration arguments have been provided for the validation of the expectation.\n\n        Args:\n            configuration (OPTIONAL[ExpectationConfiguration]): \\\n                An optional Expectation Configuration entry that will be used to configure the expectation\n        Returns:\n            None. Raises InvalidExpectationConfigurationError if the config is not validated successfully\n        \"\"\"\n\n        super().validate_configuration(configuration)\n        configuration = configuration or self.configuration\n\n        # # Check other things in configuration.kwargs and raise Exceptions if needed\n        # try:\n        #     assert (\n        #         ...\n        #     ), \"message\"\n        #     assert (\n        #         ...\n        #     ), \"message\"\n        # except AssertionError as e:\n        #     raise InvalidExpectationConfigurationError(str(e))\n\n    # This object contains metadata for display in the public Gallery\n\n    library_metadata = {\n        \"tags\": [\"null_check\"],  # Tags for this Expectation in the Gallery\n        \"contributors\": [  # Github handles for all contributors to this Expectation.\n            \"@liyusa\",  # Don't forget to add your github handle here!\n        ],\n    }\n\n\nif __name__ == \"__main__\":\n\n    ExpectMulticolumnValuesNotToBeAllNull().print_diagnostic_checklist()\n", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_values_not_to_be_all_null.py"}]}