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: `examples/expectations/column_aggregate_expectation_template.py`
Content:
```
1 """
2 This is a template for creating custom ColumnExpectations.
3 For detailed instructions on how to use it, please see:
4     https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations
5 """
6 
7 from typing import Dict
8 
9 from great_expectations.core import ExpectationConfiguration
10 from great_expectations.execution_engine import (
11     ExecutionEngine,
12     PandasExecutionEngine,
13     SparkDFExecutionEngine,
14     SqlAlchemyExecutionEngine,
15 )
16 from great_expectations.expectations.expectation import ColumnExpectation
17 from great_expectations.expectations.metrics import (
18     ColumnAggregateMetricProvider,
19     column_aggregate_partial,
20     column_aggregate_value,
21 )
22 
23 
24 # This class defines a Metric to support your Expectation.
25 # For most ColumnExpectations, the main business logic for calculation will live in this class.
26 class ColumnAggregateMatchesSomeCriteria(ColumnAggregateMetricProvider):
27 
28     # This is the id string that will be used to reference your Metric.
29     metric_name = "METRIC NAME GOES HERE"
30 
31     # This method implements the core logic for the PandasExecutionEngine
32     @column_aggregate_value(engine=PandasExecutionEngine)
33     def _pandas(cls, column, **kwargs):
34         raise NotImplementedError
35 
36     # This method defines the business logic for evaluating your Metric when using a SqlAlchemyExecutionEngine
37     @column_aggregate_partial(engine=SqlAlchemyExecutionEngine)
38     def _sqlalchemy(cls, column, _dialect, **kwargs):
39         raise NotImplementedError
40 
41     # This method defines the business logic for evaluating your Metric when using a SparkDFExecutionEngine
42     @column_aggregate_partial(engine=SparkDFExecutionEngine)
43     def _spark(cls, column, **kwargs):
44         raise NotImplementedError
45 
46 
47 # This class defines the Expectation itself
48 class ExpectColumnAggregateToMatchSomeCriteria(ColumnExpectation):
49     """TODO: add a docstring here"""
50 
51     # These examples will be shown in the public gallery.
52     # They will also be executed as unit tests for your Expectation.
53     examples = []
54 
55     # This is a tuple consisting of all Metrics necessary to evaluate the Expectation.
56     metric_dependencies = ("METRIC NAME GOES HERE",)
57 
58     # This a tuple of parameter names that can affect whether the Expectation evaluates to True or False.
59     success_keys = ("min_value", "strict_min", "max_value", "strict_max")
60 
61     # This dictionary contains default values for any parameters that should have default values.
62     default_kwarg_values = {}
63 
64     # This method performs a validation of your metrics against your success keys, returning a dict indicating the success or failure of the Expectation.
65     def _validate(
66         self,
67         configuration: ExpectationConfiguration,
68         metrics: Dict,
69         runtime_configuration: dict = None,
70         execution_engine: ExecutionEngine = None,
71     ):
72         raise NotImplementedError
73 
74     # This object contains metadata for display in the public Gallery
75     library_metadata = {
76         "tags": [],  # Tags for this Expectation in the Gallery
77         "contributors": [  # Github handles for all contributors to this Expectation.
78             "@your_name_here",  # Don't forget to add your github handle here!
79         ],
80     }
81 
82 
83 if __name__ == "__main__":
84     ExpectColumnAggregateToMatchSomeCriteria().print_diagnostic_checklist()
85
```

Path: `examples/expectations/column_map_expectation_template.py`
Content:
```
1 """
2 This is a template for creating custom ColumnMapExpectations.
3 For detailed instructions on how to use it, please see:
4     https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations
5 """
6 
7 import json
8 
9 from great_expectations.execution_engine import (
10     PandasExecutionEngine,
11     SparkDFExecutionEngine,
12     SqlAlchemyExecutionEngine,
13 )
14 from great_expectations.expectations.expectation import ColumnMapExpectation
15 from great_expectations.expectations.metrics import (
16     ColumnMapMetricProvider,
17     column_condition_partial,
18 )
19 
20 
21 # This class defines a Metric to support your Expectation.
22 # For most ColumnMapExpectations, the main business logic for calculation will live in this class.
23 class ColumnValuesMatchSomeCriteria(ColumnMapMetricProvider):
24 
25     # This is the id string that will be used to reference your metric.
26     condition_metric_name = "METRIC NAME GOES HERE"
27 
28     # This method implements the core logic for the PandasExecutionEngine
29     @column_condition_partial(engine=PandasExecutionEngine)
30     def _pandas(cls, column, **kwargs):
31         raise NotImplementedError
32 
33     # This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine
34     @column_condition_partial(engine=SqlAlchemyExecutionEngine)
35     def _sqlalchemy(cls, column, _dialect, **kwargs):
36         raise NotImplementedError
37 
38     # This method defines the business logic for evaluating your metric when using a SparkDFExecutionEngine
39     @column_condition_partial(engine=SparkDFExecutionEngine)
40     def _spark(cls, column, **kwargs):
41         raise NotImplementedError
42 
43 
44 # This class defines the Expectation itself
45 class ExpectColumnValuesToMatchSomeCriteria(ColumnMapExpectation):
46     """TODO: Add a docstring here"""
47 
48     # These examples will be shown in the public gallery.
49     # They will also be executed as unit tests for your Expectation.
50     examples = []
51 
52     # This is the id string of the Metric used by this Expectation.
53     # For most Expectations, it will be the same as the `condition_metric_name` defined in your Metric class above.
54     map_metric = "METRIC NAME GOES HERE"
55 
56     # This is a list of parameter names that can affect whether the Expectation evaluates to True or False
57     success_keys = ("mostly",)
58 
59     # This dictionary contains default values for any parameters that should have default values
60     default_kwarg_values = {}
61 
62     # This object contains metadata for display in the public Gallery
63     library_metadata = {
64         "tags": [],  # Tags for this Expectation in the Gallery
65         "contributors": [  # Github handles for all contributors to this Expectation.
66             "@your_name_here",  # Don't forget to add your github handle here!
67         ],
68     }
69 
70 
71 if __name__ == "__main__":
72     ExpectColumnValuesToMatchSomeCriteria().print_diagnostic_checklist()
73
```
--- 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/examples/expectations/column_aggregate_expectation_template.py b/examples/expectations/column_aggregate_expectation_template.py
--- a/examples/expectations/column_aggregate_expectation_template.py
+++ b/examples/expectations/column_aggregate_expectation_template.py
@@ -34,14 +34,14 @@
         raise NotImplementedError
 
     # This method defines the business logic for evaluating your Metric when using a SqlAlchemyExecutionEngine
-    @column_aggregate_partial(engine=SqlAlchemyExecutionEngine)
-    def _sqlalchemy(cls, column, _dialect, **kwargs):
-        raise NotImplementedError
-
+    # @column_aggregate_partial(engine=SqlAlchemyExecutionEngine)
+    # def _sqlalchemy(cls, column, _dialect, **kwargs):
+    #     raise NotImplementedError
+    #
     # This method defines the business logic for evaluating your Metric when using a SparkDFExecutionEngine
-    @column_aggregate_partial(engine=SparkDFExecutionEngine)
-    def _spark(cls, column, **kwargs):
-        raise NotImplementedError
+    # @column_aggregate_partial(engine=SparkDFExecutionEngine)
+    # def _spark(cls, column, **kwargs):
+    #     raise NotImplementedError
 
 
 # This class defines the Expectation itself
diff --git a/examples/expectations/column_map_expectation_template.py b/examples/expectations/column_map_expectation_template.py
--- a/examples/expectations/column_map_expectation_template.py
+++ b/examples/expectations/column_map_expectation_template.py
@@ -31,14 +31,14 @@
         raise NotImplementedError
 
     # This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine
-    @column_condition_partial(engine=SqlAlchemyExecutionEngine)
-    def _sqlalchemy(cls, column, _dialect, **kwargs):
-        raise NotImplementedError
+    # @column_condition_partial(engine=SqlAlchemyExecutionEngine)
+    # def _sqlalchemy(cls, column, _dialect, **kwargs):
+    #     raise NotImplementedError
 
     # This method defines the business logic for evaluating your metric when using a SparkDFExecutionEngine
-    @column_condition_partial(engine=SparkDFExecutionEngine)
-    def _spark(cls, column, **kwargs):
-        raise NotImplementedError
+    # @column_condition_partial(engine=SparkDFExecutionEngine)
+    # def _spark(cls, column, **kwargs):
+    #     raise NotImplementedError
 
 
 # This class defines the Expectation itself

{"golden_diff": "diff --git a/examples/expectations/column_aggregate_expectation_template.py b/examples/expectations/column_aggregate_expectation_template.py\n--- a/examples/expectations/column_aggregate_expectation_template.py\n+++ b/examples/expectations/column_aggregate_expectation_template.py\n@@ -34,14 +34,14 @@\n         raise NotImplementedError\n \n     # This method defines the business logic for evaluating your Metric when using a SqlAlchemyExecutionEngine\n-    @column_aggregate_partial(engine=SqlAlchemyExecutionEngine)\n-    def _sqlalchemy(cls, column, _dialect, **kwargs):\n-        raise NotImplementedError\n-\n+    # @column_aggregate_partial(engine=SqlAlchemyExecutionEngine)\n+    # def _sqlalchemy(cls, column, _dialect, **kwargs):\n+    #     raise NotImplementedError\n+    #\n     # This method defines the business logic for evaluating your Metric when using a SparkDFExecutionEngine\n-    @column_aggregate_partial(engine=SparkDFExecutionEngine)\n-    def _spark(cls, column, **kwargs):\n-        raise NotImplementedError\n+    # @column_aggregate_partial(engine=SparkDFExecutionEngine)\n+    # def _spark(cls, column, **kwargs):\n+    #     raise NotImplementedError\n \n \n # This class defines the Expectation itself\ndiff --git a/examples/expectations/column_map_expectation_template.py b/examples/expectations/column_map_expectation_template.py\n--- a/examples/expectations/column_map_expectation_template.py\n+++ b/examples/expectations/column_map_expectation_template.py\n@@ -31,14 +31,14 @@\n         raise NotImplementedError\n \n     # This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine\n-    @column_condition_partial(engine=SqlAlchemyExecutionEngine)\n-    def _sqlalchemy(cls, column, _dialect, **kwargs):\n-        raise NotImplementedError\n+    # @column_condition_partial(engine=SqlAlchemyExecutionEngine)\n+    # def _sqlalchemy(cls, column, _dialect, **kwargs):\n+    #     raise NotImplementedError\n \n     # This method defines the business logic for evaluating your metric when using a SparkDFExecutionEngine\n-    @column_condition_partial(engine=SparkDFExecutionEngine)\n-    def _spark(cls, column, **kwargs):\n-        raise NotImplementedError\n+    # @column_condition_partial(engine=SparkDFExecutionEngine)\n+    # def _spark(cls, column, **kwargs):\n+    #     raise NotImplementedError\n \n \n # This class defines the Expectation itself\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": "\"\"\"\nThis is a template for creating custom ColumnExpectations.\nFor detailed instructions on how to use it, please see:\n    https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations\n\"\"\"\n\nfrom typing import Dict\n\nfrom great_expectations.core import ExpectationConfiguration\nfrom great_expectations.execution_engine import (\n    ExecutionEngine,\n    PandasExecutionEngine,\n    SparkDFExecutionEngine,\n    SqlAlchemyExecutionEngine,\n)\nfrom great_expectations.expectations.expectation import ColumnExpectation\nfrom great_expectations.expectations.metrics import (\n    ColumnAggregateMetricProvider,\n    column_aggregate_partial,\n    column_aggregate_value,\n)\n\n\n# This class defines a Metric to support your Expectation.\n# For most ColumnExpectations, the main business logic for calculation will live in this class.\nclass ColumnAggregateMatchesSomeCriteria(ColumnAggregateMetricProvider):\n\n    # This is the id string that will be used to reference your Metric.\n    metric_name = \"METRIC NAME GOES HERE\"\n\n    # This method implements the core logic for the PandasExecutionEngine\n    @column_aggregate_value(engine=PandasExecutionEngine)\n    def _pandas(cls, column, **kwargs):\n        raise NotImplementedError\n\n    # This method defines the business logic for evaluating your Metric when using a SqlAlchemyExecutionEngine\n    @column_aggregate_partial(engine=SqlAlchemyExecutionEngine)\n    def _sqlalchemy(cls, column, _dialect, **kwargs):\n        raise NotImplementedError\n\n    # This method defines the business logic for evaluating your Metric when using a SparkDFExecutionEngine\n    @column_aggregate_partial(engine=SparkDFExecutionEngine)\n    def _spark(cls, column, **kwargs):\n        raise NotImplementedError\n\n\n# This class defines the Expectation itself\nclass ExpectColumnAggregateToMatchSomeCriteria(ColumnExpectation):\n    \"\"\"TODO: add a docstring here\"\"\"\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    # This is a tuple consisting of all Metrics necessary to evaluate the Expectation.\n    metric_dependencies = (\"METRIC NAME GOES HERE\",)\n\n    # This a tuple of parameter names that can affect whether the Expectation evaluates to True or False.\n    success_keys = (\"min_value\", \"strict_min\", \"max_value\", \"strict_max\")\n\n    # This dictionary contains default values for any parameters that should have default values.\n    default_kwarg_values = {}\n\n    # This method performs a validation of your metrics against your success keys, returning a dict indicating the success or failure of the Expectation.\n    def _validate(\n        self,\n        configuration: ExpectationConfiguration,\n        metrics: Dict,\n        runtime_configuration: dict = None,\n        execution_engine: ExecutionEngine = None,\n    ):\n        raise NotImplementedError\n\n    # This object contains metadata for display in the public Gallery\n    library_metadata = {\n        \"tags\": [],  # Tags for this Expectation in the Gallery\n        \"contributors\": [  # Github handles for all contributors to this Expectation.\n            \"@your_name_here\",  # Don't forget to add your github handle here!\n        ],\n    }\n\n\nif __name__ == \"__main__\":\n    ExpectColumnAggregateToMatchSomeCriteria().print_diagnostic_checklist()\n", "path": "examples/expectations/column_aggregate_expectation_template.py"}, {"content": "\"\"\"\nThis is a template for creating custom ColumnMapExpectations.\nFor detailed instructions on how to use it, please see:\n    https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations\n\"\"\"\n\nimport json\n\nfrom great_expectations.execution_engine import (\n    PandasExecutionEngine,\n    SparkDFExecutionEngine,\n    SqlAlchemyExecutionEngine,\n)\nfrom great_expectations.expectations.expectation import ColumnMapExpectation\nfrom great_expectations.expectations.metrics import (\n    ColumnMapMetricProvider,\n    column_condition_partial,\n)\n\n\n# This class defines a Metric to support your Expectation.\n# For most ColumnMapExpectations, the main business logic for calculation will live in this class.\nclass ColumnValuesMatchSomeCriteria(ColumnMapMetricProvider):\n\n    # This is the id string that will be used to reference your metric.\n    condition_metric_name = \"METRIC NAME GOES HERE\"\n\n    # This method implements the core logic for the PandasExecutionEngine\n    @column_condition_partial(engine=PandasExecutionEngine)\n    def _pandas(cls, column, **kwargs):\n        raise NotImplementedError\n\n    # This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine\n    @column_condition_partial(engine=SqlAlchemyExecutionEngine)\n    def _sqlalchemy(cls, column, _dialect, **kwargs):\n        raise NotImplementedError\n\n    # This method defines the business logic for evaluating your metric when using a SparkDFExecutionEngine\n    @column_condition_partial(engine=SparkDFExecutionEngine)\n    def _spark(cls, column, **kwargs):\n        raise NotImplementedError\n\n\n# This class defines the Expectation itself\nclass ExpectColumnValuesToMatchSomeCriteria(ColumnMapExpectation):\n    \"\"\"TODO: Add a docstring here\"\"\"\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    # 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    map_metric = \"METRIC NAME GOES HERE\"\n\n    # This is a list of parameter names that can affect whether the Expectation evaluates to True or False\n    success_keys = (\"mostly\",)\n\n    # This dictionary contains default values for any parameters that should have default values\n    default_kwarg_values = {}\n\n    # This object contains metadata for display in the public Gallery\n    library_metadata = {\n        \"tags\": [],  # Tags for this Expectation in the Gallery\n        \"contributors\": [  # Github handles for all contributors to this Expectation.\n            \"@your_name_here\",  # Don't forget to add your github handle here!\n        ],\n    }\n\n\nif __name__ == \"__main__\":\n    ExpectColumnValuesToMatchSomeCriteria().print_diagnostic_checklist()\n", "path": "examples/expectations/column_map_expectation_template.py"}], "after_files": [{"content": "\"\"\"\nThis is a template for creating custom ColumnExpectations.\nFor detailed instructions on how to use it, please see:\n    https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations\n\"\"\"\n\nfrom typing import Dict\n\nfrom great_expectations.core import ExpectationConfiguration\nfrom great_expectations.execution_engine import (\n    ExecutionEngine,\n    PandasExecutionEngine,\n    SparkDFExecutionEngine,\n    SqlAlchemyExecutionEngine,\n)\nfrom great_expectations.expectations.expectation import ColumnExpectation\nfrom great_expectations.expectations.metrics import (\n    ColumnAggregateMetricProvider,\n    column_aggregate_partial,\n    column_aggregate_value,\n)\n\n\n# This class defines a Metric to support your Expectation.\n# For most ColumnExpectations, the main business logic for calculation will live in this class.\nclass ColumnAggregateMatchesSomeCriteria(ColumnAggregateMetricProvider):\n\n    # This is the id string that will be used to reference your Metric.\n    metric_name = \"METRIC NAME GOES HERE\"\n\n    # This method implements the core logic for the PandasExecutionEngine\n    @column_aggregate_value(engine=PandasExecutionEngine)\n    def _pandas(cls, column, **kwargs):\n        raise NotImplementedError\n\n    # This method defines the business logic for evaluating your Metric when using a SqlAlchemyExecutionEngine\n    # @column_aggregate_partial(engine=SqlAlchemyExecutionEngine)\n    # def _sqlalchemy(cls, column, _dialect, **kwargs):\n    #     raise NotImplementedError\n    #\n    # This method defines the business logic for evaluating your Metric when using a SparkDFExecutionEngine\n    # @column_aggregate_partial(engine=SparkDFExecutionEngine)\n    # def _spark(cls, column, **kwargs):\n    #     raise NotImplementedError\n\n\n# This class defines the Expectation itself\nclass ExpectColumnAggregateToMatchSomeCriteria(ColumnExpectation):\n    \"\"\"TODO: add a docstring here\"\"\"\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    # This is a tuple consisting of all Metrics necessary to evaluate the Expectation.\n    metric_dependencies = (\"METRIC NAME GOES HERE\",)\n\n    # This a tuple of parameter names that can affect whether the Expectation evaluates to True or False.\n    success_keys = (\"min_value\", \"strict_min\", \"max_value\", \"strict_max\")\n\n    # This dictionary contains default values for any parameters that should have default values.\n    default_kwarg_values = {}\n\n    # This method performs a validation of your metrics against your success keys, returning a dict indicating the success or failure of the Expectation.\n    def _validate(\n        self,\n        configuration: ExpectationConfiguration,\n        metrics: Dict,\n        runtime_configuration: dict = None,\n        execution_engine: ExecutionEngine = None,\n    ):\n        raise NotImplementedError\n\n    # This object contains metadata for display in the public Gallery\n    library_metadata = {\n        \"tags\": [],  # Tags for this Expectation in the Gallery\n        \"contributors\": [  # Github handles for all contributors to this Expectation.\n            \"@your_name_here\",  # Don't forget to add your github handle here!\n        ],\n    }\n\n\nif __name__ == \"__main__\":\n    ExpectColumnAggregateToMatchSomeCriteria().print_diagnostic_checklist()\n", "path": "examples/expectations/column_aggregate_expectation_template.py"}, {"content": "\"\"\"\nThis is a template for creating custom ColumnMapExpectations.\nFor detailed instructions on how to use it, please see:\n    https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations\n\"\"\"\n\nimport json\n\nfrom great_expectations.execution_engine import (\n    PandasExecutionEngine,\n    SparkDFExecutionEngine,\n    SqlAlchemyExecutionEngine,\n)\nfrom great_expectations.expectations.expectation import ColumnMapExpectation\nfrom great_expectations.expectations.metrics import (\n    ColumnMapMetricProvider,\n    column_condition_partial,\n)\n\n\n# This class defines a Metric to support your Expectation.\n# For most ColumnMapExpectations, the main business logic for calculation will live in this class.\nclass ColumnValuesMatchSomeCriteria(ColumnMapMetricProvider):\n\n    # This is the id string that will be used to reference your metric.\n    condition_metric_name = \"METRIC NAME GOES HERE\"\n\n    # This method implements the core logic for the PandasExecutionEngine\n    @column_condition_partial(engine=PandasExecutionEngine)\n    def _pandas(cls, column, **kwargs):\n        raise NotImplementedError\n\n    # This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine\n    # @column_condition_partial(engine=SqlAlchemyExecutionEngine)\n    # def _sqlalchemy(cls, column, _dialect, **kwargs):\n    #     raise NotImplementedError\n\n    # This method defines the business logic for evaluating your metric when using a SparkDFExecutionEngine\n    # @column_condition_partial(engine=SparkDFExecutionEngine)\n    # def _spark(cls, column, **kwargs):\n    #     raise NotImplementedError\n\n\n# This class defines the Expectation itself\nclass ExpectColumnValuesToMatchSomeCriteria(ColumnMapExpectation):\n    \"\"\"TODO: Add a docstring here\"\"\"\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    # 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    map_metric = \"METRIC NAME GOES HERE\"\n\n    # This is a list of parameter names that can affect whether the Expectation evaluates to True or False\n    success_keys = (\"mostly\",)\n\n    # This dictionary contains default values for any parameters that should have default values\n    default_kwarg_values = {}\n\n    # This object contains metadata for display in the public Gallery\n    library_metadata = {\n        \"tags\": [],  # Tags for this Expectation in the Gallery\n        \"contributors\": [  # Github handles for all contributors to this Expectation.\n            \"@your_name_here\",  # Don't forget to add your github handle here!\n        ],\n    }\n\n\nif __name__ == \"__main__\":\n    ExpectColumnValuesToMatchSomeCriteria().print_diagnostic_checklist()\n", "path": "examples/expectations/column_map_expectation_template.py"}]}