prompt: 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()
  
  ```
  
golden_diff: 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
  
verification_info: {"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"}]}