prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Use cleaner solution for non-truncating division in python 2
  Prefer `from __future__ import division` to `1.*x/y`
  
  </issue>
  <code>
  [start of great_expectations/rule_based_profiler/data_assistant/data_assistant_dispatcher.py]
  1 import logging
  2 from typing import Dict, Optional, Type
  3 
  4 from great_expectations.rule_based_profiler.data_assistant import DataAssistant
  5 from great_expectations.rule_based_profiler.data_assistant.data_assistant_runner import (
  6     DataAssistantRunner,
  7 )
  8 
  9 logger = logging.getLogger(__name__)
  10 
  11 
  12 class DataAssistantDispatcher:
  13     """
  14     DataAssistantDispatcher intercepts requests for "DataAssistant" classes by their registered names and manages their
  15     associated "DataAssistantRunner" objects, which process invocations of calls to "DataAssistant" "run()" methods.
  16     """
  17 
  18     _registered_data_assistants: Dict[str, Type[DataAssistant]] = {}
  19 
  20     def __init__(self, data_context: "BaseDataContext") -> None:  # noqa: F821
  21         """
  22         Args:
  23             data_context: BaseDataContext associated with DataAssistantDispatcher
  24         """
  25         self._data_context = data_context
  26 
  27         self._data_assistant_runner_cache = {}
  28 
  29     def __getattr__(self, name: str) -> DataAssistantRunner:
  30         # Both, registered data_assistant_type and alias name are supported for invocation.
  31 
  32         # _registered_data_assistants has both aliases and full names
  33         data_assistant_cls: Optional[
  34             Type[DataAssistant]
  35         ] = DataAssistantDispatcher.get_data_assistant_impl(name=name)
  36 
  37         # If "DataAssistant" is not registered, then raise "AttributeError", which is appropriate for "__getattr__()".
  38         if data_assistant_cls is None:
  39             raise AttributeError(
  40                 f'"{type(self).__name__}" object has no attribute "{name}".'
  41             )
  42 
  43         data_assistant_name: str = data_assistant_cls.data_assistant_type
  44         data_assistant_runner: Optional[
  45             DataAssistantRunner
  46         ] = self._data_assistant_runner_cache.get(data_assistant_name)
  47         if data_assistant_runner is None:
  48             data_assistant_runner = DataAssistantRunner(
  49                 data_assistant_cls=data_assistant_cls,
  50                 data_context=self._data_context,
  51             )
  52             self._data_assistant_runner_cache[
  53                 data_assistant_name
  54             ] = data_assistant_runner
  55 
  56         return data_assistant_runner
  57 
  58     @classmethod
  59     def register_data_assistant(
  60         cls,
  61         data_assistant: Type[DataAssistant],  # noqa: F821
  62     ) -> None:
  63         """
  64         This method executes "run()" of effective "RuleBasedProfiler" and fills "DataAssistantResult" object with outputs.
  65 
  66         Args:
  67             data_assistant: "DataAssistant" class to be registered
  68         """
  69         data_assistant_type = data_assistant.data_assistant_type
  70         cls._register(data_assistant_type, data_assistant)
  71 
  72         alias: Optional[str] = data_assistant.__alias__
  73         if alias is not None:
  74             cls._register(alias, data_assistant)
  75 
  76     @classmethod
  77     def _register(cls, name: str, data_assistant: Type[DataAssistant]) -> None:
  78         registered_data_assistants = cls._registered_data_assistants
  79 
  80         if name in registered_data_assistants:
  81             raise ValueError(f'Existing declarations of DataAssistant "{name}" found.')
  82 
  83         logger.debug(
  84             f'Registering the declaration of DataAssistant "{name}" took place.'
  85         )
  86         registered_data_assistants[name] = data_assistant
  87 
  88     @classmethod
  89     def get_data_assistant_impl(
  90         cls,
  91         name: Optional[str],
  92     ) -> Optional[Type[DataAssistant]]:  # noqa: F821
  93         """
  94         This method obtains (previously registered) "DataAssistant" class from DataAssistant Registry.
  95 
  96         Note that it will clean the input string before checking against registered assistants.
  97 
  98         Args:
  99             data_assistant_type: String representing "snake case" version of "DataAssistant" class type
  100 
  101         Returns:
  102             Class inheriting "DataAssistant" if found; otherwise, None
  103         """
  104         if name is None:
  105             return None
  106         name = name.lower()
  107         return cls._registered_data_assistants.get(name)
  108
  [end of great_expectations/rule_based_profiler/data_assistant/data_assistant_dispatcher.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/great_expectations/rule_based_profiler/data_assistant/data_assistant_dispatcher.py b/great_expectations/rule_based_profiler/data_assistant/data_assistant_dispatcher.py
  --- a/great_expectations/rule_based_profiler/data_assistant/data_assistant_dispatcher.py
  +++ b/great_expectations/rule_based_profiler/data_assistant/data_assistant_dispatcher.py
  @@ -1,5 +1,5 @@
   import logging
  -from typing import Dict, Optional, Type
  +from typing import Dict, Optional, Set, Type
   
   from great_expectations.rule_based_profiler.data_assistant import DataAssistant
   from great_expectations.rule_based_profiler.data_assistant.data_assistant_runner import (
  @@ -61,7 +61,7 @@
           data_assistant: Type[DataAssistant],  # noqa: F821
       ) -> None:
           """
  -        This method executes "run()" of effective "RuleBasedProfiler" and fills "DataAssistantResult" object with outputs.
  +        This method registers "DataAssistant" subclass for future instantiation and execution of its "run()" method.
   
           Args:
               data_assistant: "DataAssistant" class to be registered
  @@ -89,14 +89,14 @@
       def get_data_assistant_impl(
           cls,
           name: Optional[str],
  -    ) -> Optional[Type[DataAssistant]]:  # noqa: F821
  +    ) -> Optional[Type[DataAssistant]]:
           """
           This method obtains (previously registered) "DataAssistant" class from DataAssistant Registry.
   
           Note that it will clean the input string before checking against registered assistants.
   
           Args:
  -            data_assistant_type: String representing "snake case" version of "DataAssistant" class type
  +            name: String representing "snake case" version of "DataAssistant" class type
   
           Returns:
               Class inheriting "DataAssistant" if found; otherwise, None
  @@ -105,3 +105,23 @@
               return None
           name = name.lower()
           return cls._registered_data_assistants.get(name)
  +
  +    def __dir__(self):
  +        """
  +        This custom magic method is used to enable tab completion on "DataAssistantDispatcher" objects.
  +        """
  +        data_assistant_dispatcher_attrs: Set[str] = set(super().__dir__())
  +        data_assistant_registered_names: Set[
  +            str
  +        ] = get_registered_data_assistant_names()
  +        combined_dir_attrs: Set[str] = (
  +            data_assistant_dispatcher_attrs | data_assistant_registered_names
  +        )
  +        return list(combined_dir_attrs)
  +
  +
  +def get_registered_data_assistant_names() -> Set[str]:
  +    """
  +    This method returns names (registered data_assistant_type and alias name) of registered "DataAssistant" classes.
  +    """
  +    return set(DataAssistantDispatcher._registered_data_assistants.keys())
  
verification_info: {"golden_diff": "diff --git a/great_expectations/rule_based_profiler/data_assistant/data_assistant_dispatcher.py b/great_expectations/rule_based_profiler/data_assistant/data_assistant_dispatcher.py\n--- a/great_expectations/rule_based_profiler/data_assistant/data_assistant_dispatcher.py\n+++ b/great_expectations/rule_based_profiler/data_assistant/data_assistant_dispatcher.py\n@@ -1,5 +1,5 @@\n import logging\n-from typing import Dict, Optional, Type\n+from typing import Dict, Optional, Set, Type\n \n from great_expectations.rule_based_profiler.data_assistant import DataAssistant\n from great_expectations.rule_based_profiler.data_assistant.data_assistant_runner import (\n@@ -61,7 +61,7 @@\n         data_assistant: Type[DataAssistant],  # noqa: F821\n     ) -> None:\n         \"\"\"\n-        This method executes \"run()\" of effective \"RuleBasedProfiler\" and fills \"DataAssistantResult\" object with outputs.\n+        This method registers \"DataAssistant\" subclass for future instantiation and execution of its \"run()\" method.\n \n         Args:\n             data_assistant: \"DataAssistant\" class to be registered\n@@ -89,14 +89,14 @@\n     def get_data_assistant_impl(\n         cls,\n         name: Optional[str],\n-    ) -> Optional[Type[DataAssistant]]:  # noqa: F821\n+    ) -> Optional[Type[DataAssistant]]:\n         \"\"\"\n         This method obtains (previously registered) \"DataAssistant\" class from DataAssistant Registry.\n \n         Note that it will clean the input string before checking against registered assistants.\n \n         Args:\n-            data_assistant_type: String representing \"snake case\" version of \"DataAssistant\" class type\n+            name: String representing \"snake case\" version of \"DataAssistant\" class type\n \n         Returns:\n             Class inheriting \"DataAssistant\" if found; otherwise, None\n@@ -105,3 +105,23 @@\n             return None\n         name = name.lower()\n         return cls._registered_data_assistants.get(name)\n+\n+    def __dir__(self):\n+        \"\"\"\n+        This custom magic method is used to enable tab completion on \"DataAssistantDispatcher\" objects.\n+        \"\"\"\n+        data_assistant_dispatcher_attrs: Set[str] = set(super().__dir__())\n+        data_assistant_registered_names: Set[\n+            str\n+        ] = get_registered_data_assistant_names()\n+        combined_dir_attrs: Set[str] = (\n+            data_assistant_dispatcher_attrs | data_assistant_registered_names\n+        )\n+        return list(combined_dir_attrs)\n+\n+\n+def get_registered_data_assistant_names() -> Set[str]:\n+    \"\"\"\n+    This method returns names (registered data_assistant_type and alias name) of registered \"DataAssistant\" classes.\n+    \"\"\"\n+    return set(DataAssistantDispatcher._registered_data_assistants.keys())\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": "import logging\nfrom typing import Dict, Optional, Type\n\nfrom great_expectations.rule_based_profiler.data_assistant import DataAssistant\nfrom great_expectations.rule_based_profiler.data_assistant.data_assistant_runner import (\n    DataAssistantRunner,\n)\n\nlogger = logging.getLogger(__name__)\n\n\nclass DataAssistantDispatcher:\n    \"\"\"\n    DataAssistantDispatcher intercepts requests for \"DataAssistant\" classes by their registered names and manages their\n    associated \"DataAssistantRunner\" objects, which process invocations of calls to \"DataAssistant\" \"run()\" methods.\n    \"\"\"\n\n    _registered_data_assistants: Dict[str, Type[DataAssistant]] = {}\n\n    def __init__(self, data_context: \"BaseDataContext\") -> None:  # noqa: F821\n        \"\"\"\n        Args:\n            data_context: BaseDataContext associated with DataAssistantDispatcher\n        \"\"\"\n        self._data_context = data_context\n\n        self._data_assistant_runner_cache = {}\n\n    def __getattr__(self, name: str) -> DataAssistantRunner:\n        # Both, registered data_assistant_type and alias name are supported for invocation.\n\n        # _registered_data_assistants has both aliases and full names\n        data_assistant_cls: Optional[\n            Type[DataAssistant]\n        ] = DataAssistantDispatcher.get_data_assistant_impl(name=name)\n\n        # If \"DataAssistant\" is not registered, then raise \"AttributeError\", which is appropriate for \"__getattr__()\".\n        if data_assistant_cls is None:\n            raise AttributeError(\n                f'\"{type(self).__name__}\" object has no attribute \"{name}\".'\n            )\n\n        data_assistant_name: str = data_assistant_cls.data_assistant_type\n        data_assistant_runner: Optional[\n            DataAssistantRunner\n        ] = self._data_assistant_runner_cache.get(data_assistant_name)\n        if data_assistant_runner is None:\n            data_assistant_runner = DataAssistantRunner(\n                data_assistant_cls=data_assistant_cls,\n                data_context=self._data_context,\n            )\n            self._data_assistant_runner_cache[\n                data_assistant_name\n            ] = data_assistant_runner\n\n        return data_assistant_runner\n\n    @classmethod\n    def register_data_assistant(\n        cls,\n        data_assistant: Type[DataAssistant],  # noqa: F821\n    ) -> None:\n        \"\"\"\n        This method executes \"run()\" of effective \"RuleBasedProfiler\" and fills \"DataAssistantResult\" object with outputs.\n\n        Args:\n            data_assistant: \"DataAssistant\" class to be registered\n        \"\"\"\n        data_assistant_type = data_assistant.data_assistant_type\n        cls._register(data_assistant_type, data_assistant)\n\n        alias: Optional[str] = data_assistant.__alias__\n        if alias is not None:\n            cls._register(alias, data_assistant)\n\n    @classmethod\n    def _register(cls, name: str, data_assistant: Type[DataAssistant]) -> None:\n        registered_data_assistants = cls._registered_data_assistants\n\n        if name in registered_data_assistants:\n            raise ValueError(f'Existing declarations of DataAssistant \"{name}\" found.')\n\n        logger.debug(\n            f'Registering the declaration of DataAssistant \"{name}\" took place.'\n        )\n        registered_data_assistants[name] = data_assistant\n\n    @classmethod\n    def get_data_assistant_impl(\n        cls,\n        name: Optional[str],\n    ) -> Optional[Type[DataAssistant]]:  # noqa: F821\n        \"\"\"\n        This method obtains (previously registered) \"DataAssistant\" class from DataAssistant Registry.\n\n        Note that it will clean the input string before checking against registered assistants.\n\n        Args:\n            data_assistant_type: String representing \"snake case\" version of \"DataAssistant\" class type\n\n        Returns:\n            Class inheriting \"DataAssistant\" if found; otherwise, None\n        \"\"\"\n        if name is None:\n            return None\n        name = name.lower()\n        return cls._registered_data_assistants.get(name)\n", "path": "great_expectations/rule_based_profiler/data_assistant/data_assistant_dispatcher.py"}]}