We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Align terminology (`option` v.s. `config`)


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `pytorch_pfn_extras/runtime/_to.py`
Content:
```
1 from typing import Any, Dict, Optional, Type, TypeVar
2 
3 import torch
4 
5 import pytorch_pfn_extras as ppe
6 from pytorch_pfn_extras.runtime._runtime import DeviceLike, BaseRuntime
7 
8 
9 ModuleOrTensor = TypeVar('ModuleOrTensor', torch.nn.Module, torch.Tensor)
10 
11 
12 def to(
13         module_or_tensor: ModuleOrTensor,
14         device: DeviceLike,
15         *,
16         config: Optional[Dict[str, Any]] = None,
17         runtime_class: Optional[Type[BaseRuntime]] = None,
18 ) -> ModuleOrTensor:
19     """A function to transfer the given object to the given device.
20 
21     If PyTorch's device type is given as the ``device`` argument,
22     the behavior of this function is  equivalent to
23     ``module_or_tensor.to(module_or_tensor, device)``.
24 
25     Otherwise, this function uses the **Runtime** mechanism.
26     This function looks for the Runtime for the device from the RuntimeRegistry
27     and delegates the actual transfer operation to it.
28 
29     See also the documentation of ``ppe.runtime.BaseRuntime`` for details.
30 
31     Args:
32         module_or_tensor (torch.nn.Module or torch.Tensor):
33             An object to be transferred.
34         device (torch.device or str):
35             The device that the input object is transferred to.
36         config (dict, optional):
37             A config of dictionary type that is passed to
38             ``runtime_class.__init__`` as an argument.
39         runtime_class:
40             A runtime class inherited from `BaseRuntime` class.
41             If ``None``, a runtime class is automatically selected
42             based on the ``device`` argument from the runtime registry.
43 
44     Returns:
45         A `torch.Tensor` with the specified device.
46     """
47     if config is None:
48         config = {}
49     if runtime_class is None:
50         registry = ppe.runtime.runtime_registry
51         runtime_class = registry.get_runtime_class_for_device_spec(device)
52     runtime = runtime_class(device, config)
53     obj = module_or_tensor
54     if isinstance(obj, torch.nn.Module):
55         ppe.runtime._runtime._set_module_runtime_tag(obj, runtime)
56         return runtime.move_module(obj)
57     elif isinstance(obj, torch.Tensor):
58         return runtime.move_tensor(obj)
59     else:
60         raise ValueError('Unsupported type for module_or_tensor')
61
```
--- 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/pytorch_pfn_extras/runtime/_to.py b/pytorch_pfn_extras/runtime/_to.py
--- a/pytorch_pfn_extras/runtime/_to.py
+++ b/pytorch_pfn_extras/runtime/_to.py
@@ -13,8 +13,9 @@
         module_or_tensor: ModuleOrTensor,
         device: DeviceLike,
         *,
-        config: Optional[Dict[str, Any]] = None,
+        options: Optional[Dict[str, Any]] = None,
         runtime_class: Optional[Type[BaseRuntime]] = None,
+        config: Optional[Dict[str, Any]] = None,
 ) -> ModuleOrTensor:
     """A function to transfer the given object to the given device.
 
@@ -33,23 +34,30 @@
             An object to be transferred.
         device (torch.device or str):
             The device that the input object is transferred to.
-        config (dict, optional):
-            A config of dictionary type that is passed to
+        options (dict, optional):
+            An options of dictionary type that is passed to
             ``runtime_class.__init__`` as an argument.
         runtime_class:
             A runtime class inherited from `BaseRuntime` class.
             If ``None``, a runtime class is automatically selected
             based on the ``device`` argument from the runtime registry.
+        config (dict, optional):
+            DEPRECATED. Use `options`.
 
     Returns:
         A `torch.Tensor` with the specified device.
     """
-    if config is None:
-        config = {}
+    if options is None:
+        options = {}
+        if config is not None:
+            options = config
+    elif config is not None:
+        raise ValueError('options and config cannot be specified together')
+
     if runtime_class is None:
         registry = ppe.runtime.runtime_registry
         runtime_class = registry.get_runtime_class_for_device_spec(device)
-    runtime = runtime_class(device, config)
+    runtime = runtime_class(device, options)
     obj = module_or_tensor
     if isinstance(obj, torch.nn.Module):
         ppe.runtime._runtime._set_module_runtime_tag(obj, runtime)

{"golden_diff": "diff --git a/pytorch_pfn_extras/runtime/_to.py b/pytorch_pfn_extras/runtime/_to.py\n--- a/pytorch_pfn_extras/runtime/_to.py\n+++ b/pytorch_pfn_extras/runtime/_to.py\n@@ -13,8 +13,9 @@\n         module_or_tensor: ModuleOrTensor,\n         device: DeviceLike,\n         *,\n-        config: Optional[Dict[str, Any]] = None,\n+        options: Optional[Dict[str, Any]] = None,\n         runtime_class: Optional[Type[BaseRuntime]] = None,\n+        config: Optional[Dict[str, Any]] = None,\n ) -> ModuleOrTensor:\n     \"\"\"A function to transfer the given object to the given device.\n \n@@ -33,23 +34,30 @@\n             An object to be transferred.\n         device (torch.device or str):\n             The device that the input object is transferred to.\n-        config (dict, optional):\n-            A config of dictionary type that is passed to\n+        options (dict, optional):\n+            An options of dictionary type that is passed to\n             ``runtime_class.__init__`` as an argument.\n         runtime_class:\n             A runtime class inherited from `BaseRuntime` class.\n             If ``None``, a runtime class is automatically selected\n             based on the ``device`` argument from the runtime registry.\n+        config (dict, optional):\n+            DEPRECATED. Use `options`.\n \n     Returns:\n         A `torch.Tensor` with the specified device.\n     \"\"\"\n-    if config is None:\n-        config = {}\n+    if options is None:\n+        options = {}\n+        if config is not None:\n+            options = config\n+    elif config is not None:\n+        raise ValueError('options and config cannot be specified together')\n+\n     if runtime_class is None:\n         registry = ppe.runtime.runtime_registry\n         runtime_class = registry.get_runtime_class_for_device_spec(device)\n-    runtime = runtime_class(device, config)\n+    runtime = runtime_class(device, options)\n     obj = module_or_tensor\n     if isinstance(obj, torch.nn.Module):\n         ppe.runtime._runtime._set_module_runtime_tag(obj, runtime)\n", "issue": "Align terminology (`option` v.s. `config`)\n\n", "before_files": [{"content": "from typing import Any, Dict, Optional, Type, TypeVar\n\nimport torch\n\nimport pytorch_pfn_extras as ppe\nfrom pytorch_pfn_extras.runtime._runtime import DeviceLike, BaseRuntime\n\n\nModuleOrTensor = TypeVar('ModuleOrTensor', torch.nn.Module, torch.Tensor)\n\n\ndef to(\n        module_or_tensor: ModuleOrTensor,\n        device: DeviceLike,\n        *,\n        config: Optional[Dict[str, Any]] = None,\n        runtime_class: Optional[Type[BaseRuntime]] = None,\n) -> ModuleOrTensor:\n    \"\"\"A function to transfer the given object to the given device.\n\n    If PyTorch's device type is given as the ``device`` argument,\n    the behavior of this function is  equivalent to\n    ``module_or_tensor.to(module_or_tensor, device)``.\n\n    Otherwise, this function uses the **Runtime** mechanism.\n    This function looks for the Runtime for the device from the RuntimeRegistry\n    and delegates the actual transfer operation to it.\n\n    See also the documentation of ``ppe.runtime.BaseRuntime`` for details.\n\n    Args:\n        module_or_tensor (torch.nn.Module or torch.Tensor):\n            An object to be transferred.\n        device (torch.device or str):\n            The device that the input object is transferred to.\n        config (dict, optional):\n            A config of dictionary type that is passed to\n            ``runtime_class.__init__`` as an argument.\n        runtime_class:\n            A runtime class inherited from `BaseRuntime` class.\n            If ``None``, a runtime class is automatically selected\n            based on the ``device`` argument from the runtime registry.\n\n    Returns:\n        A `torch.Tensor` with the specified device.\n    \"\"\"\n    if config is None:\n        config = {}\n    if runtime_class is None:\n        registry = ppe.runtime.runtime_registry\n        runtime_class = registry.get_runtime_class_for_device_spec(device)\n    runtime = runtime_class(device, config)\n    obj = module_or_tensor\n    if isinstance(obj, torch.nn.Module):\n        ppe.runtime._runtime._set_module_runtime_tag(obj, runtime)\n        return runtime.move_module(obj)\n    elif isinstance(obj, torch.Tensor):\n        return runtime.move_tensor(obj)\n    else:\n        raise ValueError('Unsupported type for module_or_tensor')\n", "path": "pytorch_pfn_extras/runtime/_to.py"}], "after_files": [{"content": "from typing import Any, Dict, Optional, Type, TypeVar\n\nimport torch\n\nimport pytorch_pfn_extras as ppe\nfrom pytorch_pfn_extras.runtime._runtime import DeviceLike, BaseRuntime\n\n\nModuleOrTensor = TypeVar('ModuleOrTensor', torch.nn.Module, torch.Tensor)\n\n\ndef to(\n        module_or_tensor: ModuleOrTensor,\n        device: DeviceLike,\n        *,\n        options: Optional[Dict[str, Any]] = None,\n        runtime_class: Optional[Type[BaseRuntime]] = None,\n        config: Optional[Dict[str, Any]] = None,\n) -> ModuleOrTensor:\n    \"\"\"A function to transfer the given object to the given device.\n\n    If PyTorch's device type is given as the ``device`` argument,\n    the behavior of this function is  equivalent to\n    ``module_or_tensor.to(module_or_tensor, device)``.\n\n    Otherwise, this function uses the **Runtime** mechanism.\n    This function looks for the Runtime for the device from the RuntimeRegistry\n    and delegates the actual transfer operation to it.\n\n    See also the documentation of ``ppe.runtime.BaseRuntime`` for details.\n\n    Args:\n        module_or_tensor (torch.nn.Module or torch.Tensor):\n            An object to be transferred.\n        device (torch.device or str):\n            The device that the input object is transferred to.\n        options (dict, optional):\n            An options of dictionary type that is passed to\n            ``runtime_class.__init__`` as an argument.\n        runtime_class:\n            A runtime class inherited from `BaseRuntime` class.\n            If ``None``, a runtime class is automatically selected\n            based on the ``device`` argument from the runtime registry.\n        config (dict, optional):\n            DEPRECATED. Use `options`.\n\n    Returns:\n        A `torch.Tensor` with the specified device.\n    \"\"\"\n    if options is None:\n        options = {}\n        if config is not None:\n            options = config\n    elif config is not None:\n        raise ValueError('options and config cannot be specified together')\n\n    if runtime_class is None:\n        registry = ppe.runtime.runtime_registry\n        runtime_class = registry.get_runtime_class_for_device_spec(device)\n    runtime = runtime_class(device, options)\n    obj = module_or_tensor\n    if isinstance(obj, torch.nn.Module):\n        ppe.runtime._runtime._set_module_runtime_tag(obj, runtime)\n        return runtime.move_module(obj)\n    elif isinstance(obj, torch.Tensor):\n        return runtime.move_tensor(obj)\n    else:\n        raise ValueError('Unsupported type for module_or_tensor')\n", "path": "pytorch_pfn_extras/runtime/_to.py"}]}