We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Add Windows to CI
## Description
Add windows to the CI tests as a separate step for say python 3.8 and torch==1.6.0 initially just to get things working. Then if it works expand to all versions to see any potential issues.

## Definition of Done
This ticket is done when we know what does and doesn't run on Windows in CI from the current "fast" tests and the new "slow" tests. Post a screenshot and link to CI here when it's running.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `src/syft/lib/torch/__init__.py`
Content:
```
1 # stdlib
2 from typing import Dict
3 from typing import Union
4 
5 # third party
6 from packaging import version
7 import torch
8 
9 # syft relative
10 from . import parameter  # noqa: 401
11 from . import uppercase_tensor  # noqa: 401
12 from ...ast.globals import Globals
13 from .allowlist import allowlist
14 
15 TORCH_VERSION = version.parse(torch.__version__)
16 
17 
18 def get_return_type(support_dict: Union[str, Dict[str, str]]) -> str:
19     if isinstance(support_dict, str):
20         return support_dict
21     else:
22         return support_dict["return_type"]
23 
24 
25 def version_supported(support_dict: Union[str, Dict[str, str]]) -> bool:
26     if isinstance(support_dict, str):
27         return True
28     else:
29         return TORCH_VERSION >= version.parse(support_dict["min_version"])
30 
31 
32 def create_torch_ast() -> Globals:
33     ast = Globals()
34 
35     # most methods work in all versions and have a single return type
36     # for the more complicated ones we pass a dict with keys like return_type and
37     # min_version
38     for method, return_type_name_or_dict in allowlist.items():
39         if version_supported(support_dict=return_type_name_or_dict):
40             return_type = get_return_type(support_dict=return_type_name_or_dict)
41             if return_type == "unknown":
42                 # this allows us to import them for testing
43                 continue
44             ast.add_path(
45                 path=method, framework_reference=torch, return_type_name=return_type
46             )
47             # add all the torch.nn.Parameter hooks
48             if method.startswith("torch.Tensor."):
49                 method = method.replace("torch.Tensor.", "torch.nn.Parameter.")
50                 return_type = return_type.replace("torch.Tensor", "torch.nn.Parameter")
51                 ast.add_path(
52                     path=method, framework_reference=torch, return_type_name=return_type
53                 )
54         else:
55             print(f"Skipping torch.{method} not supported in {TORCH_VERSION}")
56 
57     for klass in ast.classes:
58         klass.create_pointer_class()
59         klass.create_send_method()
60         klass.create_serialization_methods()
61         klass.create_storable_object_attr_convenience_methods()
62     return ast
63
```
--- 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/src/syft/lib/torch/__init__.py b/src/syft/lib/torch/__init__.py
--- a/src/syft/lib/torch/__init__.py
+++ b/src/syft/lib/torch/__init__.py
@@ -12,7 +12,7 @@
 from ...ast.globals import Globals
 from .allowlist import allowlist
 
-TORCH_VERSION = version.parse(torch.__version__)
+TORCH_VERSION = version.parse(torch.__version__.split("+")[0])
 
 
 def get_return_type(support_dict: Union[str, Dict[str, str]]) -> str:

{"golden_diff": "diff --git a/src/syft/lib/torch/__init__.py b/src/syft/lib/torch/__init__.py\n--- a/src/syft/lib/torch/__init__.py\n+++ b/src/syft/lib/torch/__init__.py\n@@ -12,7 +12,7 @@\n from ...ast.globals import Globals\n from .allowlist import allowlist\n \n-TORCH_VERSION = version.parse(torch.__version__)\n+TORCH_VERSION = version.parse(torch.__version__.split(\"+\")[0])\n \n \n def get_return_type(support_dict: Union[str, Dict[str, str]]) -> str:\n", "issue": "Add Windows to CI\n## Description\r\nAdd windows to the CI tests as a separate step for say python 3.8 and torch==1.6.0 initially just to get things working. Then if it works expand to all versions to see any potential issues.\r\n\r\n## Definition of Done\r\nThis ticket is done when we know what does and doesn't run on Windows in CI from the current \"fast\" tests and the new \"slow\" tests. Post a screenshot and link to CI here when it's running.\n", "before_files": [{"content": "# stdlib\nfrom typing import Dict\nfrom typing import Union\n\n# third party\nfrom packaging import version\nimport torch\n\n# syft relative\nfrom . import parameter  # noqa: 401\nfrom . import uppercase_tensor  # noqa: 401\nfrom ...ast.globals import Globals\nfrom .allowlist import allowlist\n\nTORCH_VERSION = version.parse(torch.__version__)\n\n\ndef get_return_type(support_dict: Union[str, Dict[str, str]]) -> str:\n    if isinstance(support_dict, str):\n        return support_dict\n    else:\n        return support_dict[\"return_type\"]\n\n\ndef version_supported(support_dict: Union[str, Dict[str, str]]) -> bool:\n    if isinstance(support_dict, str):\n        return True\n    else:\n        return TORCH_VERSION >= version.parse(support_dict[\"min_version\"])\n\n\ndef create_torch_ast() -> Globals:\n    ast = Globals()\n\n    # most methods work in all versions and have a single return type\n    # for the more complicated ones we pass a dict with keys like return_type and\n    # min_version\n    for method, return_type_name_or_dict in allowlist.items():\n        if version_supported(support_dict=return_type_name_or_dict):\n            return_type = get_return_type(support_dict=return_type_name_or_dict)\n            if return_type == \"unknown\":\n                # this allows us to import them for testing\n                continue\n            ast.add_path(\n                path=method, framework_reference=torch, return_type_name=return_type\n            )\n            # add all the torch.nn.Parameter hooks\n            if method.startswith(\"torch.Tensor.\"):\n                method = method.replace(\"torch.Tensor.\", \"torch.nn.Parameter.\")\n                return_type = return_type.replace(\"torch.Tensor\", \"torch.nn.Parameter\")\n                ast.add_path(\n                    path=method, framework_reference=torch, return_type_name=return_type\n                )\n        else:\n            print(f\"Skipping torch.{method} not supported in {TORCH_VERSION}\")\n\n    for klass in ast.classes:\n        klass.create_pointer_class()\n        klass.create_send_method()\n        klass.create_serialization_methods()\n        klass.create_storable_object_attr_convenience_methods()\n    return ast\n", "path": "src/syft/lib/torch/__init__.py"}], "after_files": [{"content": "# stdlib\nfrom typing import Dict\nfrom typing import Union\n\n# third party\nfrom packaging import version\nimport torch\n\n# syft relative\nfrom . import parameter  # noqa: 401\nfrom . import uppercase_tensor  # noqa: 401\nfrom ...ast.globals import Globals\nfrom .allowlist import allowlist\n\nTORCH_VERSION = version.parse(torch.__version__.split(\"+\")[0])\n\n\ndef get_return_type(support_dict: Union[str, Dict[str, str]]) -> str:\n    if isinstance(support_dict, str):\n        return support_dict\n    else:\n        return support_dict[\"return_type\"]\n\n\ndef version_supported(support_dict: Union[str, Dict[str, str]]) -> bool:\n    if isinstance(support_dict, str):\n        return True\n    else:\n        return TORCH_VERSION >= version.parse(support_dict[\"min_version\"])\n\n\ndef create_torch_ast() -> Globals:\n    ast = Globals()\n\n    # most methods work in all versions and have a single return type\n    # for the more complicated ones we pass a dict with keys like return_type and\n    # min_version\n    for method, return_type_name_or_dict in allowlist.items():\n        if version_supported(support_dict=return_type_name_or_dict):\n            return_type = get_return_type(support_dict=return_type_name_or_dict)\n            if return_type == \"unknown\":\n                # this allows us to import them for testing\n                continue\n            ast.add_path(\n                path=method, framework_reference=torch, return_type_name=return_type\n            )\n            # add all the torch.nn.Parameter hooks\n            if method.startswith(\"torch.Tensor.\"):\n                method = method.replace(\"torch.Tensor.\", \"torch.nn.Parameter.\")\n                return_type = return_type.replace(\"torch.Tensor\", \"torch.nn.Parameter\")\n                ast.add_path(\n                    path=method, framework_reference=torch, return_type_name=return_type\n                )\n        else:\n            print(f\"Skipping torch.{method} not supported in {TORCH_VERSION}\")\n\n    for klass in ast.classes:\n        klass.create_pointer_class()\n        klass.create_send_method()\n        klass.create_serialization_methods()\n        klass.create_storable_object_attr_convenience_methods()\n    return ast\n", "path": "src/syft/lib/torch/__init__.py"}]}