We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Add DCGAN example Duet Notebooks
## Description
Add two notebook's which reflect the DCGAN example split into DO (Data Owner) and DS (Data Scientist):
https://github.com/pytorch/examples/blob/master/dcgan/

## Definition of Done
The partially runnable DCGAN example notebooks should be in the examples/duet/dcgan folder and a README.md should be added in the parent examples/duet directory with a link to the original example and our notebook.
Add Super Resolution Example Duet Notebooks
## Description
Add two notebook's which reflect the Super Resolution example split into DO (Data Owner) and DS (Data Scientist):
https://github.com/pytorch/examples/blob/master/super_resolution/

## Definition of Done
The partially runnable Super Resolution example notebooks should be in the examples/duet/super_resolution folder and a README.md should be added in the parent examples/duet directory with a link to the original example and our notebook.
Add DCGAN example Duet Notebooks
## Description
Add two notebook's which reflect the DCGAN example split into DO (Data Owner) and DS (Data Scientist):
https://github.com/pytorch/examples/blob/master/dcgan/

## Definition of Done
The partially runnable DCGAN example notebooks should be in the examples/duet/dcgan folder and a README.md should be added in the parent examples/duet directory with a link to the original example and our notebook.

--- 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 Any
3 from typing import Dict
4 from typing import Union
5 
6 # third party
7 from packaging import version
8 import torch
9 
10 # syft relative
11 from . import device  # noqa: 401
12 from . import parameter  # noqa: 401
13 from . import uppercase_tensor  # noqa: 401
14 from ...ast.globals import Globals
15 from ...logger import critical
16 from .allowlist import allowlist
17 
18 TORCH_VERSION = version.parse(torch.__version__.split("+")[0])
19 
20 
21 def get_return_type(support_dict: Union[str, Dict[str, str]]) -> str:
22     if isinstance(support_dict, str):
23         return support_dict
24     else:
25         return support_dict["return_type"]
26 
27 
28 def version_supported(support_dict: Union[str, Dict[str, str]]) -> bool:
29     if isinstance(support_dict, str):
30         return True
31     else:
32         # if we are on either side of the min or max versions we don't support this op
33         if "min_version" in support_dict and TORCH_VERSION < version.parse(
34             support_dict["min_version"]
35         ):
36             return False
37         if "max_version" in support_dict and TORCH_VERSION > version.parse(
38             support_dict["max_version"]
39         ):
40             return False
41         return True
42 
43 
44 def create_torch_ast(client: Any = None) -> Globals:
45     ast = Globals(client)
46 
47     # most methods work in all versions and have a single return type
48     # for the more complicated ones we pass a dict with keys like return_type and
49     # min_version
50     for method, return_type_name_or_dict in allowlist.items():
51         if version_supported(support_dict=return_type_name_or_dict):
52             return_type = get_return_type(support_dict=return_type_name_or_dict)
53             if return_type == "unknown":
54                 # this allows us to import them for testing
55                 continue
56             ast.add_path(
57                 path=method, framework_reference=torch, return_type_name=return_type
58             )
59             # add all the torch.nn.Parameter hooks
60             if method.startswith("torch.Tensor."):
61                 method = method.replace("torch.Tensor.", "torch.nn.Parameter.")
62                 return_type = return_type.replace("torch.Tensor", "torch.nn.Parameter")
63                 ast.add_path(
64                     path=method, framework_reference=torch, return_type_name=return_type
65                 )
66         else:
67             critical(f"Skipping {method} not supported in {TORCH_VERSION}")
68 
69     for klass in ast.classes:
70         klass.create_pointer_class()
71         klass.create_send_method()
72         klass.create_storable_object_attr_convenience_methods()
73     return ast
74
```
--- 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 . import parameter  # noqa: 401
 from . import uppercase_tensor  # noqa: 401
 from ...ast.globals import Globals
-from ...logger import critical
+from ...logger import info
 from .allowlist import allowlist
 
 TORCH_VERSION = version.parse(torch.__version__.split("+")[0])
@@ -64,7 +64,7 @@
                     path=method, framework_reference=torch, return_type_name=return_type
                 )
         else:
-            critical(f"Skipping {method} not supported in {TORCH_VERSION}")
+            info(f"Skipping {method} not supported in {TORCH_VERSION}")
 
     for klass in ast.classes:
         klass.create_pointer_class()

{"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 . import parameter  # noqa: 401\n from . import uppercase_tensor  # noqa: 401\n from ...ast.globals import Globals\n-from ...logger import critical\n+from ...logger import info\n from .allowlist import allowlist\n \n TORCH_VERSION = version.parse(torch.__version__.split(\"+\")[0])\n@@ -64,7 +64,7 @@\n                     path=method, framework_reference=torch, return_type_name=return_type\n                 )\n         else:\n-            critical(f\"Skipping {method} not supported in {TORCH_VERSION}\")\n+            info(f\"Skipping {method} not supported in {TORCH_VERSION}\")\n \n     for klass in ast.classes:\n         klass.create_pointer_class()\n", "issue": "Add DCGAN example Duet Notebooks\n## Description\r\nAdd two notebook's which reflect the DCGAN example split into DO (Data Owner) and DS (Data Scientist):\r\nhttps://github.com/pytorch/examples/blob/master/dcgan/\r\n\r\n## Definition of Done\r\nThe partially runnable DCGAN example notebooks should be in the examples/duet/dcgan folder and a README.md should be added in the parent examples/duet directory with a link to the original example and our notebook.\nAdd Super Resolution Example Duet Notebooks\n## Description\r\nAdd two notebook's which reflect the Super Resolution example split into DO (Data Owner) and DS (Data Scientist):\r\nhttps://github.com/pytorch/examples/blob/master/super_resolution/\r\n\r\n## Definition of Done\r\nThe partially runnable Super Resolution example notebooks should be in the examples/duet/super_resolution folder and a README.md should be added in the parent examples/duet directory with a link to the original example and our notebook.\nAdd DCGAN example Duet Notebooks\n## Description\r\nAdd two notebook's which reflect the DCGAN example split into DO (Data Owner) and DS (Data Scientist):\r\nhttps://github.com/pytorch/examples/blob/master/dcgan/\r\n\r\n## Definition of Done\r\nThe partially runnable DCGAN example notebooks should be in the examples/duet/dcgan folder and a README.md should be added in the parent examples/duet directory with a link to the original example and our notebook.\n", "before_files": [{"content": "# stdlib\nfrom typing import Any\nfrom typing import Dict\nfrom typing import Union\n\n# third party\nfrom packaging import version\nimport torch\n\n# syft relative\nfrom . import device  # noqa: 401\nfrom . import parameter  # noqa: 401\nfrom . import uppercase_tensor  # noqa: 401\nfrom ...ast.globals import Globals\nfrom ...logger import critical\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        # if we are on either side of the min or max versions we don't support this op\n        if \"min_version\" in support_dict and TORCH_VERSION < version.parse(\n            support_dict[\"min_version\"]\n        ):\n            return False\n        if \"max_version\" in support_dict and TORCH_VERSION > version.parse(\n            support_dict[\"max_version\"]\n        ):\n            return False\n        return True\n\n\ndef create_torch_ast(client: Any = None) -> Globals:\n    ast = Globals(client)\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            critical(f\"Skipping {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_storable_object_attr_convenience_methods()\n    return ast\n", "path": "src/syft/lib/torch/__init__.py"}], "after_files": [{"content": "# stdlib\nfrom typing import Any\nfrom typing import Dict\nfrom typing import Union\n\n# third party\nfrom packaging import version\nimport torch\n\n# syft relative\nfrom . import device  # noqa: 401\nfrom . import parameter  # noqa: 401\nfrom . import uppercase_tensor  # noqa: 401\nfrom ...ast.globals import Globals\nfrom ...logger import info\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        # if we are on either side of the min or max versions we don't support this op\n        if \"min_version\" in support_dict and TORCH_VERSION < version.parse(\n            support_dict[\"min_version\"]\n        ):\n            return False\n        if \"max_version\" in support_dict and TORCH_VERSION > version.parse(\n            support_dict[\"max_version\"]\n        ):\n            return False\n        return True\n\n\ndef create_torch_ast(client: Any = None) -> Globals:\n    ast = Globals(client)\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            info(f\"Skipping {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_storable_object_attr_convenience_methods()\n    return ast\n", "path": "src/syft/lib/torch/__init__.py"}]}