prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <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.
  
  </issue>
  <code>
  [start of src/syft/lib/torch/__init__.py]
  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 of src/syft/lib/torch/__init__.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/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()
  
verification_info: {"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"}]}