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