prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  [DOC] Documentation for sigmoid focal loss
  ## 📚 Documentation
  
  Sigmoid focal loss was added to torchvision, but I think docs are not generated for it
  
  It was initalized in `init` as well [here](https://github.com/pytorch/vision/blob/v0.8.0/torchvision/ops/__init__.py#L11)
  
  I think it's really minor fix, we might need `..autodoc::sigmoid_focal_loss` and it should be fine.
  
  </issue>
  <code>
  [start of torchvision/ops/focal_loss.py]
  1 import torch
  2 import torch.nn.functional as F
  3 
  4 
  5 def sigmoid_focal_loss(
  6     inputs,
  7     targets,
  8     alpha: float = 0.25,
  9     gamma: float = 2,
  10     reduction: str = "none",
  11 ):
  12     """
  13     Original implementation from https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/focal_loss.py .
  14     Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
  15     Args:
  16         inputs: A float tensor of arbitrary shape.
  17                 The predictions for each example.
  18         targets: A float tensor with the same shape as inputs. Stores the binary
  19                  classification label for each element in inputs
  20                 (0 for the negative class and 1 for the positive class).
  21         alpha: (optional) Weighting factor in range (0,1) to balance
  22                 positive vs negative examples or -1 for ignore. Default = 0.25
  23         gamma: Exponent of the modulating factor (1 - p_t) to
  24                balance easy vs hard examples.
  25         reduction: 'none' | 'mean' | 'sum'
  26                  'none': No reduction will be applied to the output.
  27                  'mean': The output will be averaged.
  28                  'sum': The output will be summed.
  29     Returns:
  30         Loss tensor with the reduction option applied.
  31     """
  32     p = torch.sigmoid(inputs)
  33     ce_loss = F.binary_cross_entropy_with_logits(
  34         inputs, targets, reduction="none"
  35     )
  36     p_t = p * targets + (1 - p) * (1 - targets)
  37     loss = ce_loss * ((1 - p_t) ** gamma)
  38 
  39     if alpha >= 0:
  40         alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
  41         loss = alpha_t * loss
  42 
  43     if reduction == "mean":
  44         loss = loss.mean()
  45     elif reduction == "sum":
  46         loss = loss.sum()
  47 
  48     return loss
  49
  [end of torchvision/ops/focal_loss.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/torchvision/ops/focal_loss.py b/torchvision/ops/focal_loss.py
  --- a/torchvision/ops/focal_loss.py
  +++ b/torchvision/ops/focal_loss.py
  @@ -3,8 +3,8 @@
   
   
   def sigmoid_focal_loss(
  -    inputs,
  -    targets,
  +    inputs: torch.Tensor,
  +    targets: torch.Tensor,
       alpha: float = 0.25,
       gamma: float = 2,
       reduction: str = "none",
  @@ -12,11 +12,12 @@
       """
       Original implementation from https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/focal_loss.py .
       Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
  -    Args:
  +
  +    Arguments:
           inputs: A float tensor of arbitrary shape.
                   The predictions for each example.
           targets: A float tensor with the same shape as inputs. Stores the binary
  -                 classification label for each element in inputs
  +                classification label for each element in inputs
                   (0 for the negative class and 1 for the positive class).
           alpha: (optional) Weighting factor in range (0,1) to balance
                   positive vs negative examples or -1 for ignore. Default = 0.25
  
verification_info: {"golden_diff": "diff --git a/torchvision/ops/focal_loss.py b/torchvision/ops/focal_loss.py\n--- a/torchvision/ops/focal_loss.py\n+++ b/torchvision/ops/focal_loss.py\n@@ -3,8 +3,8 @@\n \n \n def sigmoid_focal_loss(\n-    inputs,\n-    targets,\n+    inputs: torch.Tensor,\n+    targets: torch.Tensor,\n     alpha: float = 0.25,\n     gamma: float = 2,\n     reduction: str = \"none\",\n@@ -12,11 +12,12 @@\n     \"\"\"\n     Original implementation from https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/focal_loss.py .\n     Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.\n-    Args:\n+\n+    Arguments:\n         inputs: A float tensor of arbitrary shape.\n                 The predictions for each example.\n         targets: A float tensor with the same shape as inputs. Stores the binary\n-                 classification label for each element in inputs\n+                classification label for each element in inputs\n                 (0 for the negative class and 1 for the positive class).\n         alpha: (optional) Weighting factor in range (0,1) to balance\n                 positive vs negative examples or -1 for ignore. Default = 0.25\n", "issue": "[DOC] Documentation for sigmoid focal loss\n## \ud83d\udcda Documentation\r\n\r\nSigmoid focal loss was added to torchvision, but I think docs are not generated for it\r\n\r\nIt was initalized in `init` as well [here](https://github.com/pytorch/vision/blob/v0.8.0/torchvision/ops/__init__.py#L11)\r\n\r\nI think it's really minor fix, we might need `..autodoc::sigmoid_focal_loss` and it should be fine.\n", "before_files": [{"content": "import torch\nimport torch.nn.functional as F\n\n\ndef sigmoid_focal_loss(\n    inputs,\n    targets,\n    alpha: float = 0.25,\n    gamma: float = 2,\n    reduction: str = \"none\",\n):\n    \"\"\"\n    Original implementation from https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/focal_loss.py .\n    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.\n    Args:\n        inputs: A float tensor of arbitrary shape.\n                The predictions for each example.\n        targets: A float tensor with the same shape as inputs. Stores the binary\n                 classification label for each element in inputs\n                (0 for the negative class and 1 for the positive class).\n        alpha: (optional) Weighting factor in range (0,1) to balance\n                positive vs negative examples or -1 for ignore. Default = 0.25\n        gamma: Exponent of the modulating factor (1 - p_t) to\n               balance easy vs hard examples.\n        reduction: 'none' | 'mean' | 'sum'\n                 'none': No reduction will be applied to the output.\n                 'mean': The output will be averaged.\n                 'sum': The output will be summed.\n    Returns:\n        Loss tensor with the reduction option applied.\n    \"\"\"\n    p = torch.sigmoid(inputs)\n    ce_loss = F.binary_cross_entropy_with_logits(\n        inputs, targets, reduction=\"none\"\n    )\n    p_t = p * targets + (1 - p) * (1 - targets)\n    loss = ce_loss * ((1 - p_t) ** gamma)\n\n    if alpha >= 0:\n        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)\n        loss = alpha_t * loss\n\n    if reduction == \"mean\":\n        loss = loss.mean()\n    elif reduction == \"sum\":\n        loss = loss.sum()\n\n    return loss\n", "path": "torchvision/ops/focal_loss.py"}]}