prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN 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.
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `torchvision/ops/focal_loss.py`
  Content:
  ```
  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 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()
  
  ```
  
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"}], "after_files": [{"content": "import torch\nimport torch.nn.functional as F\n\n\ndef sigmoid_focal_loss(\n    inputs: torch.Tensor,\n    targets: torch.Tensor,\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\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                (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"}]}