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()

```

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

{"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"}]}