We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Docstring of Canberra metric warning
Following this comment 


> @sdesrozis could you please investigate why there is a warning here : https://travis-ci.org/github/pytorch/ignite/jobs/730492404#L2924 
thanks !
> Doc link is apparently badly rendered : https://pytorch.org/ignite/master/contrib/metrics.html#ignite.contrib.metrics.regression.CanberraMetric
> _Originally posted by @vfdev-5 in https://github.com/pytorch/ignite/pull/1314#issuecomment-699506241_

Namespace are shared so reference should be unique


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `ignite/contrib/metrics/regression/canberra_metric.py`
Content:
```
1 from typing import Callable, Union
2 
3 import torch
4 
5 from ignite.contrib.metrics.regression._base import _BaseRegression
6 from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce
7 
8 
9 class CanberraMetric(_BaseRegression):
10     r"""
11     Calculates the Canberra Metric.
12 
13     :math:`\text{CM} = \sum_{j=1}^n\frac{|A_j - P_j|}{|A_j| + |P_j|}`
14 
15     where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value.
16 
17     More details can be found in `Botchkarev 2018`_ or `scikit-learn distance metrics`_
18 
19     - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
20     - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.
21 
22     .. _Botchkarev 2018: https://arxiv.org/abs/1809.03006
23     .. _scikit-learn distance metrics:
24         https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html
25 
26     """
27 
28     def __init__(
29         self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu")
30     ):
31         self._sum_of_errors = None
32         super(CanberraMetric, self).__init__(output_transform, device)
33 
34     @reinit__is_reduced
35     def reset(self):
36         self._sum_of_errors = torch.tensor(0.0, device=self._device)
37 
38     def _update(self, output):
39         y_pred, y = output
40         errors = torch.abs(y - y_pred) / (torch.abs(y_pred) + torch.abs(y))
41         self._sum_of_errors += torch.sum(errors).to(self._device)
42 
43     @sync_all_reduce("_sum_of_errors")
44     def compute(self):
45         return self._sum_of_errors.item()
46
```
--- 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/ignite/contrib/metrics/regression/canberra_metric.py b/ignite/contrib/metrics/regression/canberra_metric.py
--- a/ignite/contrib/metrics/regression/canberra_metric.py
+++ b/ignite/contrib/metrics/regression/canberra_metric.py
@@ -19,7 +19,6 @@
     - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
     - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.
 
-    .. _Botchkarev 2018: https://arxiv.org/abs/1809.03006
     .. _scikit-learn distance metrics:
         https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html

{"golden_diff": "diff --git a/ignite/contrib/metrics/regression/canberra_metric.py b/ignite/contrib/metrics/regression/canberra_metric.py\n--- a/ignite/contrib/metrics/regression/canberra_metric.py\n+++ b/ignite/contrib/metrics/regression/canberra_metric.py\n@@ -19,7 +19,6 @@\n     - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.\n     - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.\n \n-    .. _Botchkarev 2018: https://arxiv.org/abs/1809.03006\n     .. _scikit-learn distance metrics:\n         https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html\n", "issue": "Docstring of Canberra metric warning\nFollowing this comment \r\n\r\n\r\n> @sdesrozis could you please investigate why there is a warning here : https://travis-ci.org/github/pytorch/ignite/jobs/730492404#L2924 \r\nthanks !\r\n> Doc link is apparently badly rendered : https://pytorch.org/ignite/master/contrib/metrics.html#ignite.contrib.metrics.regression.CanberraMetric\r\n> _Originally posted by @vfdev-5 in https://github.com/pytorch/ignite/pull/1314#issuecomment-699506241_\r\n\r\nNamespace are shared so reference should be unique\r\n\n", "before_files": [{"content": "from typing import Callable, Union\n\nimport torch\n\nfrom ignite.contrib.metrics.regression._base import _BaseRegression\nfrom ignite.metrics.metric import reinit__is_reduced, sync_all_reduce\n\n\nclass CanberraMetric(_BaseRegression):\n    r\"\"\"\n    Calculates the Canberra Metric.\n\n    :math:`\\text{CM} = \\sum_{j=1}^n\\frac{|A_j - P_j|}{|A_j| + |P_j|}`\n\n    where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value.\n\n    More details can be found in `Botchkarev 2018`_ or `scikit-learn distance metrics`_\n\n    - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.\n    - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.\n\n    .. _Botchkarev 2018: https://arxiv.org/abs/1809.03006\n    .. _scikit-learn distance metrics:\n        https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html\n\n    \"\"\"\n\n    def __init__(\n        self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device(\"cpu\")\n    ):\n        self._sum_of_errors = None\n        super(CanberraMetric, self).__init__(output_transform, device)\n\n    @reinit__is_reduced\n    def reset(self):\n        self._sum_of_errors = torch.tensor(0.0, device=self._device)\n\n    def _update(self, output):\n        y_pred, y = output\n        errors = torch.abs(y - y_pred) / (torch.abs(y_pred) + torch.abs(y))\n        self._sum_of_errors += torch.sum(errors).to(self._device)\n\n    @sync_all_reduce(\"_sum_of_errors\")\n    def compute(self):\n        return self._sum_of_errors.item()\n", "path": "ignite/contrib/metrics/regression/canberra_metric.py"}], "after_files": [{"content": "from typing import Callable, Union\n\nimport torch\n\nfrom ignite.contrib.metrics.regression._base import _BaseRegression\nfrom ignite.metrics.metric import reinit__is_reduced, sync_all_reduce\n\n\nclass CanberraMetric(_BaseRegression):\n    r\"\"\"\n    Calculates the Canberra Metric.\n\n    :math:`\\text{CM} = \\sum_{j=1}^n\\frac{|A_j - P_j|}{|A_j| + |P_j|}`\n\n    where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value.\n\n    More details can be found in `Botchkarev 2018`_ or `scikit-learn distance metrics`_\n\n    - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.\n    - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.\n\n    .. _scikit-learn distance metrics:\n        https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html\n\n    \"\"\"\n\n    def __init__(\n        self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device(\"cpu\")\n    ):\n        self._sum_of_errors = None\n        super(CanberraMetric, self).__init__(output_transform, device)\n\n    @reinit__is_reduced\n    def reset(self):\n        self._sum_of_errors = torch.tensor(0.0, device=self._device)\n\n    def _update(self, output):\n        y_pred, y = output\n        errors = torch.abs(y - y_pred) / (torch.abs(y_pred) + torch.abs(y))\n        self._sum_of_errors += torch.sum(errors).to(self._device)\n\n    @sync_all_reduce(\"_sum_of_errors\")\n    def compute(self):\n        return self._sum_of_errors.item()\n", "path": "ignite/contrib/metrics/regression/canberra_metric.py"}]}