We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Resampling Maps in the example gallery gives a confusing example for the superpixel method
### Provide a general description of the issue or problem.

That's a minor thing perhaps but checking this [page](https://docs.sunpy.org/en/stable/generated/gallery/map/map_resampling_and_superpixels.html) I got confused by the example for the superpixel method. 
It says:
`new_dimensions = u.Quantity(aia_map.dimensions) / 16`
`aia_superpixel_map = aia_map.superpixel([new_dimensions]`

The first line should be instead e.g.:
`new_dimensions=[16,16]*u.pixel `

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `examples/map/map_resampling_and_superpixels.py`
Content:
```
1 """
2 ===============
3 Resampling Maps
4 ===============
5 
6 How to resample a map using the resample method, which implements interpolation, or
7 using superpixels, which combines pixels.
8 """
9 import matplotlib.pyplot as plt
10 
11 import astropy.units as u
12 
13 import sunpy.data.sample
14 import sunpy.map
15 
16 ###############################################################################
17 # We start with the sample data.
18 
19 aia_map = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE)
20 
21 ##############################################################################
22 # To reduce the angular resolution of the map you can use the `~sunpy.map.GenericMap.resample` method,
23 # specifying the new dimensions in pixels. By default, this method uses linear interpolation
24 # but this can be changed with the ``method`` argument ('nearest', 'linear' or 'spline').
25 
26 new_dimensions = [40, 40] * u.pixel
27 aia_resampled_map = aia_map.resample(new_dimensions)
28 
29 ##############################################################################
30 # Let's plot the result.
31 
32 fig = plt.figure()
33 ax = fig.add_subplot(projection=aia_resampled_map)
34 aia_resampled_map.plot(axes=ax)
35 plt.show()
36 
37 ##############################################################################
38 # Another way to resample is by using the `~sunpy.map.GenericMap.superpixel` method.
39 # This can be used to increase the signal to noise ratio by reducing the
40 # resolution of the image by combining pixels. This means that the new dimension
41 # must divide the original size exactly.
42 # For example you can reduce the AIA map resolution by a factor of 16.
43 
44 new_dimensions = u.Quantity(aia_map.dimensions) / 16
45 aia_superpixel_map = aia_map.superpixel(new_dimensions)
46 
47 ##############################################################################
48 # Let's plot the result.
49 
50 fig = plt.figure()
51 ax = fig.add_subplot(projection=aia_superpixel_map)
52 aia_superpixel_map.plot(axes=ax)
53 plt.show()
54
```
--- 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/examples/map/map_resampling_and_superpixels.py b/examples/map/map_resampling_and_superpixels.py
--- a/examples/map/map_resampling_and_superpixels.py
+++ b/examples/map/map_resampling_and_superpixels.py
@@ -13,15 +13,16 @@
 import sunpy.data.sample
 import sunpy.map
 
-###############################################################################
+##############################################################################
 # We start with the sample data.
 
 aia_map = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE)
 
 ##############################################################################
-# To reduce the angular resolution of the map you can use the `~sunpy.map.GenericMap.resample` method,
-# specifying the new dimensions in pixels. By default, this method uses linear interpolation
-# but this can be changed with the ``method`` argument ('nearest', 'linear' or 'spline').
+# To reduce the angular resolution of the map, you can use the
+# :meth:`~sunpy.map.GenericMap.resample` method, specifying the new dimensions
+# in pixels. By default, this method uses linear interpolation but this can be
+# changed with the ``method`` argument ('nearest', 'linear' or 'spline').
 
 new_dimensions = [40, 40] * u.pixel
 aia_resampled_map = aia_map.resample(new_dimensions)
@@ -35,14 +36,15 @@
 plt.show()
 
 ##############################################################################
-# Another way to resample is by using the `~sunpy.map.GenericMap.superpixel` method.
-# This can be used to increase the signal to noise ratio by reducing the
-# resolution of the image by combining pixels. This means that the new dimension
-# must divide the original size exactly.
-# For example you can reduce the AIA map resolution by a factor of 16.
-
-new_dimensions = u.Quantity(aia_map.dimensions) / 16
-aia_superpixel_map = aia_map.superpixel(new_dimensions)
+# Another way to reduce the angular resolution of the map is by using the
+# :meth:`~sunpy.map.GenericMap.superpixel` method, which combines pixels.
+# The superpixel dimensions do not need to be square, and the intensity of
+# each superpixel defaults to the sum of the constituent pixels. For example,
+# you can reduce the AIA map resolution by a factor of 16 by specifying 16x16
+# superpixels.
+
+superpixel_size = [16, 16] * u.pixel
+aia_superpixel_map = aia_map.superpixel(superpixel_size)
 
 ##############################################################################
 # Let's plot the result.

{"golden_diff": "diff --git a/examples/map/map_resampling_and_superpixels.py b/examples/map/map_resampling_and_superpixels.py\n--- a/examples/map/map_resampling_and_superpixels.py\n+++ b/examples/map/map_resampling_and_superpixels.py\n@@ -13,15 +13,16 @@\n import sunpy.data.sample\n import sunpy.map\n \n-###############################################################################\n+##############################################################################\n # We start with the sample data.\n \n aia_map = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE)\n \n ##############################################################################\n-# To reduce the angular resolution of the map you can use the `~sunpy.map.GenericMap.resample` method,\n-# specifying the new dimensions in pixels. By default, this method uses linear interpolation\n-# but this can be changed with the ``method`` argument ('nearest', 'linear' or 'spline').\n+# To reduce the angular resolution of the map, you can use the\n+# :meth:`~sunpy.map.GenericMap.resample` method, specifying the new dimensions\n+# in pixels. By default, this method uses linear interpolation but this can be\n+# changed with the ``method`` argument ('nearest', 'linear' or 'spline').\n \n new_dimensions = [40, 40] * u.pixel\n aia_resampled_map = aia_map.resample(new_dimensions)\n@@ -35,14 +36,15 @@\n plt.show()\n \n ##############################################################################\n-# Another way to resample is by using the `~sunpy.map.GenericMap.superpixel` method.\n-# This can be used to increase the signal to noise ratio by reducing the\n-# resolution of the image by combining pixels. This means that the new dimension\n-# must divide the original size exactly.\n-# For example you can reduce the AIA map resolution by a factor of 16.\n-\n-new_dimensions = u.Quantity(aia_map.dimensions) / 16\n-aia_superpixel_map = aia_map.superpixel(new_dimensions)\n+# Another way to reduce the angular resolution of the map is by using the\n+# :meth:`~sunpy.map.GenericMap.superpixel` method, which combines pixels.\n+# The superpixel dimensions do not need to be square, and the intensity of\n+# each superpixel defaults to the sum of the constituent pixels. For example,\n+# you can reduce the AIA map resolution by a factor of 16 by specifying 16x16\n+# superpixels.\n+\n+superpixel_size = [16, 16] * u.pixel\n+aia_superpixel_map = aia_map.superpixel(superpixel_size)\n \n ##############################################################################\n # Let's plot the result.\n", "issue": "Resampling Maps in the example gallery gives a confusing example for the superpixel method\n### Provide a general description of the issue or problem.\n\nThat's a minor thing perhaps but checking this [page](https://docs.sunpy.org/en/stable/generated/gallery/map/map_resampling_and_superpixels.html) I got confused by the example for the superpixel method. \r\nIt says:\r\n`new_dimensions = u.Quantity(aia_map.dimensions) / 16`\r\n`aia_superpixel_map = aia_map.superpixel([new_dimensions]`\r\n\r\nThe first line should be instead e.g.:\r\n`new_dimensions=[16,16]*u.pixel `\n", "before_files": [{"content": "\"\"\"\n===============\nResampling Maps\n===============\n\nHow to resample a map using the resample method, which implements interpolation, or\nusing superpixels, which combines pixels.\n\"\"\"\nimport matplotlib.pyplot as plt\n\nimport astropy.units as u\n\nimport sunpy.data.sample\nimport sunpy.map\n\n###############################################################################\n# We start with the sample data.\n\naia_map = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE)\n\n##############################################################################\n# To reduce the angular resolution of the map you can use the `~sunpy.map.GenericMap.resample` method,\n# specifying the new dimensions in pixels. By default, this method uses linear interpolation\n# but this can be changed with the ``method`` argument ('nearest', 'linear' or 'spline').\n\nnew_dimensions = [40, 40] * u.pixel\naia_resampled_map = aia_map.resample(new_dimensions)\n\n##############################################################################\n# Let's plot the result.\n\nfig = plt.figure()\nax = fig.add_subplot(projection=aia_resampled_map)\naia_resampled_map.plot(axes=ax)\nplt.show()\n\n##############################################################################\n# Another way to resample is by using the `~sunpy.map.GenericMap.superpixel` method.\n# This can be used to increase the signal to noise ratio by reducing the\n# resolution of the image by combining pixels. This means that the new dimension\n# must divide the original size exactly.\n# For example you can reduce the AIA map resolution by a factor of 16.\n\nnew_dimensions = u.Quantity(aia_map.dimensions) / 16\naia_superpixel_map = aia_map.superpixel(new_dimensions)\n\n##############################################################################\n# Let's plot the result.\n\nfig = plt.figure()\nax = fig.add_subplot(projection=aia_superpixel_map)\naia_superpixel_map.plot(axes=ax)\nplt.show()\n", "path": "examples/map/map_resampling_and_superpixels.py"}], "after_files": [{"content": "\"\"\"\n===============\nResampling Maps\n===============\n\nHow to resample a map using the resample method, which implements interpolation, or\nusing superpixels, which combines pixels.\n\"\"\"\nimport matplotlib.pyplot as plt\n\nimport astropy.units as u\n\nimport sunpy.data.sample\nimport sunpy.map\n\n##############################################################################\n# We start with the sample data.\n\naia_map = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE)\n\n##############################################################################\n# To reduce the angular resolution of the map, you can use the\n# :meth:`~sunpy.map.GenericMap.resample` method, specifying the new dimensions\n# in pixels. By default, this method uses linear interpolation but this can be\n# changed with the ``method`` argument ('nearest', 'linear' or 'spline').\n\nnew_dimensions = [40, 40] * u.pixel\naia_resampled_map = aia_map.resample(new_dimensions)\n\n##############################################################################\n# Let's plot the result.\n\nfig = plt.figure()\nax = fig.add_subplot(projection=aia_resampled_map)\naia_resampled_map.plot(axes=ax)\nplt.show()\n\n##############################################################################\n# Another way to reduce the angular resolution of the map is by using the\n# :meth:`~sunpy.map.GenericMap.superpixel` method, which combines pixels.\n# The superpixel dimensions do not need to be square, and the intensity of\n# each superpixel defaults to the sum of the constituent pixels. For example,\n# you can reduce the AIA map resolution by a factor of 16 by specifying 16x16\n# superpixels.\n\nsuperpixel_size = [16, 16] * u.pixel\naia_superpixel_map = aia_map.superpixel(superpixel_size)\n\n##############################################################################\n# Let's plot the result.\n\nfig = plt.figure()\nax = fig.add_subplot(projection=aia_superpixel_map)\naia_superpixel_map.plot(axes=ax)\nplt.show()\n", "path": "examples/map/map_resampling_and_superpixels.py"}]}