prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  DOC: Reduce size image in plot_inpaint
  In reference to #1920 
  - Reduce the size of the image in  doc/examples/filters/plot_inpaint.py to show more clearly the result of the algorithm.
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `doc/examples/filters/plot_inpaint.py`
  Content:
  ```
  1 """
  2 ===========
  3 Inpainting
  4 ===========
  5 Inpainting [1]_ is the process of reconstructing lost or deteriorated
  6 parts of images and videos.
  7 
  8 The reconstruction is supposed to be performed in fully automatic way by
  9 exploiting the information presented in non-damaged regions.
  10 
  11 In this example, we show how the masked pixels get inpainted by
  12 inpainting algorithm based on 'biharmonic equation'-assumption [2]_ [3]_.
  13 
  14 .. [1]  Wikipedia. Inpainting
  15         https://en.wikipedia.org/wiki/Inpainting
  16 .. [2]  Wikipedia. Biharmonic equation
  17         https://en.wikipedia.org/wiki/Biharmonic_equation
  18 .. [3]  N.S.Hoang, S.B.Damelin, "On surface completion and image
  19         inpainting by biharmonic functions: numerical aspects",
  20         http://www.ima.umn.edu/~damelin/biharmonic
  21 """
  22 
  23 import numpy as np
  24 import matplotlib.pyplot as plt
  25 
  26 from skimage import data, color
  27 from skimage.restoration import inpaint
  28 
  29 image_orig = data.astronaut()
  30 
  31 # Create mask with three defect regions: left, middle, right respectively
  32 mask = np.zeros(image_orig.shape[:-1])
  33 mask[20:60, 0:20] = 1
  34 mask[200:300, 150:170] = 1
  35 mask[50:100, 400:430] = 1
  36 
  37 # Defect image over the same region in each color channel
  38 image_defect = image_orig.copy()
  39 for layer in range(image_defect.shape[-1]):
  40     image_defect[np.where(mask)] = 0
  41 
  42 image_result = inpaint.inpaint_biharmonic(image_defect, mask, multichannel=True)
  43 
  44 fig, axes = plt.subplots(ncols=2, nrows=2)
  45 ax0, ax1, ax2, ax3 = axes.ravel()
  46 
  47 ax0.set_title('Original image')
  48 ax0.imshow(image_orig)
  49 ax0.axis('off')
  50 
  51 ax1.set_title('Mask')
  52 ax1.imshow(mask, cmap=plt.cm.gray)
  53 ax1.axis('off')
  54 
  55 ax2.set_title('Defected image')
  56 ax2.imshow(image_defect)
  57 ax2.axis('off')
  58 
  59 ax3.set_title('Inpainted image')
  60 ax3.imshow(image_result)
  61 ax3.axis('off')
  62 
  63 plt.tight_layout()
  64 plt.show()
  65
  ```
  --- 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/doc/examples/filters/plot_inpaint.py b/doc/examples/filters/plot_inpaint.py
  --- a/doc/examples/filters/plot_inpaint.py
  +++ b/doc/examples/filters/plot_inpaint.py
  @@ -26,13 +26,13 @@
   from skimage import data, color
   from skimage.restoration import inpaint
   
  -image_orig = data.astronaut()
  +image_orig = data.astronaut()[0:200, 0:200]
   
   # Create mask with three defect regions: left, middle, right respectively
   mask = np.zeros(image_orig.shape[:-1])
   mask[20:60, 0:20] = 1
  -mask[200:300, 150:170] = 1
  -mask[50:100, 400:430] = 1
  +mask[160:180, 70:155] = 1
  +mask[30:60, 170:195] = 1
   
   # Defect image over the same region in each color channel
   image_defect = image_orig.copy()
  @@ -60,5 +60,5 @@
   ax3.imshow(image_result)
   ax3.axis('off')
   
  -plt.tight_layout()
  +fig.tight_layout()
   plt.show()
  
verification_info: {"golden_diff": "diff --git a/doc/examples/filters/plot_inpaint.py b/doc/examples/filters/plot_inpaint.py\n--- a/doc/examples/filters/plot_inpaint.py\n+++ b/doc/examples/filters/plot_inpaint.py\n@@ -26,13 +26,13 @@\n from skimage import data, color\n from skimage.restoration import inpaint\n \n-image_orig = data.astronaut()\n+image_orig = data.astronaut()[0:200, 0:200]\n \n # Create mask with three defect regions: left, middle, right respectively\n mask = np.zeros(image_orig.shape[:-1])\n mask[20:60, 0:20] = 1\n-mask[200:300, 150:170] = 1\n-mask[50:100, 400:430] = 1\n+mask[160:180, 70:155] = 1\n+mask[30:60, 170:195] = 1\n \n # Defect image over the same region in each color channel\n image_defect = image_orig.copy()\n@@ -60,5 +60,5 @@\n ax3.imshow(image_result)\n ax3.axis('off')\n \n-plt.tight_layout()\n+fig.tight_layout()\n plt.show()\n", "issue": "DOC: Reduce size image in plot_inpaint\nIn reference to #1920 \n- Reduce the size of the image in  doc/examples/filters/plot_inpaint.py to show more clearly the result of the algorithm.\n\n", "before_files": [{"content": "\"\"\"\n===========\nInpainting\n===========\nInpainting [1]_ is the process of reconstructing lost or deteriorated\nparts of images and videos.\n\nThe reconstruction is supposed to be performed in fully automatic way by\nexploiting the information presented in non-damaged regions.\n\nIn this example, we show how the masked pixels get inpainted by\ninpainting algorithm based on 'biharmonic equation'-assumption [2]_ [3]_.\n\n.. [1]  Wikipedia. Inpainting\n        https://en.wikipedia.org/wiki/Inpainting\n.. [2]  Wikipedia. Biharmonic equation\n        https://en.wikipedia.org/wiki/Biharmonic_equation\n.. [3]  N.S.Hoang, S.B.Damelin, \"On surface completion and image\n        inpainting by biharmonic functions: numerical aspects\",\n        http://www.ima.umn.edu/~damelin/biharmonic\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom skimage import data, color\nfrom skimage.restoration import inpaint\n\nimage_orig = data.astronaut()\n\n# Create mask with three defect regions: left, middle, right respectively\nmask = np.zeros(image_orig.shape[:-1])\nmask[20:60, 0:20] = 1\nmask[200:300, 150:170] = 1\nmask[50:100, 400:430] = 1\n\n# Defect image over the same region in each color channel\nimage_defect = image_orig.copy()\nfor layer in range(image_defect.shape[-1]):\n    image_defect[np.where(mask)] = 0\n\nimage_result = inpaint.inpaint_biharmonic(image_defect, mask, multichannel=True)\n\nfig, axes = plt.subplots(ncols=2, nrows=2)\nax0, ax1, ax2, ax3 = axes.ravel()\n\nax0.set_title('Original image')\nax0.imshow(image_orig)\nax0.axis('off')\n\nax1.set_title('Mask')\nax1.imshow(mask, cmap=plt.cm.gray)\nax1.axis('off')\n\nax2.set_title('Defected image')\nax2.imshow(image_defect)\nax2.axis('off')\n\nax3.set_title('Inpainted image')\nax3.imshow(image_result)\nax3.axis('off')\n\nplt.tight_layout()\nplt.show()\n", "path": "doc/examples/filters/plot_inpaint.py"}], "after_files": [{"content": "\"\"\"\n===========\nInpainting\n===========\nInpainting [1]_ is the process of reconstructing lost or deteriorated\nparts of images and videos.\n\nThe reconstruction is supposed to be performed in fully automatic way by\nexploiting the information presented in non-damaged regions.\n\nIn this example, we show how the masked pixels get inpainted by\ninpainting algorithm based on 'biharmonic equation'-assumption [2]_ [3]_.\n\n.. [1]  Wikipedia. Inpainting\n        https://en.wikipedia.org/wiki/Inpainting\n.. [2]  Wikipedia. Biharmonic equation\n        https://en.wikipedia.org/wiki/Biharmonic_equation\n.. [3]  N.S.Hoang, S.B.Damelin, \"On surface completion and image\n        inpainting by biharmonic functions: numerical aspects\",\n        http://www.ima.umn.edu/~damelin/biharmonic\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom skimage import data, color\nfrom skimage.restoration import inpaint\n\nimage_orig = data.astronaut()[0:200, 0:200]\n\n# Create mask with three defect regions: left, middle, right respectively\nmask = np.zeros(image_orig.shape[:-1])\nmask[20:60, 0:20] = 1\nmask[160:180, 70:155] = 1\nmask[30:60, 170:195] = 1\n\n# Defect image over the same region in each color channel\nimage_defect = image_orig.copy()\nfor layer in range(image_defect.shape[-1]):\n    image_defect[np.where(mask)] = 0\n\nimage_result = inpaint.inpaint_biharmonic(image_defect, mask, multichannel=True)\n\nfig, axes = plt.subplots(ncols=2, nrows=2)\nax0, ax1, ax2, ax3 = axes.ravel()\n\nax0.set_title('Original image')\nax0.imshow(image_orig)\nax0.axis('off')\n\nax1.set_title('Mask')\nax1.imshow(mask, cmap=plt.cm.gray)\nax1.axis('off')\n\nax2.set_title('Defected image')\nax2.imshow(image_defect)\nax2.axis('off')\n\nax3.set_title('Inpainted image')\nax3.imshow(image_result)\nax3.axis('off')\n\nfig.tight_layout()\nplt.show()\n", "path": "doc/examples/filters/plot_inpaint.py"}]}