We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Fix simple typo: suports -> supports
There is a small typo in kornia/filters/laplacian.py.
Should read `supports` rather than `suports`.



--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `kornia/filters/laplacian.py`
Content:
```
1 from typing import Tuple
2 
3 import torch
4 import torch.nn as nn
5 
6 import kornia
7 from kornia.filters.kernels import get_laplacian_kernel2d
8 from kornia.filters.kernels import normalize_kernel2d
9 
10 
11 class Laplacian(nn.Module):
12     r"""Creates an operator that returns a tensor using a Laplacian filter.
13 
14     The operator smooths the given tensor with a laplacian kernel by convolving
15     it to each channel. It suports batched operation.
16 
17     Arguments:
18         kernel_size (int): the size of the kernel.
19         border_type (str): the padding mode to be applied before convolving.
20           The expected modes are: ``'constant'``, ``'reflect'``,
21           ``'replicate'`` or ``'circular'``. Default: ``'reflect'``.
22         normalized (bool): if True, L1 norm of the kernel is set to 1.
23 
24     Returns:
25         Tensor: the tensor.
26 
27     Shape:
28         - Input: :math:`(B, C, H, W)`
29         - Output: :math:`(B, C, H, W)`
30 
31     Examples::
32 
33         >>> input = torch.rand(2, 4, 5, 5)
34         >>> laplace = kornia.filters.Laplacian(5)
35         >>> output = laplace(input)  # 2x4x5x5
36     """
37 
38     def __init__(self,
39                  kernel_size: int, border_type: str = 'reflect',
40                  normalized: bool = True) -> None:
41         super(Laplacian, self).__init__()
42         self.kernel_size: int = kernel_size
43         self.border_type: str = border_type
44         self.normalized: bool = normalized
45         self.kernel: torch.Tensor = torch.unsqueeze(
46             get_laplacian_kernel2d(kernel_size), dim=0)
47         if self.normalized:
48             self.kernel = normalize_kernel2d(self.kernel)
49 
50     def __repr__(self) -> str:
51         return self.__class__.__name__ +\
52             '(kernel_size=' + str(self.kernel_size) + ', ' +\
53             'normalized=' + str(self.normalized) + ', ' + \
54             'border_type=' + self.border_type + ')'
55 
56     def forward(self, input: torch.Tensor):  # type: ignore
57         return kornia.filter2D(input, self.kernel, self.border_type)
58 
59 
60 ######################
61 # functional interface
62 ######################
63 
64 
65 def laplacian(
66         input: torch.Tensor,
67         kernel_size: int,
68         border_type: str = 'reflect',
69         normalized: bool = True) -> torch.Tensor:
70     r"""Function that returns a tensor using a Laplacian filter.
71 
72     See :class:`~kornia.filters.Laplacian` for details.
73     """
74     return Laplacian(kernel_size, border_type, normalized)(input)
75
```
--- 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/kornia/filters/laplacian.py b/kornia/filters/laplacian.py
--- a/kornia/filters/laplacian.py
+++ b/kornia/filters/laplacian.py
@@ -12,7 +12,7 @@
     r"""Creates an operator that returns a tensor using a Laplacian filter.
 
     The operator smooths the given tensor with a laplacian kernel by convolving
-    it to each channel. It suports batched operation.
+    it to each channel. It supports batched operation.
 
     Arguments:
         kernel_size (int): the size of the kernel.

{"golden_diff": "diff --git a/kornia/filters/laplacian.py b/kornia/filters/laplacian.py\n--- a/kornia/filters/laplacian.py\n+++ b/kornia/filters/laplacian.py\n@@ -12,7 +12,7 @@\n     r\"\"\"Creates an operator that returns a tensor using a Laplacian filter.\n \n     The operator smooths the given tensor with a laplacian kernel by convolving\n-    it to each channel. It suports batched operation.\n+    it to each channel. It supports batched operation.\n \n     Arguments:\n         kernel_size (int): the size of the kernel.\n", "issue": "Fix simple typo: suports -> supports\nThere is a small typo in kornia/filters/laplacian.py.\nShould read `supports` rather than `suports`.\n\n\n", "before_files": [{"content": "from typing import Tuple\n\nimport torch\nimport torch.nn as nn\n\nimport kornia\nfrom kornia.filters.kernels import get_laplacian_kernel2d\nfrom kornia.filters.kernels import normalize_kernel2d\n\n\nclass Laplacian(nn.Module):\n    r\"\"\"Creates an operator that returns a tensor using a Laplacian filter.\n\n    The operator smooths the given tensor with a laplacian kernel by convolving\n    it to each channel. It suports batched operation.\n\n    Arguments:\n        kernel_size (int): the size of the kernel.\n        border_type (str): the padding mode to be applied before convolving.\n          The expected modes are: ``'constant'``, ``'reflect'``,\n          ``'replicate'`` or ``'circular'``. Default: ``'reflect'``.\n        normalized (bool): if True, L1 norm of the kernel is set to 1.\n\n    Returns:\n        Tensor: the tensor.\n\n    Shape:\n        - Input: :math:`(B, C, H, W)`\n        - Output: :math:`(B, C, H, W)`\n\n    Examples::\n\n        >>> input = torch.rand(2, 4, 5, 5)\n        >>> laplace = kornia.filters.Laplacian(5)\n        >>> output = laplace(input)  # 2x4x5x5\n    \"\"\"\n\n    def __init__(self,\n                 kernel_size: int, border_type: str = 'reflect',\n                 normalized: bool = True) -> None:\n        super(Laplacian, self).__init__()\n        self.kernel_size: int = kernel_size\n        self.border_type: str = border_type\n        self.normalized: bool = normalized\n        self.kernel: torch.Tensor = torch.unsqueeze(\n            get_laplacian_kernel2d(kernel_size), dim=0)\n        if self.normalized:\n            self.kernel = normalize_kernel2d(self.kernel)\n\n    def __repr__(self) -> str:\n        return self.__class__.__name__ +\\\n            '(kernel_size=' + str(self.kernel_size) + ', ' +\\\n            'normalized=' + str(self.normalized) + ', ' + \\\n            'border_type=' + self.border_type + ')'\n\n    def forward(self, input: torch.Tensor):  # type: ignore\n        return kornia.filter2D(input, self.kernel, self.border_type)\n\n\n######################\n# functional interface\n######################\n\n\ndef laplacian(\n        input: torch.Tensor,\n        kernel_size: int,\n        border_type: str = 'reflect',\n        normalized: bool = True) -> torch.Tensor:\n    r\"\"\"Function that returns a tensor using a Laplacian filter.\n\n    See :class:`~kornia.filters.Laplacian` for details.\n    \"\"\"\n    return Laplacian(kernel_size, border_type, normalized)(input)\n", "path": "kornia/filters/laplacian.py"}], "after_files": [{"content": "from typing import Tuple\n\nimport torch\nimport torch.nn as nn\n\nimport kornia\nfrom kornia.filters.kernels import get_laplacian_kernel2d\nfrom kornia.filters.kernels import normalize_kernel2d\n\n\nclass Laplacian(nn.Module):\n    r\"\"\"Creates an operator that returns a tensor using a Laplacian filter.\n\n    The operator smooths the given tensor with a laplacian kernel by convolving\n    it to each channel. It supports batched operation.\n\n    Arguments:\n        kernel_size (int): the size of the kernel.\n        border_type (str): the padding mode to be applied before convolving.\n          The expected modes are: ``'constant'``, ``'reflect'``,\n          ``'replicate'`` or ``'circular'``. Default: ``'reflect'``.\n        normalized (bool): if True, L1 norm of the kernel is set to 1.\n\n    Returns:\n        Tensor: the tensor.\n\n    Shape:\n        - Input: :math:`(B, C, H, W)`\n        - Output: :math:`(B, C, H, W)`\n\n    Examples::\n\n        >>> input = torch.rand(2, 4, 5, 5)\n        >>> laplace = kornia.filters.Laplacian(5)\n        >>> output = laplace(input)  # 2x4x5x5\n    \"\"\"\n\n    def __init__(self,\n                 kernel_size: int, border_type: str = 'reflect',\n                 normalized: bool = True) -> None:\n        super(Laplacian, self).__init__()\n        self.kernel_size: int = kernel_size\n        self.border_type: str = border_type\n        self.normalized: bool = normalized\n        self.kernel: torch.Tensor = torch.unsqueeze(\n            get_laplacian_kernel2d(kernel_size), dim=0)\n        if self.normalized:\n            self.kernel = normalize_kernel2d(self.kernel)\n\n    def __repr__(self) -> str:\n        return self.__class__.__name__ +\\\n            '(kernel_size=' + str(self.kernel_size) + ', ' +\\\n            'normalized=' + str(self.normalized) + ', ' + \\\n            'border_type=' + self.border_type + ')'\n\n    def forward(self, input: torch.Tensor):  # type: ignore\n        return kornia.filter2D(input, self.kernel, self.border_type)\n\n\n######################\n# functional interface\n######################\n\n\ndef laplacian(\n        input: torch.Tensor,\n        kernel_size: int,\n        border_type: str = 'reflect',\n        normalized: bool = True) -> torch.Tensor:\n    r\"\"\"Function that returns a tensor using a Laplacian filter.\n\n    See :class:`~kornia.filters.Laplacian` for details.\n    \"\"\"\n    return Laplacian(kernel_size, border_type, normalized)(input)\n", "path": "kornia/filters/laplacian.py"}]}