We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Fix a Bug in `kornia.augmentation.RandomRain`
### Describe the bug


When applying `[kornia.augmentation.RandomRain](https://kornia.readthedocs.io/en/latest/augmentation.module.html#kornia.augmentation.RandomRain)` over an image, it seems that subsequent distortions are all affected by the rain effect. For example, if you apply `RandomRotation` before and after calling rain, the rotation before `RandomRain` is fine. However, the rotation after calling `RandomRain` is influenced by the rain. same thing happens for all of the other distortion you may call after `RandomRotation` function. I tried several other [augmentation ](https://kornia.readthedocs.io/en/latest/augmentation.module.html#kornia.augmentation)here and this is the case for all of them. 



![image](https://github.com/kornia/kornia/assets/107806531/b741865f-9737-44b9-b55e-45c1d912494c)







### Reproduction steps

```bash
The following link is a minimal code as an example (on google colab).
```
[sample code](https://colab.research.google.com/drive/1m_Pjqa6-MgO2ybZeNnLYdDerrErYpKw_#scrollTo=dwr_iTFt28t5)

![image](https://github.com/kornia/kornia/assets/107806531/c48b6903-820b-4fef-bdab-849eab21a6c5)

### Expected behavior

When applying the `kornia.augmentation.RandomRain` augmentation to an image, it should only affect the rain effect on that particular image and not introduce any unintended global effects on subsequent transformations. For example, if rotation is applied before and after calling `RandomRain`, the rotation after the rain effect should not be influenced by the rain.


### Environment

```shell
- Running on colab
- OS  Linux
- Python version: 2.0.1+cu118
- GPU models and configuration:T4
```


### Additional context

_No response_

--- 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/augmentation/_2d/intensity/random_rain.py`
Content:
```
1 from __future__ import annotations
2 
3 from typing import Any
4 
5 import torch
6 
7 from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
8 from kornia.augmentation.random_generator._2d import RainGenerator
9 from kornia.core import Tensor
10 from kornia.core.check import KORNIA_CHECK
11 
12 
13 class RandomRain(IntensityAugmentationBase2D):
14     r"""Add Random Rain to the image.
15 
16     Args:
17         p: probability of applying the transformation.
18         number_of_drops: number of drops per image
19         drop_height: height of the drop in image(same for each drops in one image)
20         drop_width: width of the drop in image(same for each drops in one image)
21     Shape:
22         - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`
23         - Output: :math:`(B, C, H, W)`
24 
25     Examples:
26         >>> rng = torch.manual_seed(0)
27         >>> input = torch.rand(1, 1, 5, 5)
28         >>> rain = RandomRain(p=1,drop_height=(1,2),drop_width=(1,2),number_of_drops=(1,1))
29         >>> rain(input)
30         tensor([[[[0.4963, 0.7843, 0.0885, 0.1320, 0.3074],
31                   [0.6341, 0.4901, 0.8964, 0.4556, 0.6323],
32                   [0.3489, 0.4017, 0.0223, 0.1689, 0.2939],
33                   [0.5185, 0.6977, 0.8000, 0.1610, 0.2823],
34                   [0.6816, 0.9152, 0.3971, 0.8742, 0.4194]]]])
35     """
36 
37     def __init__(
38         self,
39         same_on_batch: bool = False,
40         p: float = 0.5,
41         keepdim: bool = False,
42         number_of_drops: tuple[int, int] = (1000, 2000),
43         drop_height: tuple[int, int] = (5, 20),
44         drop_width: tuple[int, int] = (-5, 5),
45     ) -> None:
46         super().__init__(p=p, same_on_batch=same_on_batch, p_batch=1.0, keepdim=keepdim)
47         self._param_generator = RainGenerator(number_of_drops, drop_height, drop_width)
48 
49     def apply_transform(
50         self, image: Tensor, params: dict[str, Tensor], flags: dict[str, Any], transform: Tensor | None = None
51     ) -> Tensor:
52         # Check array and drops size
53         KORNIA_CHECK(image.shape[1] in {3, 1}, "Number of color channels should be 1 or 3.")
54         KORNIA_CHECK(
55             bool(
56                 torch.all(params['drop_height_factor'] <= image.shape[2])
57                 and torch.all(params['drop_height_factor'] > 0)
58             ),
59             "Height of drop should be greater than zero and less than image height.",
60         )
61 
62         KORNIA_CHECK(
63             bool(torch.all(torch.abs(params['drop_width_factor']) <= image.shape[3])),
64             "Width of drop should be less than image width.",
65         )
66 
67         for i in range(image.shape[0]):
68             number_of_drops: int = int(params['number_of_drops_factor'][i])
69             # We generate tensor with maximum number of drops, and then remove unnecessary drops.
70 
71             coordinates_of_drops: Tensor = params['coordinates_factor'][i][:number_of_drops]
72             height_of_drop: int = int(params['drop_height_factor'][i])
73             width_of_drop: int = int(params['drop_width_factor'][i])
74 
75             # Generate start coordinates for each drop
76             random_y_coords = coordinates_of_drops[:, 0] * (image.shape[2] - height_of_drop - 1)
77             if width_of_drop > 0:
78                 random_x_coords = coordinates_of_drops[:, 1] * (image.shape[3] - width_of_drop - 1)
79             else:
80                 random_x_coords = coordinates_of_drops[:, 1] * (image.shape[3] + width_of_drop - 1) - width_of_drop
81 
82             coords = torch.cat([random_y_coords[None], random_x_coords[None]], dim=0).to(image.device, dtype=torch.long)
83 
84             # Generate how our drop will look like into the image
85             size_of_line: int = max(height_of_drop, abs(width_of_drop))
86             x = torch.linspace(start=0, end=height_of_drop, steps=size_of_line, dtype=torch.long).to(image.device)
87             y = torch.linspace(start=0, end=width_of_drop, steps=size_of_line, dtype=torch.long).to(image.device)
88             # Draw lines
89             for k in range(x.shape[0]):
90                 image[i, :, coords[0] + x[k], coords[1] + y[k]] = 200 / 255
91         return image
92
```
--- 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/augmentation/_2d/intensity/random_rain.py b/kornia/augmentation/_2d/intensity/random_rain.py
--- a/kornia/augmentation/_2d/intensity/random_rain.py
+++ b/kornia/augmentation/_2d/intensity/random_rain.py
@@ -63,7 +63,7 @@
             bool(torch.all(torch.abs(params['drop_width_factor']) <= image.shape[3])),
             "Width of drop should be less than image width.",
         )
-
+        modeified_img = image.clone()
         for i in range(image.shape[0]):
             number_of_drops: int = int(params['number_of_drops_factor'][i])
             # We generate tensor with maximum number of drops, and then remove unnecessary drops.
@@ -87,5 +87,5 @@
             y = torch.linspace(start=0, end=width_of_drop, steps=size_of_line, dtype=torch.long).to(image.device)
             # Draw lines
             for k in range(x.shape[0]):
-                image[i, :, coords[0] + x[k], coords[1] + y[k]] = 200 / 255
-        return image
+                modeified_img[i, :, coords[0] + x[k], coords[1] + y[k]] = 200 / 255
+        return modeified_img

{"golden_diff": "diff --git a/kornia/augmentation/_2d/intensity/random_rain.py b/kornia/augmentation/_2d/intensity/random_rain.py\n--- a/kornia/augmentation/_2d/intensity/random_rain.py\n+++ b/kornia/augmentation/_2d/intensity/random_rain.py\n@@ -63,7 +63,7 @@\n             bool(torch.all(torch.abs(params['drop_width_factor']) <= image.shape[3])),\n             \"Width of drop should be less than image width.\",\n         )\n-\n+        modeified_img = image.clone()\n         for i in range(image.shape[0]):\n             number_of_drops: int = int(params['number_of_drops_factor'][i])\n             # We generate tensor with maximum number of drops, and then remove unnecessary drops.\n@@ -87,5 +87,5 @@\n             y = torch.linspace(start=0, end=width_of_drop, steps=size_of_line, dtype=torch.long).to(image.device)\n             # Draw lines\n             for k in range(x.shape[0]):\n-                image[i, :, coords[0] + x[k], coords[1] + y[k]] = 200 / 255\n-        return image\n+                modeified_img[i, :, coords[0] + x[k], coords[1] + y[k]] = 200 / 255\n+        return modeified_img\n", "issue": "Fix a Bug in `kornia.augmentation.RandomRain`\n### Describe the bug\r\n\r\n\r\nWhen applying `[kornia.augmentation.RandomRain](https://kornia.readthedocs.io/en/latest/augmentation.module.html#kornia.augmentation.RandomRain)` over an image, it seems that subsequent distortions are all affected by the rain effect. For example, if you apply `RandomRotation` before and after calling rain, the rotation before `RandomRain` is fine. However, the rotation after calling `RandomRain` is influenced by the rain. same thing happens for all of the other distortion you may call after `RandomRotation` function. I tried several other [augmentation ](https://kornia.readthedocs.io/en/latest/augmentation.module.html#kornia.augmentation)here and this is the case for all of them. \r\n\r\n\r\n\r\n![image](https://github.com/kornia/kornia/assets/107806531/b741865f-9737-44b9-b55e-45c1d912494c)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n### Reproduction steps\r\n\r\n```bash\r\nThe following link is a minimal code as an example (on google colab).\r\n```\r\n[sample code](https://colab.research.google.com/drive/1m_Pjqa6-MgO2ybZeNnLYdDerrErYpKw_#scrollTo=dwr_iTFt28t5)\r\n\r\n![image](https://github.com/kornia/kornia/assets/107806531/c48b6903-820b-4fef-bdab-849eab21a6c5)\r\n\r\n### Expected behavior\r\n\r\nWhen applying the `kornia.augmentation.RandomRain` augmentation to an image, it should only affect the rain effect on that particular image and not introduce any unintended global effects on subsequent transformations. For example, if rotation is applied before and after calling `RandomRain`, the rotation after the rain effect should not be influenced by the rain.\r\n\r\n\r\n### Environment\r\n\r\n```shell\r\n- Running on colab\r\n- OS  Linux\r\n- Python version: 2.0.1+cu118\r\n- GPU models and configuration:T4\r\n```\r\n\r\n\r\n### Additional context\r\n\r\n_No response_\n", "before_files": [{"content": "from __future__ import annotations\n\nfrom typing import Any\n\nimport torch\n\nfrom kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D\nfrom kornia.augmentation.random_generator._2d import RainGenerator\nfrom kornia.core import Tensor\nfrom kornia.core.check import KORNIA_CHECK\n\n\nclass RandomRain(IntensityAugmentationBase2D):\n    r\"\"\"Add Random Rain to the image.\n\n    Args:\n        p: probability of applying the transformation.\n        number_of_drops: number of drops per image\n        drop_height: height of the drop in image(same for each drops in one image)\n        drop_width: width of the drop in image(same for each drops in one image)\n    Shape:\n        - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`\n        - Output: :math:`(B, C, H, W)`\n\n    Examples:\n        >>> rng = torch.manual_seed(0)\n        >>> input = torch.rand(1, 1, 5, 5)\n        >>> rain = RandomRain(p=1,drop_height=(1,2),drop_width=(1,2),number_of_drops=(1,1))\n        >>> rain(input)\n        tensor([[[[0.4963, 0.7843, 0.0885, 0.1320, 0.3074],\n                  [0.6341, 0.4901, 0.8964, 0.4556, 0.6323],\n                  [0.3489, 0.4017, 0.0223, 0.1689, 0.2939],\n                  [0.5185, 0.6977, 0.8000, 0.1610, 0.2823],\n                  [0.6816, 0.9152, 0.3971, 0.8742, 0.4194]]]])\n    \"\"\"\n\n    def __init__(\n        self,\n        same_on_batch: bool = False,\n        p: float = 0.5,\n        keepdim: bool = False,\n        number_of_drops: tuple[int, int] = (1000, 2000),\n        drop_height: tuple[int, int] = (5, 20),\n        drop_width: tuple[int, int] = (-5, 5),\n    ) -> None:\n        super().__init__(p=p, same_on_batch=same_on_batch, p_batch=1.0, keepdim=keepdim)\n        self._param_generator = RainGenerator(number_of_drops, drop_height, drop_width)\n\n    def apply_transform(\n        self, image: Tensor, params: dict[str, Tensor], flags: dict[str, Any], transform: Tensor | None = None\n    ) -> Tensor:\n        # Check array and drops size\n        KORNIA_CHECK(image.shape[1] in {3, 1}, \"Number of color channels should be 1 or 3.\")\n        KORNIA_CHECK(\n            bool(\n                torch.all(params['drop_height_factor'] <= image.shape[2])\n                and torch.all(params['drop_height_factor'] > 0)\n            ),\n            \"Height of drop should be greater than zero and less than image height.\",\n        )\n\n        KORNIA_CHECK(\n            bool(torch.all(torch.abs(params['drop_width_factor']) <= image.shape[3])),\n            \"Width of drop should be less than image width.\",\n        )\n\n        for i in range(image.shape[0]):\n            number_of_drops: int = int(params['number_of_drops_factor'][i])\n            # We generate tensor with maximum number of drops, and then remove unnecessary drops.\n\n            coordinates_of_drops: Tensor = params['coordinates_factor'][i][:number_of_drops]\n            height_of_drop: int = int(params['drop_height_factor'][i])\n            width_of_drop: int = int(params['drop_width_factor'][i])\n\n            # Generate start coordinates for each drop\n            random_y_coords = coordinates_of_drops[:, 0] * (image.shape[2] - height_of_drop - 1)\n            if width_of_drop > 0:\n                random_x_coords = coordinates_of_drops[:, 1] * (image.shape[3] - width_of_drop - 1)\n            else:\n                random_x_coords = coordinates_of_drops[:, 1] * (image.shape[3] + width_of_drop - 1) - width_of_drop\n\n            coords = torch.cat([random_y_coords[None], random_x_coords[None]], dim=0).to(image.device, dtype=torch.long)\n\n            # Generate how our drop will look like into the image\n            size_of_line: int = max(height_of_drop, abs(width_of_drop))\n            x = torch.linspace(start=0, end=height_of_drop, steps=size_of_line, dtype=torch.long).to(image.device)\n            y = torch.linspace(start=0, end=width_of_drop, steps=size_of_line, dtype=torch.long).to(image.device)\n            # Draw lines\n            for k in range(x.shape[0]):\n                image[i, :, coords[0] + x[k], coords[1] + y[k]] = 200 / 255\n        return image\n", "path": "kornia/augmentation/_2d/intensity/random_rain.py"}], "after_files": [{"content": "from __future__ import annotations\n\nfrom typing import Any\n\nimport torch\n\nfrom kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D\nfrom kornia.augmentation.random_generator._2d import RainGenerator\nfrom kornia.core import Tensor\nfrom kornia.core.check import KORNIA_CHECK\n\n\nclass RandomRain(IntensityAugmentationBase2D):\n    r\"\"\"Add Random Rain to the image.\n\n    Args:\n        p: probability of applying the transformation.\n        number_of_drops: number of drops per image\n        drop_height: height of the drop in image(same for each drops in one image)\n        drop_width: width of the drop in image(same for each drops in one image)\n    Shape:\n        - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`\n        - Output: :math:`(B, C, H, W)`\n\n    Examples:\n        >>> rng = torch.manual_seed(0)\n        >>> input = torch.rand(1, 1, 5, 5)\n        >>> rain = RandomRain(p=1,drop_height=(1,2),drop_width=(1,2),number_of_drops=(1,1))\n        >>> rain(input)\n        tensor([[[[0.4963, 0.7843, 0.0885, 0.1320, 0.3074],\n                  [0.6341, 0.4901, 0.8964, 0.4556, 0.6323],\n                  [0.3489, 0.4017, 0.0223, 0.1689, 0.2939],\n                  [0.5185, 0.6977, 0.8000, 0.1610, 0.2823],\n                  [0.6816, 0.9152, 0.3971, 0.8742, 0.4194]]]])\n    \"\"\"\n\n    def __init__(\n        self,\n        same_on_batch: bool = False,\n        p: float = 0.5,\n        keepdim: bool = False,\n        number_of_drops: tuple[int, int] = (1000, 2000),\n        drop_height: tuple[int, int] = (5, 20),\n        drop_width: tuple[int, int] = (-5, 5),\n    ) -> None:\n        super().__init__(p=p, same_on_batch=same_on_batch, p_batch=1.0, keepdim=keepdim)\n        self._param_generator = RainGenerator(number_of_drops, drop_height, drop_width)\n\n    def apply_transform(\n        self, image: Tensor, params: dict[str, Tensor], flags: dict[str, Any], transform: Tensor | None = None\n    ) -> Tensor:\n        # Check array and drops size\n        KORNIA_CHECK(image.shape[1] in {3, 1}, \"Number of color channels should be 1 or 3.\")\n        KORNIA_CHECK(\n            bool(\n                torch.all(params['drop_height_factor'] <= image.shape[2])\n                and torch.all(params['drop_height_factor'] > 0)\n            ),\n            \"Height of drop should be greater than zero and less than image height.\",\n        )\n\n        KORNIA_CHECK(\n            bool(torch.all(torch.abs(params['drop_width_factor']) <= image.shape[3])),\n            \"Width of drop should be less than image width.\",\n        )\n        modeified_img = image.clone()\n        for i in range(image.shape[0]):\n            number_of_drops: int = int(params['number_of_drops_factor'][i])\n            # We generate tensor with maximum number of drops, and then remove unnecessary drops.\n\n            coordinates_of_drops: Tensor = params['coordinates_factor'][i][:number_of_drops]\n            height_of_drop: int = int(params['drop_height_factor'][i])\n            width_of_drop: int = int(params['drop_width_factor'][i])\n\n            # Generate start coordinates for each drop\n            random_y_coords = coordinates_of_drops[:, 0] * (image.shape[2] - height_of_drop - 1)\n            if width_of_drop > 0:\n                random_x_coords = coordinates_of_drops[:, 1] * (image.shape[3] - width_of_drop - 1)\n            else:\n                random_x_coords = coordinates_of_drops[:, 1] * (image.shape[3] + width_of_drop - 1) - width_of_drop\n\n            coords = torch.cat([random_y_coords[None], random_x_coords[None]], dim=0).to(image.device, dtype=torch.long)\n\n            # Generate how our drop will look like into the image\n            size_of_line: int = max(height_of_drop, abs(width_of_drop))\n            x = torch.linspace(start=0, end=height_of_drop, steps=size_of_line, dtype=torch.long).to(image.device)\n            y = torch.linspace(start=0, end=width_of_drop, steps=size_of_line, dtype=torch.long).to(image.device)\n            # Draw lines\n            for k in range(x.shape[0]):\n                modeified_img[i, :, coords[0] + x[k], coords[1] + y[k]] = 200 / 255\n        return modeified_img\n", "path": "kornia/augmentation/_2d/intensity/random_rain.py"}]}