We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Important typo in RandAugment default policy list
### Describe the bug

'translate_x' is in the default policy list twice, whereas 'translate_y' is missing.

### Reproduction steps

```bash
1. Open kornia/augmentation/auto/rand_augment/rand_augment.py
```


### Expected behavior

Current default_policy list:
```
default_policy: List[SUBPLOLICY_CONFIG] = [
    [("auto_contrast", 0, 1)],
    [("equalize", 0, 1)],
    [("invert", 0, 1)],
    [("rotate", -30.0, 30.0)],
    [("posterize", 0.0, 4)],
    [("solarize", 0.0, 1.0)],
    [("solarize_add", 0.0, 0.43)],
    [("color", 0.1, 1.9)],
    [("contrast", 0.1, 1.9)],
    [("brightness", 0.1, 1.9)],
    [("sharpness", 0.1, 1.9)],
    [("shear_x", -0.3, 0.3)],
    [("shear_y", -0.3, 0.3)],
    # (CutoutAbs, 0, 40),
    [("translate_x", -0.1, 0.1)],
    [("translate_x", -0.1, 0.1)],
]
```

Expected list:
```
default_policy: List[SUBPLOLICY_CONFIG] = [
    [("auto_contrast", 0, 1)],
    [("equalize", 0, 1)],
    [("invert", 0, 1)],
    [("rotate", -30.0, 30.0)],
    [("posterize", 0.0, 4)],
    [("solarize", 0.0, 1.0)],
    [("solarize_add", 0.0, 0.43)],
    [("color", 0.1, 1.9)],
    [("contrast", 0.1, 1.9)],
    [("brightness", 0.1, 1.9)],
    [("sharpness", 0.1, 1.9)],
    [("shear_x", -0.3, 0.3)],
    [("shear_y", -0.3, 0.3)],
    # (CutoutAbs, 0, 40),
    [("translate_x", -0.1, 0.1)],
    [("translate_y", -0.1, 0.1)],
]
```

### Environment

```shell
Irrelevant
```


### 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/auto/rand_augment/rand_augment.py`
Content:
```
1 from typing import Dict, Iterator, List, Optional, Tuple, Union, cast
2 
3 import torch
4 from torch.distributions import Categorical
5 
6 from kornia.augmentation.auto.base import SUBPLOLICY_CONFIG, PolicyAugmentBase
7 from kornia.augmentation.auto.operations import OperationBase
8 from kornia.augmentation.auto.operations.policy import PolicySequential
9 from kornia.augmentation.container.params import ParamItem
10 from kornia.core import Module, Tensor
11 
12 from . import ops
13 
14 default_policy: List[SUBPLOLICY_CONFIG] = [
15     [("auto_contrast", 0, 1)],
16     [("equalize", 0, 1)],
17     [("invert", 0, 1)],
18     [("rotate", -30.0, 30.0)],
19     [("posterize", 0.0, 4)],
20     [("solarize", 0.0, 1.0)],
21     [("solarize_add", 0.0, 0.43)],
22     [("color", 0.1, 1.9)],
23     [("contrast", 0.1, 1.9)],
24     [("brightness", 0.1, 1.9)],
25     [("sharpness", 0.1, 1.9)],
26     [("shear_x", -0.3, 0.3)],
27     [("shear_y", -0.3, 0.3)],
28     # (CutoutAbs, 0, 40),
29     [("translate_x", -0.1, 0.1)],
30     [("translate_x", -0.1, 0.1)],
31 ]
32 
33 
34 class RandAugment(PolicyAugmentBase):
35     """Apply RandAugment :cite:`cubuk2020randaugment` augmentation strategies.
36 
37     Args:
38         n: the number of augmentations to apply sequentially.
39         m: magnitude for all the augmentations, ranged from [0, 30].
40         policy: candidate transformations. If None, a default candidate list will be used.
41         transformation_matrix_mode: computation mode for the chained transformation matrix, via `.transform_matrix`
42                                     attribute.
43                                     If `silent`, transformation matrix will be computed silently and the non-rigid
44                                     modules will be ignored as identity transformations.
45                                     If `rigid`, transformation matrix will be computed silently and the non-rigid
46                                     modules will trigger errors.
47                                     If `skip`, transformation matrix will be totally ignored.
48 
49     Examples:
50         >>> import kornia.augmentation as K
51         >>> in_tensor = torch.rand(5, 3, 30, 30)
52         >>> aug = K.AugmentationSequential(RandAugment(n=2, m=10))
53         >>> aug(in_tensor).shape
54         torch.Size([5, 3, 30, 30])
55     """
56 
57     def __init__(
58         self,
59         n: int,
60         m: int,
61         policy: Optional[List[SUBPLOLICY_CONFIG]] = None,
62         transformation_matrix_mode: str = "silent",
63     ) -> None:
64         if m <= 0 or m >= 30:
65             raise ValueError(f"Expect `m` in [0, 30]. Got {m}.")
66 
67         if policy is None:
68             _policy = default_policy
69         else:
70             _policy = policy
71 
72         super().__init__(_policy, transformation_matrix_mode=transformation_matrix_mode)
73         selection_weights = torch.tensor([1.0 / len(self)] * len(self))
74         self.rand_selector = Categorical(selection_weights)
75         self.n = n
76         self.m = m
77 
78     def compose_subpolicy_sequential(self, subpolicy: SUBPLOLICY_CONFIG) -> PolicySequential:
79         if len(subpolicy) != 1:
80             raise RuntimeError(f"Each policy must have only one operation for RandAugment. Got {len(subpolicy)}.")
81         name, low, high = subpolicy[0]
82         return PolicySequential(*[getattr(ops, name)(low, high)])
83 
84     def get_forward_sequence(self, params: Optional[List[ParamItem]] = None) -> Iterator[Tuple[str, Module]]:
85         if params is None:
86             idx = self.rand_selector.sample((self.n,))
87             return self.get_children_by_indices(idx)
88 
89         return self.get_children_by_params(params)
90 
91     def forward_parameters(self, batch_shape: torch.Size) -> List[ParamItem]:
92         named_modules: Iterator[Tuple[str, Module]] = self.get_forward_sequence()
93 
94         params: List[ParamItem] = []
95         mod_param: Union[Dict[str, Tensor], List[ParamItem]]
96         m = torch.tensor([self.m / 30] * batch_shape[0])
97 
98         for name, module in named_modules:
99             # The Input PolicySequential only got one child.
100             op = cast(PolicySequential, module)[0]
101             op = cast(OperationBase, op)
102             mag = None
103             if op.magnitude_range is not None:
104                 minval, maxval = op.magnitude_range
105                 mag = m * float(maxval - minval) + minval
106             mod_param = op.forward_parameters(batch_shape, mag=mag)
107             # Compose it
108             param = ParamItem(name, [ParamItem(next(iter(module.named_children()))[0], mod_param)])
109             params.append(param)
110 
111         return params
112
```
--- 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/auto/rand_augment/rand_augment.py b/kornia/augmentation/auto/rand_augment/rand_augment.py
--- a/kornia/augmentation/auto/rand_augment/rand_augment.py
+++ b/kornia/augmentation/auto/rand_augment/rand_augment.py
@@ -27,7 +27,7 @@
     [("shear_y", -0.3, 0.3)],
     # (CutoutAbs, 0, 40),
     [("translate_x", -0.1, 0.1)],
-    [("translate_x", -0.1, 0.1)],
+    [("translate_y", -0.1, 0.1)],
 ]

{"golden_diff": "diff --git a/kornia/augmentation/auto/rand_augment/rand_augment.py b/kornia/augmentation/auto/rand_augment/rand_augment.py\n--- a/kornia/augmentation/auto/rand_augment/rand_augment.py\n+++ b/kornia/augmentation/auto/rand_augment/rand_augment.py\n@@ -27,7 +27,7 @@\n     [(\"shear_y\", -0.3, 0.3)],\n     # (CutoutAbs, 0, 40),\n     [(\"translate_x\", -0.1, 0.1)],\n-    [(\"translate_x\", -0.1, 0.1)],\n+    [(\"translate_y\", -0.1, 0.1)],\n ]\n", "issue": "Important typo in RandAugment default policy list\n### Describe the bug\n\n'translate_x' is in the default policy list twice, whereas 'translate_y' is missing.\n\n### Reproduction steps\n\n```bash\n1. Open kornia/augmentation/auto/rand_augment/rand_augment.py\n```\n\n\n### Expected behavior\n\nCurrent default_policy list:\r\n```\r\ndefault_policy: List[SUBPLOLICY_CONFIG] = [\r\n    [(\"auto_contrast\", 0, 1)],\r\n    [(\"equalize\", 0, 1)],\r\n    [(\"invert\", 0, 1)],\r\n    [(\"rotate\", -30.0, 30.0)],\r\n    [(\"posterize\", 0.0, 4)],\r\n    [(\"solarize\", 0.0, 1.0)],\r\n    [(\"solarize_add\", 0.0, 0.43)],\r\n    [(\"color\", 0.1, 1.9)],\r\n    [(\"contrast\", 0.1, 1.9)],\r\n    [(\"brightness\", 0.1, 1.9)],\r\n    [(\"sharpness\", 0.1, 1.9)],\r\n    [(\"shear_x\", -0.3, 0.3)],\r\n    [(\"shear_y\", -0.3, 0.3)],\r\n    # (CutoutAbs, 0, 40),\r\n    [(\"translate_x\", -0.1, 0.1)],\r\n    [(\"translate_x\", -0.1, 0.1)],\r\n]\r\n```\r\n\r\nExpected list:\r\n```\r\ndefault_policy: List[SUBPLOLICY_CONFIG] = [\r\n    [(\"auto_contrast\", 0, 1)],\r\n    [(\"equalize\", 0, 1)],\r\n    [(\"invert\", 0, 1)],\r\n    [(\"rotate\", -30.0, 30.0)],\r\n    [(\"posterize\", 0.0, 4)],\r\n    [(\"solarize\", 0.0, 1.0)],\r\n    [(\"solarize_add\", 0.0, 0.43)],\r\n    [(\"color\", 0.1, 1.9)],\r\n    [(\"contrast\", 0.1, 1.9)],\r\n    [(\"brightness\", 0.1, 1.9)],\r\n    [(\"sharpness\", 0.1, 1.9)],\r\n    [(\"shear_x\", -0.3, 0.3)],\r\n    [(\"shear_y\", -0.3, 0.3)],\r\n    # (CutoutAbs, 0, 40),\r\n    [(\"translate_x\", -0.1, 0.1)],\r\n    [(\"translate_y\", -0.1, 0.1)],\r\n]\r\n```\n\n### Environment\n\n```shell\nIrrelevant\n```\n\n\n### Additional context\n\n_No response_\n", "before_files": [{"content": "from typing import Dict, Iterator, List, Optional, Tuple, Union, cast\n\nimport torch\nfrom torch.distributions import Categorical\n\nfrom kornia.augmentation.auto.base import SUBPLOLICY_CONFIG, PolicyAugmentBase\nfrom kornia.augmentation.auto.operations import OperationBase\nfrom kornia.augmentation.auto.operations.policy import PolicySequential\nfrom kornia.augmentation.container.params import ParamItem\nfrom kornia.core import Module, Tensor\n\nfrom . import ops\n\ndefault_policy: List[SUBPLOLICY_CONFIG] = [\n    [(\"auto_contrast\", 0, 1)],\n    [(\"equalize\", 0, 1)],\n    [(\"invert\", 0, 1)],\n    [(\"rotate\", -30.0, 30.0)],\n    [(\"posterize\", 0.0, 4)],\n    [(\"solarize\", 0.0, 1.0)],\n    [(\"solarize_add\", 0.0, 0.43)],\n    [(\"color\", 0.1, 1.9)],\n    [(\"contrast\", 0.1, 1.9)],\n    [(\"brightness\", 0.1, 1.9)],\n    [(\"sharpness\", 0.1, 1.9)],\n    [(\"shear_x\", -0.3, 0.3)],\n    [(\"shear_y\", -0.3, 0.3)],\n    # (CutoutAbs, 0, 40),\n    [(\"translate_x\", -0.1, 0.1)],\n    [(\"translate_x\", -0.1, 0.1)],\n]\n\n\nclass RandAugment(PolicyAugmentBase):\n    \"\"\"Apply RandAugment :cite:`cubuk2020randaugment` augmentation strategies.\n\n    Args:\n        n: the number of augmentations to apply sequentially.\n        m: magnitude for all the augmentations, ranged from [0, 30].\n        policy: candidate transformations. If None, a default candidate list will be used.\n        transformation_matrix_mode: computation mode for the chained transformation matrix, via `.transform_matrix`\n                                    attribute.\n                                    If `silent`, transformation matrix will be computed silently and the non-rigid\n                                    modules will be ignored as identity transformations.\n                                    If `rigid`, transformation matrix will be computed silently and the non-rigid\n                                    modules will trigger errors.\n                                    If `skip`, transformation matrix will be totally ignored.\n\n    Examples:\n        >>> import kornia.augmentation as K\n        >>> in_tensor = torch.rand(5, 3, 30, 30)\n        >>> aug = K.AugmentationSequential(RandAugment(n=2, m=10))\n        >>> aug(in_tensor).shape\n        torch.Size([5, 3, 30, 30])\n    \"\"\"\n\n    def __init__(\n        self,\n        n: int,\n        m: int,\n        policy: Optional[List[SUBPLOLICY_CONFIG]] = None,\n        transformation_matrix_mode: str = \"silent\",\n    ) -> None:\n        if m <= 0 or m >= 30:\n            raise ValueError(f\"Expect `m` in [0, 30]. Got {m}.\")\n\n        if policy is None:\n            _policy = default_policy\n        else:\n            _policy = policy\n\n        super().__init__(_policy, transformation_matrix_mode=transformation_matrix_mode)\n        selection_weights = torch.tensor([1.0 / len(self)] * len(self))\n        self.rand_selector = Categorical(selection_weights)\n        self.n = n\n        self.m = m\n\n    def compose_subpolicy_sequential(self, subpolicy: SUBPLOLICY_CONFIG) -> PolicySequential:\n        if len(subpolicy) != 1:\n            raise RuntimeError(f\"Each policy must have only one operation for RandAugment. Got {len(subpolicy)}.\")\n        name, low, high = subpolicy[0]\n        return PolicySequential(*[getattr(ops, name)(low, high)])\n\n    def get_forward_sequence(self, params: Optional[List[ParamItem]] = None) -> Iterator[Tuple[str, Module]]:\n        if params is None:\n            idx = self.rand_selector.sample((self.n,))\n            return self.get_children_by_indices(idx)\n\n        return self.get_children_by_params(params)\n\n    def forward_parameters(self, batch_shape: torch.Size) -> List[ParamItem]:\n        named_modules: Iterator[Tuple[str, Module]] = self.get_forward_sequence()\n\n        params: List[ParamItem] = []\n        mod_param: Union[Dict[str, Tensor], List[ParamItem]]\n        m = torch.tensor([self.m / 30] * batch_shape[0])\n\n        for name, module in named_modules:\n            # The Input PolicySequential only got one child.\n            op = cast(PolicySequential, module)[0]\n            op = cast(OperationBase, op)\n            mag = None\n            if op.magnitude_range is not None:\n                minval, maxval = op.magnitude_range\n                mag = m * float(maxval - minval) + minval\n            mod_param = op.forward_parameters(batch_shape, mag=mag)\n            # Compose it\n            param = ParamItem(name, [ParamItem(next(iter(module.named_children()))[0], mod_param)])\n            params.append(param)\n\n        return params\n", "path": "kornia/augmentation/auto/rand_augment/rand_augment.py"}], "after_files": [{"content": "from typing import Dict, Iterator, List, Optional, Tuple, Union, cast\n\nimport torch\nfrom torch.distributions import Categorical\n\nfrom kornia.augmentation.auto.base import SUBPLOLICY_CONFIG, PolicyAugmentBase\nfrom kornia.augmentation.auto.operations import OperationBase\nfrom kornia.augmentation.auto.operations.policy import PolicySequential\nfrom kornia.augmentation.container.params import ParamItem\nfrom kornia.core import Module, Tensor\n\nfrom . import ops\n\ndefault_policy: List[SUBPLOLICY_CONFIG] = [\n    [(\"auto_contrast\", 0, 1)],\n    [(\"equalize\", 0, 1)],\n    [(\"invert\", 0, 1)],\n    [(\"rotate\", -30.0, 30.0)],\n    [(\"posterize\", 0.0, 4)],\n    [(\"solarize\", 0.0, 1.0)],\n    [(\"solarize_add\", 0.0, 0.43)],\n    [(\"color\", 0.1, 1.9)],\n    [(\"contrast\", 0.1, 1.9)],\n    [(\"brightness\", 0.1, 1.9)],\n    [(\"sharpness\", 0.1, 1.9)],\n    [(\"shear_x\", -0.3, 0.3)],\n    [(\"shear_y\", -0.3, 0.3)],\n    # (CutoutAbs, 0, 40),\n    [(\"translate_x\", -0.1, 0.1)],\n    [(\"translate_y\", -0.1, 0.1)],\n]\n\n\nclass RandAugment(PolicyAugmentBase):\n    \"\"\"Apply RandAugment :cite:`cubuk2020randaugment` augmentation strategies.\n\n    Args:\n        n: the number of augmentations to apply sequentially.\n        m: magnitude for all the augmentations, ranged from [0, 30].\n        policy: candidate transformations. If None, a default candidate list will be used.\n        transformation_matrix_mode: computation mode for the chained transformation matrix, via `.transform_matrix`\n                                    attribute.\n                                    If `silent`, transformation matrix will be computed silently and the non-rigid\n                                    modules will be ignored as identity transformations.\n                                    If `rigid`, transformation matrix will be computed silently and the non-rigid\n                                    modules will trigger errors.\n                                    If `skip`, transformation matrix will be totally ignored.\n\n    Examples:\n        >>> import kornia.augmentation as K\n        >>> in_tensor = torch.rand(5, 3, 30, 30)\n        >>> aug = K.AugmentationSequential(RandAugment(n=2, m=10))\n        >>> aug(in_tensor).shape\n        torch.Size([5, 3, 30, 30])\n    \"\"\"\n\n    def __init__(\n        self,\n        n: int,\n        m: int,\n        policy: Optional[List[SUBPLOLICY_CONFIG]] = None,\n        transformation_matrix_mode: str = \"silent\",\n    ) -> None:\n        if m <= 0 or m >= 30:\n            raise ValueError(f\"Expect `m` in [0, 30]. Got {m}.\")\n\n        if policy is None:\n            _policy = default_policy\n        else:\n            _policy = policy\n\n        super().__init__(_policy, transformation_matrix_mode=transformation_matrix_mode)\n        selection_weights = torch.tensor([1.0 / len(self)] * len(self))\n        self.rand_selector = Categorical(selection_weights)\n        self.n = n\n        self.m = m\n\n    def compose_subpolicy_sequential(self, subpolicy: SUBPLOLICY_CONFIG) -> PolicySequential:\n        if len(subpolicy) != 1:\n            raise RuntimeError(f\"Each policy must have only one operation for RandAugment. Got {len(subpolicy)}.\")\n        name, low, high = subpolicy[0]\n        return PolicySequential(*[getattr(ops, name)(low, high)])\n\n    def get_forward_sequence(self, params: Optional[List[ParamItem]] = None) -> Iterator[Tuple[str, Module]]:\n        if params is None:\n            idx = self.rand_selector.sample((self.n,))\n            return self.get_children_by_indices(idx)\n\n        return self.get_children_by_params(params)\n\n    def forward_parameters(self, batch_shape: torch.Size) -> List[ParamItem]:\n        named_modules: Iterator[Tuple[str, Module]] = self.get_forward_sequence()\n\n        params: List[ParamItem] = []\n        mod_param: Union[Dict[str, Tensor], List[ParamItem]]\n        m = torch.tensor([self.m / 30] * batch_shape[0])\n\n        for name, module in named_modules:\n            # The Input PolicySequential only got one child.\n            op = cast(PolicySequential, module)[0]\n            op = cast(OperationBase, op)\n            mag = None\n            if op.magnitude_range is not None:\n                minval, maxval = op.magnitude_range\n                mag = m * float(maxval - minval) + minval\n            mod_param = op.forward_parameters(batch_shape, mag=mag)\n            # Compose it\n            param = ParamItem(name, [ParamItem(next(iter(module.named_children()))[0], mod_param)])\n            params.append(param)\n\n        return params\n", "path": "kornia/augmentation/auto/rand_augment/rand_augment.py"}]}