We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Importing kornia causes `logging` to print to stderr?
### Describe the bug

I pip-installed the master version of kornia to access my latest PR and now my training scripts started to print all kinds of debug info. Could it be because importing kornia imports in turn `kornia.x.trainer` which has [this](https://github.com/kornia/kornia/blob/ed4eb7ab77218b021914f77cad426528a59bd780/kornia/x/trainer.py#L18) line? If so, how can I disable `x` when installing via `pip install git+https://github.com/kornia/kornia.git`?

### Reproduction steps

```bash
Import `kornia` in any script which uses `logging`.
```


### Expected behavior

Merely importing `kornia` should not toggle global settings of `logging`.

### Environment

```shell
PyTorch version: 1.9.0
Is debug build: False
CUDA used to build PyTorch: 11.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.3 LTS (x86_64)
GCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31

Python version: 3.8.10 (default, Jun  4 2021, 15:09:15)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.11.0-1018-gcp-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: 
GPU 0: Tesla V100-SXM2-16GB
GPU 1: Tesla V100-SXM2-16GB
GPU 2: Tesla V100-SXM2-16GB
GPU 3: Tesla V100-SXM2-16GB
GPU 4: Tesla V100-SXM2-16GB
GPU 5: Tesla V100-SXM2-16GB
GPU 6: Tesla V100-SXM2-16GB
GPU 7: Tesla V100-SXM2-16GB

Nvidia driver version: 470.57.02
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.19.5
[pip3] pytorch-lightning==1.4.7
[pip3] torch==1.9.0
[pip3] torch-dimcheck==0.0.1
[pip3] torchaudio==0.9.0a0+33b2469
[pip3] torchmetrics==0.4.1
[pip3] torchvision==0.10.0
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.1.74              h6bb024c_0    nvidia
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] mkl                       2021.3.0           h06a4308_520  
[conda] mkl-service               2.4.0            py38h7f8727e_0  
[conda] mkl_fft                   1.3.0            py38h42c9631_2  
[conda] mkl_random                1.2.2            py38h51133e4_0  
[conda] mypy-extensions           0.4.3                    pypi_0    pypi
[conda] numpy                     1.19.5                   pypi_0    pypi
[conda] pytorch                   1.9.0           py3.8_cuda11.1_cudnn8.0.5_0    pytorch
[conda] pytorch-lightning         1.4.7                    pypi_0    pypi
[conda] torchaudio                0.9.0                      py38    pytorch
[conda] torchmetrics              0.4.1                    pypi_0    pypi
[conda] torchvision               0.10.0               py38_cu111    pytorch
```


### 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/x/trainer.py`
Content:
```
1 import logging
2 from typing import Callable, Dict
3 
4 import torch
5 import torch.nn as nn
6 from torch.utils.data import DataLoader
7 
8 # the accelerator library is a requirement for the Trainer
9 # but it is optional for grousnd base user of kornia.
10 try:
11     from accelerate import Accelerator
12 except ImportError:
13     Accelerator = None
14 
15 from .metrics import AverageMeter
16 from .utils import Configuration, TrainerState
17 
18 logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)
19 
20 
21 callbacks_whitelist = [
22     "preprocess", "augmentations", "evaluate", "fit", "checkpoint", "terminate"
23 ]
24 
25 
26 class Trainer:
27     """Base class to train the different models in kornia.
28 
29     .. warning::
30         The API is experimental and subject to be modified based on the needs of kornia models.
31 
32     Args:
33         model: the nn.Module to be optimized.
34         train_dataloader: the data loader used in the training loop.
35         valid_dataloader: the data loader used in the validation loop.
36         criterion: the nn.Module with the function that computes the loss.
37         optimizer: the torch optimizer object to be used during the optimization.
38         scheduler: the torch scheduler object with defiing the scheduling strategy.
39         accelerator: the Accelerator object to distribute the training.
40         config: a TrainerConfiguration structure containing the experiment hyper parameters.
41         callbacks: a dictionary containing the pointers to the functions to overrides. The
42           main supported hooks are ``evaluate``, ``preprocess``, ``augmentations`` and ``fit``.
43 
44     .. important::
45         The API heavily relies on `accelerate <https://github.com/huggingface/accelerate/>`_.
46         In order to use it, you must: ``pip install kornia[x]``
47 
48     .. seealso::
49         Learn how to use the API in our documentation
50         `here <https://kornia.readthedocs.io/en/latest/get-started/training.html>`_.
51     """
52     def __init__(
53         self,
54         model: nn.Module,
55         train_dataloader: DataLoader,
56         valid_dataloader: DataLoader,
57         criterion: nn.Module,
58         optimizer: torch.optim.Optimizer,
59         scheduler: torch.optim.lr_scheduler.CosineAnnealingLR,
60         config: Configuration,
61         callbacks: Dict[str, Callable] = {},
62     ) -> None:
63         # setup the accelerator
64         if Accelerator is None:
65             raise ModuleNotFoundError(
66                 "accelerate library is not installed: pip install kornia[x]")
67         self.accelerator = Accelerator()
68 
69         # setup the data related objects
70         self.model = self.accelerator.prepare(model)
71         self.train_dataloader = self.accelerator.prepare(train_dataloader)
72         self.valid_dataloader = self.accelerator.prepare(valid_dataloader)
73         self.criterion = criterion.to(self.device)
74         self.optimizer = self.accelerator.prepare(optimizer)
75         self.scheduler = scheduler
76         self.config = config
77 
78         # configure callbacks
79         for fn_name, fn in callbacks.items():
80             if fn_name not in callbacks_whitelist:
81                 raise ValueError(f"Not supported: {fn_name}.")
82             setattr(self, fn_name, fn)
83 
84         # hyper-params
85         self.num_epochs = config.num_epochs
86 
87         self._logger = logging.getLogger('train')
88 
89     @property
90     def device(self) -> torch.device:
91         return self.accelerator.device
92 
93     def backward(self, loss: torch.Tensor) -> None:
94         self.accelerator.backward(loss)
95 
96     def fit_epoch(self, epoch: int) -> None:
97         # train loop
98         self.model.train()
99         losses = AverageMeter()
100         for sample_id, sample in enumerate(self.train_dataloader):
101             source, target = sample  # this might change with new pytorch dataset structure
102             self.optimizer.zero_grad()
103 
104             # perform the preprocess and augmentations in batch
105             img = self.preprocess(source)
106             img = self.augmentations(img)
107             # make the actual inference
108             output = self.model(img)
109             loss = self.criterion(output, target)
110             self.backward(loss)
111             self.optimizer.step()
112 
113             losses.update(loss.item(), img.shape[0])
114 
115             if sample_id % 50 == 0:
116                 self._logger.info(
117                     f"Train: {epoch + 1}/{self.num_epochs}  "
118                     f"Sample: {sample_id + 1}/{len(self.train_dataloader)} "
119                     f"Loss: {losses.val:.3f} {losses.avg:.3f}"
120                 )
121 
122     def fit(self,) -> None:
123         # execute the main loop
124         # NOTE: Do not change and keep this structure clear for readability.
125         for epoch in range(self.num_epochs):
126             # call internally the training loop
127             # NOTE: override to customize your evaluation routine
128             self.fit_epoch(epoch)
129 
130             # call internally the evaluation loop
131             # NOTE: override to customize your evaluation routine
132             valid_stats = self.evaluate()
133 
134             self.checkpoint(self.model, epoch, valid_stats)
135 
136             state = self.terminate(self.model, epoch, valid_stats)
137             if state == TrainerState.TERMINATE:
138                 break
139 
140             # END OF THE EPOCH
141             self.scheduler.step()
142 
143         ...
144 
145     def evaluate(self):
146         ...
147 
148     def preprocess(self, x):
149         return x
150 
151     def augmentations(self, x):
152         return x
153 
154     def checkpoint(self, *args, **kwargs):
155         ...
156 
157     def terminate(self, *args, **kwargs):
158         ...
159
```
--- 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/x/trainer.py b/kornia/x/trainer.py
--- a/kornia/x/trainer.py
+++ b/kornia/x/trainer.py
@@ -15,9 +15,6 @@
 from .metrics import AverageMeter
 from .utils import Configuration, TrainerState
 
-logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)
-
-
 callbacks_whitelist = [
     "preprocess", "augmentations", "evaluate", "fit", "checkpoint", "terminate"
 ]

{"golden_diff": "diff --git a/kornia/x/trainer.py b/kornia/x/trainer.py\n--- a/kornia/x/trainer.py\n+++ b/kornia/x/trainer.py\n@@ -15,9 +15,6 @@\n from .metrics import AverageMeter\n from .utils import Configuration, TrainerState\n \n-logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)\n-\n-\n callbacks_whitelist = [\n     \"preprocess\", \"augmentations\", \"evaluate\", \"fit\", \"checkpoint\", \"terminate\"\n ]\n", "issue": "Importing kornia causes `logging` to print to stderr?\n### Describe the bug\n\nI pip-installed the master version of kornia to access my latest PR and now my training scripts started to print all kinds of debug info. Could it be because importing kornia imports in turn `kornia.x.trainer` which has [this](https://github.com/kornia/kornia/blob/ed4eb7ab77218b021914f77cad426528a59bd780/kornia/x/trainer.py#L18) line? If so, how can I disable `x` when installing via `pip install git+https://github.com/kornia/kornia.git`?\n\n### Reproduction steps\n\n```bash\nImport `kornia` in any script which uses `logging`.\n```\n\n\n### Expected behavior\n\nMerely importing `kornia` should not toggle global settings of `logging`.\n\n### Environment\n\n```shell\nPyTorch version: 1.9.0\r\nIs debug build: False\r\nCUDA used to build PyTorch: 11.1\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Ubuntu 20.04.3 LTS (x86_64)\r\nGCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0\r\nClang version: Could not collect\r\nCMake version: Could not collect\r\nLibc version: glibc-2.31\r\n\r\nPython version: 3.8.10 (default, Jun  4 2021, 15:09:15)  [GCC 7.5.0] (64-bit runtime)\r\nPython platform: Linux-5.11.0-1018-gcp-x86_64-with-glibc2.17\r\nIs CUDA available: True\r\nCUDA runtime version: Could not collect\r\nGPU models and configuration: \r\nGPU 0: Tesla V100-SXM2-16GB\r\nGPU 1: Tesla V100-SXM2-16GB\r\nGPU 2: Tesla V100-SXM2-16GB\r\nGPU 3: Tesla V100-SXM2-16GB\r\nGPU 4: Tesla V100-SXM2-16GB\r\nGPU 5: Tesla V100-SXM2-16GB\r\nGPU 6: Tesla V100-SXM2-16GB\r\nGPU 7: Tesla V100-SXM2-16GB\r\n\r\nNvidia driver version: 470.57.02\r\ncuDNN version: Could not collect\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\n\r\nVersions of relevant libraries:\r\n[pip3] mypy-extensions==0.4.3\r\n[pip3] numpy==1.19.5\r\n[pip3] pytorch-lightning==1.4.7\r\n[pip3] torch==1.9.0\r\n[pip3] torch-dimcheck==0.0.1\r\n[pip3] torchaudio==0.9.0a0+33b2469\r\n[pip3] torchmetrics==0.4.1\r\n[pip3] torchvision==0.10.0\r\n[conda] blas                      1.0                         mkl  \r\n[conda] cudatoolkit               11.1.74              h6bb024c_0    nvidia\r\n[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch\r\n[conda] mkl                       2021.3.0           h06a4308_520  \r\n[conda] mkl-service               2.4.0            py38h7f8727e_0  \r\n[conda] mkl_fft                   1.3.0            py38h42c9631_2  \r\n[conda] mkl_random                1.2.2            py38h51133e4_0  \r\n[conda] mypy-extensions           0.4.3                    pypi_0    pypi\r\n[conda] numpy                     1.19.5                   pypi_0    pypi\r\n[conda] pytorch                   1.9.0           py3.8_cuda11.1_cudnn8.0.5_0    pytorch\r\n[conda] pytorch-lightning         1.4.7                    pypi_0    pypi\r\n[conda] torchaudio                0.9.0                      py38    pytorch\r\n[conda] torchmetrics              0.4.1                    pypi_0    pypi\r\n[conda] torchvision               0.10.0               py38_cu111    pytorch\n```\n\n\n### Additional context\n\n_No response_\n", "before_files": [{"content": "import logging\nfrom typing import Callable, Dict\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\n\n# the accelerator library is a requirement for the Trainer\n# but it is optional for grousnd base user of kornia.\ntry:\n    from accelerate import Accelerator\nexcept ImportError:\n    Accelerator = None\n\nfrom .metrics import AverageMeter\nfrom .utils import Configuration, TrainerState\n\nlogging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)\n\n\ncallbacks_whitelist = [\n    \"preprocess\", \"augmentations\", \"evaluate\", \"fit\", \"checkpoint\", \"terminate\"\n]\n\n\nclass Trainer:\n    \"\"\"Base class to train the different models in kornia.\n\n    .. warning::\n        The API is experimental and subject to be modified based on the needs of kornia models.\n\n    Args:\n        model: the nn.Module to be optimized.\n        train_dataloader: the data loader used in the training loop.\n        valid_dataloader: the data loader used in the validation loop.\n        criterion: the nn.Module with the function that computes the loss.\n        optimizer: the torch optimizer object to be used during the optimization.\n        scheduler: the torch scheduler object with defiing the scheduling strategy.\n        accelerator: the Accelerator object to distribute the training.\n        config: a TrainerConfiguration structure containing the experiment hyper parameters.\n        callbacks: a dictionary containing the pointers to the functions to overrides. The\n          main supported hooks are ``evaluate``, ``preprocess``, ``augmentations`` and ``fit``.\n\n    .. important::\n        The API heavily relies on `accelerate <https://github.com/huggingface/accelerate/>`_.\n        In order to use it, you must: ``pip install kornia[x]``\n\n    .. seealso::\n        Learn how to use the API in our documentation\n        `here <https://kornia.readthedocs.io/en/latest/get-started/training.html>`_.\n    \"\"\"\n    def __init__(\n        self,\n        model: nn.Module,\n        train_dataloader: DataLoader,\n        valid_dataloader: DataLoader,\n        criterion: nn.Module,\n        optimizer: torch.optim.Optimizer,\n        scheduler: torch.optim.lr_scheduler.CosineAnnealingLR,\n        config: Configuration,\n        callbacks: Dict[str, Callable] = {},\n    ) -> None:\n        # setup the accelerator\n        if Accelerator is None:\n            raise ModuleNotFoundError(\n                \"accelerate library is not installed: pip install kornia[x]\")\n        self.accelerator = Accelerator()\n\n        # setup the data related objects\n        self.model = self.accelerator.prepare(model)\n        self.train_dataloader = self.accelerator.prepare(train_dataloader)\n        self.valid_dataloader = self.accelerator.prepare(valid_dataloader)\n        self.criterion = criterion.to(self.device)\n        self.optimizer = self.accelerator.prepare(optimizer)\n        self.scheduler = scheduler\n        self.config = config\n\n        # configure callbacks\n        for fn_name, fn in callbacks.items():\n            if fn_name not in callbacks_whitelist:\n                raise ValueError(f\"Not supported: {fn_name}.\")\n            setattr(self, fn_name, fn)\n\n        # hyper-params\n        self.num_epochs = config.num_epochs\n\n        self._logger = logging.getLogger('train')\n\n    @property\n    def device(self) -> torch.device:\n        return self.accelerator.device\n\n    def backward(self, loss: torch.Tensor) -> None:\n        self.accelerator.backward(loss)\n\n    def fit_epoch(self, epoch: int) -> None:\n        # train loop\n        self.model.train()\n        losses = AverageMeter()\n        for sample_id, sample in enumerate(self.train_dataloader):\n            source, target = sample  # this might change with new pytorch dataset structure\n            self.optimizer.zero_grad()\n\n            # perform the preprocess and augmentations in batch\n            img = self.preprocess(source)\n            img = self.augmentations(img)\n            # make the actual inference\n            output = self.model(img)\n            loss = self.criterion(output, target)\n            self.backward(loss)\n            self.optimizer.step()\n\n            losses.update(loss.item(), img.shape[0])\n\n            if sample_id % 50 == 0:\n                self._logger.info(\n                    f\"Train: {epoch + 1}/{self.num_epochs}  \"\n                    f\"Sample: {sample_id + 1}/{len(self.train_dataloader)} \"\n                    f\"Loss: {losses.val:.3f} {losses.avg:.3f}\"\n                )\n\n    def fit(self,) -> None:\n        # execute the main loop\n        # NOTE: Do not change and keep this structure clear for readability.\n        for epoch in range(self.num_epochs):\n            # call internally the training loop\n            # NOTE: override to customize your evaluation routine\n            self.fit_epoch(epoch)\n\n            # call internally the evaluation loop\n            # NOTE: override to customize your evaluation routine\n            valid_stats = self.evaluate()\n\n            self.checkpoint(self.model, epoch, valid_stats)\n\n            state = self.terminate(self.model, epoch, valid_stats)\n            if state == TrainerState.TERMINATE:\n                break\n\n            # END OF THE EPOCH\n            self.scheduler.step()\n\n        ...\n\n    def evaluate(self):\n        ...\n\n    def preprocess(self, x):\n        return x\n\n    def augmentations(self, x):\n        return x\n\n    def checkpoint(self, *args, **kwargs):\n        ...\n\n    def terminate(self, *args, **kwargs):\n        ...\n", "path": "kornia/x/trainer.py"}], "after_files": [{"content": "import logging\nfrom typing import Callable, Dict\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\n\n# the accelerator library is a requirement for the Trainer\n# but it is optional for grousnd base user of kornia.\ntry:\n    from accelerate import Accelerator\nexcept ImportError:\n    Accelerator = None\n\nfrom .metrics import AverageMeter\nfrom .utils import Configuration, TrainerState\n\ncallbacks_whitelist = [\n    \"preprocess\", \"augmentations\", \"evaluate\", \"fit\", \"checkpoint\", \"terminate\"\n]\n\n\nclass Trainer:\n    \"\"\"Base class to train the different models in kornia.\n\n    .. warning::\n        The API is experimental and subject to be modified based on the needs of kornia models.\n\n    Args:\n        model: the nn.Module to be optimized.\n        train_dataloader: the data loader used in the training loop.\n        valid_dataloader: the data loader used in the validation loop.\n        criterion: the nn.Module with the function that computes the loss.\n        optimizer: the torch optimizer object to be used during the optimization.\n        scheduler: the torch scheduler object with defiing the scheduling strategy.\n        accelerator: the Accelerator object to distribute the training.\n        config: a TrainerConfiguration structure containing the experiment hyper parameters.\n        callbacks: a dictionary containing the pointers to the functions to overrides. The\n          main supported hooks are ``evaluate``, ``preprocess``, ``augmentations`` and ``fit``.\n\n    .. important::\n        The API heavily relies on `accelerate <https://github.com/huggingface/accelerate/>`_.\n        In order to use it, you must: ``pip install kornia[x]``\n\n    .. seealso::\n        Learn how to use the API in our documentation\n        `here <https://kornia.readthedocs.io/en/latest/get-started/training.html>`_.\n    \"\"\"\n    def __init__(\n        self,\n        model: nn.Module,\n        train_dataloader: DataLoader,\n        valid_dataloader: DataLoader,\n        criterion: nn.Module,\n        optimizer: torch.optim.Optimizer,\n        scheduler: torch.optim.lr_scheduler.CosineAnnealingLR,\n        config: Configuration,\n        callbacks: Dict[str, Callable] = {},\n    ) -> None:\n        # setup the accelerator\n        if Accelerator is None:\n            raise ModuleNotFoundError(\n                \"accelerate library is not installed: pip install kornia[x]\")\n        self.accelerator = Accelerator()\n\n        # setup the data related objects\n        self.model = self.accelerator.prepare(model)\n        self.train_dataloader = self.accelerator.prepare(train_dataloader)\n        self.valid_dataloader = self.accelerator.prepare(valid_dataloader)\n        self.criterion = criterion.to(self.device)\n        self.optimizer = self.accelerator.prepare(optimizer)\n        self.scheduler = scheduler\n        self.config = config\n\n        # configure callbacks\n        for fn_name, fn in callbacks.items():\n            if fn_name not in callbacks_whitelist:\n                raise ValueError(f\"Not supported: {fn_name}.\")\n            setattr(self, fn_name, fn)\n\n        # hyper-params\n        self.num_epochs = config.num_epochs\n\n        self._logger = logging.getLogger('train')\n\n    @property\n    def device(self) -> torch.device:\n        return self.accelerator.device\n\n    def backward(self, loss: torch.Tensor) -> None:\n        self.accelerator.backward(loss)\n\n    def fit_epoch(self, epoch: int) -> None:\n        # train loop\n        self.model.train()\n        losses = AverageMeter()\n        for sample_id, sample in enumerate(self.train_dataloader):\n            source, target = sample  # this might change with new pytorch dataset structure\n            self.optimizer.zero_grad()\n\n            # perform the preprocess and augmentations in batch\n            img = self.preprocess(source)\n            img = self.augmentations(img)\n            # make the actual inference\n            output = self.model(img)\n            loss = self.criterion(output, target)\n            self.backward(loss)\n            self.optimizer.step()\n\n            losses.update(loss.item(), img.shape[0])\n\n            if sample_id % 50 == 0:\n                self._logger.info(\n                    f\"Train: {epoch + 1}/{self.num_epochs}  \"\n                    f\"Sample: {sample_id + 1}/{len(self.train_dataloader)} \"\n                    f\"Loss: {losses.val:.3f} {losses.avg:.3f}\"\n                )\n\n    def fit(self,) -> None:\n        # execute the main loop\n        # NOTE: Do not change and keep this structure clear for readability.\n        for epoch in range(self.num_epochs):\n            # call internally the training loop\n            # NOTE: override to customize your evaluation routine\n            self.fit_epoch(epoch)\n\n            # call internally the evaluation loop\n            # NOTE: override to customize your evaluation routine\n            valid_stats = self.evaluate()\n\n            self.checkpoint(self.model, epoch, valid_stats)\n\n            state = self.terminate(self.model, epoch, valid_stats)\n            if state == TrainerState.TERMINATE:\n                break\n\n            # END OF THE EPOCH\n            self.scheduler.step()\n\n        ...\n\n    def evaluate(self):\n        ...\n\n    def preprocess(self, x):\n        return x\n\n    def augmentations(self, x):\n        return x\n\n    def checkpoint(self, *args, **kwargs):\n        ...\n\n    def terminate(self, *args, **kwargs):\n        ...\n", "path": "kornia/x/trainer.py"}]}