We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
`VisionDataset` abstract class forces to set 'root' parameter, even if it is unused
### 🐛 Describe the bug

`TypeError: __init__() missing 1 required positional argument: 'root'`

when initializing VisionDataset without `root` param.

```python

from torchvision.transforms import ToTensor
from torchvision.datasets import VisionDataset

class ExtendedVisionDataset(VisionDataset):
  def __init__(self, **kwargs):
    super().__init__(**kwargs)  

transforms = ToTensor()
dataset =ExtendedVisionDataset(transforms =transforms)  # I dont really need root param
```

### Versions

```
PyTorch version: 2.0.0
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.3 LTS (x86_64)
GCC version: (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
Clang version: Could not collect
CMake version: version 3.10.2
Libc version: glibc-2.27

Python version: 3.9.18 (main, Sep 11 2023, 13:41:44)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-88-generic-x86_64-with-glibc2.27
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: Tesla V100-DGXS-32GB
GPU 1: Tesla V100-DGXS-32GB
GPU 2: Tesla V100-DGXS-32GB
GPU 3: Tesla V100-DGXS-32GB

Nvidia driver version: 515.105.01
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.3
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
CPU(s):              40
On-line CPU(s) list: 0-39
Thread(s) per core:  2
Core(s) per socket:  20
Socket(s):           1
NUMA node(s):        1
Vendor ID:           GenuineIntel
CPU family:          6
Model:               79
Model name:          Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz
Stepping:            1
CPU MHz:             2468.528
CPU max MHz:         3600.0000
CPU min MHz:         1200.0000
BogoMIPS:            4397.69
L1d cache:           32K
L1i cache:           32K
L2 cache:            256K
L3 cache:            51200K
NUMA node0 CPU(s):   0-39
Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d

Versions of relevant libraries:
[pip3] numpy==1.24.4
[pip3] torch==2.0.0
[pip3] torchmetrics==0.10.3
[pip3] torchvision==0.15.0
[pip3] triton==2.0.0
[conda] blas                      1.0                         mkl  
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-service               2.4.0            py39h5eee18b_1  
[conda] mkl_fft                   1.3.8            py39h5eee18b_0  
[conda] mkl_random                1.2.4            py39hdb19cb5_0  
[conda] numpy                     1.24.4                   pypi_0    pypi
[conda] pytorch                   2.0.0           py3.9_cuda11.7_cudnn8.5.0_0    pytorch
[conda] pytorch-cuda              11.7                 h778d358_5    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchmetrics              0.10.3             pyhd8ed1ab_0    conda-forge
[conda] torchtriton               2.0.0                      py39    pytorch
[conda] torchvision               0.15.0               py39_cu117    pytorch```

cc @pmeier

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `torchvision/datasets/vision.py`
Content:
```
1 import os
2 from typing import Any, Callable, List, Optional, Tuple
3 
4 import torch.utils.data as data
5 
6 from ..utils import _log_api_usage_once
7 
8 
9 class VisionDataset(data.Dataset):
10     """
11     Base Class For making datasets which are compatible with torchvision.
12     It is necessary to override the ``__getitem__`` and ``__len__`` method.
13 
14     Args:
15         root (string): Root directory of dataset.
16         transforms (callable, optional): A function/transforms that takes in
17             an image and a label and returns the transformed versions of both.
18         transform (callable, optional): A function/transform that  takes in an PIL image
19             and returns a transformed version. E.g, ``transforms.RandomCrop``
20         target_transform (callable, optional): A function/transform that takes in the
21             target and transforms it.
22 
23     .. note::
24 
25         :attr:`transforms` and the combination of :attr:`transform` and :attr:`target_transform` are mutually exclusive.
26     """
27 
28     _repr_indent = 4
29 
30     def __init__(
31         self,
32         root: str,
33         transforms: Optional[Callable] = None,
34         transform: Optional[Callable] = None,
35         target_transform: Optional[Callable] = None,
36     ) -> None:
37         _log_api_usage_once(self)
38         if isinstance(root, str):
39             root = os.path.expanduser(root)
40         self.root = root
41 
42         has_transforms = transforms is not None
43         has_separate_transform = transform is not None or target_transform is not None
44         if has_transforms and has_separate_transform:
45             raise ValueError("Only transforms or transform/target_transform can be passed as argument")
46 
47         # for backwards-compatibility
48         self.transform = transform
49         self.target_transform = target_transform
50 
51         if has_separate_transform:
52             transforms = StandardTransform(transform, target_transform)
53         self.transforms = transforms
54 
55     def __getitem__(self, index: int) -> Any:
56         """
57         Args:
58             index (int): Index
59 
60         Returns:
61             (Any): Sample and meta data, optionally transformed by the respective transforms.
62         """
63         raise NotImplementedError
64 
65     def __len__(self) -> int:
66         raise NotImplementedError
67 
68     def __repr__(self) -> str:
69         head = "Dataset " + self.__class__.__name__
70         body = [f"Number of datapoints: {self.__len__()}"]
71         if self.root is not None:
72             body.append(f"Root location: {self.root}")
73         body += self.extra_repr().splitlines()
74         if hasattr(self, "transforms") and self.transforms is not None:
75             body += [repr(self.transforms)]
76         lines = [head] + [" " * self._repr_indent + line for line in body]
77         return "\n".join(lines)
78 
79     def _format_transform_repr(self, transform: Callable, head: str) -> List[str]:
80         lines = transform.__repr__().splitlines()
81         return [f"{head}{lines[0]}"] + ["{}{}".format(" " * len(head), line) for line in lines[1:]]
82 
83     def extra_repr(self) -> str:
84         return ""
85 
86 
87 class StandardTransform:
88     def __init__(self, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None) -> None:
89         self.transform = transform
90         self.target_transform = target_transform
91 
92     def __call__(self, input: Any, target: Any) -> Tuple[Any, Any]:
93         if self.transform is not None:
94             input = self.transform(input)
95         if self.target_transform is not None:
96             target = self.target_transform(target)
97         return input, target
98 
99     def _format_transform_repr(self, transform: Callable, head: str) -> List[str]:
100         lines = transform.__repr__().splitlines()
101         return [f"{head}{lines[0]}"] + ["{}{}".format(" " * len(head), line) for line in lines[1:]]
102 
103     def __repr__(self) -> str:
104         body = [self.__class__.__name__]
105         if self.transform is not None:
106             body += self._format_transform_repr(self.transform, "Transform: ")
107         if self.target_transform is not None:
108             body += self._format_transform_repr(self.target_transform, "Target transform: ")
109 
110         return "\n".join(body)
111
```
--- 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/torchvision/datasets/vision.py b/torchvision/datasets/vision.py
--- a/torchvision/datasets/vision.py
+++ b/torchvision/datasets/vision.py
@@ -12,7 +12,7 @@
     It is necessary to override the ``__getitem__`` and ``__len__`` method.
 
     Args:
-        root (string): Root directory of dataset.
+        root (string, optional): Root directory of dataset. Only used for `__repr__`.
         transforms (callable, optional): A function/transforms that takes in
             an image and a label and returns the transformed versions of both.
         transform (callable, optional): A function/transform that  takes in an PIL image
@@ -29,7 +29,7 @@
 
     def __init__(
         self,
-        root: str,
+        root: Optional[str] = None,
         transforms: Optional[Callable] = None,
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,

{"golden_diff": "diff --git a/torchvision/datasets/vision.py b/torchvision/datasets/vision.py\n--- a/torchvision/datasets/vision.py\n+++ b/torchvision/datasets/vision.py\n@@ -12,7 +12,7 @@\n     It is necessary to override the ``__getitem__`` and ``__len__`` method.\n \n     Args:\n-        root (string): Root directory of dataset.\n+        root (string, optional): Root directory of dataset. Only used for `__repr__`.\n         transforms (callable, optional): A function/transforms that takes in\n             an image and a label and returns the transformed versions of both.\n         transform (callable, optional): A function/transform that  takes in an PIL image\n@@ -29,7 +29,7 @@\n \n     def __init__(\n         self,\n-        root: str,\n+        root: Optional[str] = None,\n         transforms: Optional[Callable] = None,\n         transform: Optional[Callable] = None,\n         target_transform: Optional[Callable] = None,\n", "issue": "`VisionDataset` abstract class forces to set 'root' parameter, even if it is unused\n### \ud83d\udc1b Describe the bug\n\n`TypeError: __init__() missing 1 required positional argument: 'root'`\r\n\r\nwhen initializing VisionDataset without `root` param.\r\n\r\n```python\r\n\r\nfrom torchvision.transforms import ToTensor\r\nfrom torchvision.datasets import VisionDataset\r\n\r\nclass ExtendedVisionDataset(VisionDataset):\r\n  def __init__(self, **kwargs):\r\n    super().__init__(**kwargs)  \r\n\r\ntransforms = ToTensor()\r\ndataset =ExtendedVisionDataset(transforms =transforms)  # I dont really need root param\r\n```\n\n### Versions\n\n```\r\nPyTorch version: 2.0.0\r\nIs debug build: False\r\nCUDA used to build PyTorch: 11.7\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Ubuntu 18.04.3 LTS (x86_64)\r\nGCC version: (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0\r\nClang version: Could not collect\r\nCMake version: version 3.10.2\r\nLibc version: glibc-2.27\r\n\r\nPython version: 3.9.18 (main, Sep 11 2023, 13:41:44)  [GCC 11.2.0] (64-bit runtime)\r\nPython platform: Linux-5.15.0-88-generic-x86_64-with-glibc2.27\r\nIs CUDA available: True\r\nCUDA runtime version: Could not collect\r\nCUDA_MODULE_LOADING set to: LAZY\r\nGPU models and configuration: \r\nGPU 0: Tesla V100-DGXS-32GB\r\nGPU 1: Tesla V100-DGXS-32GB\r\nGPU 2: Tesla V100-DGXS-32GB\r\nGPU 3: Tesla V100-DGXS-32GB\r\n\r\nNvidia driver version: 515.105.01\r\ncuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.3\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\nIs XNNPACK available: True\r\n\r\nCPU:\r\nArchitecture:        x86_64\r\nCPU op-mode(s):      32-bit, 64-bit\r\nByte Order:          Little Endian\r\nCPU(s):              40\r\nOn-line CPU(s) list: 0-39\r\nThread(s) per core:  2\r\nCore(s) per socket:  20\r\nSocket(s):           1\r\nNUMA node(s):        1\r\nVendor ID:           GenuineIntel\r\nCPU family:          6\r\nModel:               79\r\nModel name:          Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz\r\nStepping:            1\r\nCPU MHz:             2468.528\r\nCPU max MHz:         3600.0000\r\nCPU min MHz:         1200.0000\r\nBogoMIPS:            4397.69\r\nL1d cache:           32K\r\nL1i cache:           32K\r\nL2 cache:            256K\r\nL3 cache:            51200K\r\nNUMA node0 CPU(s):   0-39\r\nFlags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d\r\n\r\nVersions of relevant libraries:\r\n[pip3] numpy==1.24.4\r\n[pip3] torch==2.0.0\r\n[pip3] torchmetrics==0.10.3\r\n[pip3] torchvision==0.15.0\r\n[pip3] triton==2.0.0\r\n[conda] blas                      1.0                         mkl  \r\n[conda] mkl                       2023.1.0         h213fc3f_46344  \r\n[conda] mkl-service               2.4.0            py39h5eee18b_1  \r\n[conda] mkl_fft                   1.3.8            py39h5eee18b_0  \r\n[conda] mkl_random                1.2.4            py39hdb19cb5_0  \r\n[conda] numpy                     1.24.4                   pypi_0    pypi\r\n[conda] pytorch                   2.0.0           py3.9_cuda11.7_cudnn8.5.0_0    pytorch\r\n[conda] pytorch-cuda              11.7                 h778d358_5    pytorch\r\n[conda] pytorch-mutex             1.0                        cuda    pytorch\r\n[conda] torchmetrics              0.10.3             pyhd8ed1ab_0    conda-forge\r\n[conda] torchtriton               2.0.0                      py39    pytorch\r\n[conda] torchvision               0.15.0               py39_cu117    pytorch```\n\ncc @pmeier\n", "before_files": [{"content": "import os\nfrom typing import Any, Callable, List, Optional, Tuple\n\nimport torch.utils.data as data\n\nfrom ..utils import _log_api_usage_once\n\n\nclass VisionDataset(data.Dataset):\n    \"\"\"\n    Base Class For making datasets which are compatible with torchvision.\n    It is necessary to override the ``__getitem__`` and ``__len__`` method.\n\n    Args:\n        root (string): Root directory of dataset.\n        transforms (callable, optional): A function/transforms that takes in\n            an image and a label and returns the transformed versions of both.\n        transform (callable, optional): A function/transform that  takes in an PIL image\n            and returns a transformed version. E.g, ``transforms.RandomCrop``\n        target_transform (callable, optional): A function/transform that takes in the\n            target and transforms it.\n\n    .. note::\n\n        :attr:`transforms` and the combination of :attr:`transform` and :attr:`target_transform` are mutually exclusive.\n    \"\"\"\n\n    _repr_indent = 4\n\n    def __init__(\n        self,\n        root: str,\n        transforms: Optional[Callable] = None,\n        transform: Optional[Callable] = None,\n        target_transform: Optional[Callable] = None,\n    ) -> None:\n        _log_api_usage_once(self)\n        if isinstance(root, str):\n            root = os.path.expanduser(root)\n        self.root = root\n\n        has_transforms = transforms is not None\n        has_separate_transform = transform is not None or target_transform is not None\n        if has_transforms and has_separate_transform:\n            raise ValueError(\"Only transforms or transform/target_transform can be passed as argument\")\n\n        # for backwards-compatibility\n        self.transform = transform\n        self.target_transform = target_transform\n\n        if has_separate_transform:\n            transforms = StandardTransform(transform, target_transform)\n        self.transforms = transforms\n\n    def __getitem__(self, index: int) -> Any:\n        \"\"\"\n        Args:\n            index (int): Index\n\n        Returns:\n            (Any): Sample and meta data, optionally transformed by the respective transforms.\n        \"\"\"\n        raise NotImplementedError\n\n    def __len__(self) -> int:\n        raise NotImplementedError\n\n    def __repr__(self) -> str:\n        head = \"Dataset \" + self.__class__.__name__\n        body = [f\"Number of datapoints: {self.__len__()}\"]\n        if self.root is not None:\n            body.append(f\"Root location: {self.root}\")\n        body += self.extra_repr().splitlines()\n        if hasattr(self, \"transforms\") and self.transforms is not None:\n            body += [repr(self.transforms)]\n        lines = [head] + [\" \" * self._repr_indent + line for line in body]\n        return \"\\n\".join(lines)\n\n    def _format_transform_repr(self, transform: Callable, head: str) -> List[str]:\n        lines = transform.__repr__().splitlines()\n        return [f\"{head}{lines[0]}\"] + [\"{}{}\".format(\" \" * len(head), line) for line in lines[1:]]\n\n    def extra_repr(self) -> str:\n        return \"\"\n\n\nclass StandardTransform:\n    def __init__(self, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None) -> None:\n        self.transform = transform\n        self.target_transform = target_transform\n\n    def __call__(self, input: Any, target: Any) -> Tuple[Any, Any]:\n        if self.transform is not None:\n            input = self.transform(input)\n        if self.target_transform is not None:\n            target = self.target_transform(target)\n        return input, target\n\n    def _format_transform_repr(self, transform: Callable, head: str) -> List[str]:\n        lines = transform.__repr__().splitlines()\n        return [f\"{head}{lines[0]}\"] + [\"{}{}\".format(\" \" * len(head), line) for line in lines[1:]]\n\n    def __repr__(self) -> str:\n        body = [self.__class__.__name__]\n        if self.transform is not None:\n            body += self._format_transform_repr(self.transform, \"Transform: \")\n        if self.target_transform is not None:\n            body += self._format_transform_repr(self.target_transform, \"Target transform: \")\n\n        return \"\\n\".join(body)\n", "path": "torchvision/datasets/vision.py"}], "after_files": [{"content": "import os\nfrom typing import Any, Callable, List, Optional, Tuple\n\nimport torch.utils.data as data\n\nfrom ..utils import _log_api_usage_once\n\n\nclass VisionDataset(data.Dataset):\n    \"\"\"\n    Base Class For making datasets which are compatible with torchvision.\n    It is necessary to override the ``__getitem__`` and ``__len__`` method.\n\n    Args:\n        root (string, optional): Root directory of dataset. Only used for `__repr__`.\n        transforms (callable, optional): A function/transforms that takes in\n            an image and a label and returns the transformed versions of both.\n        transform (callable, optional): A function/transform that  takes in an PIL image\n            and returns a transformed version. E.g, ``transforms.RandomCrop``\n        target_transform (callable, optional): A function/transform that takes in the\n            target and transforms it.\n\n    .. note::\n\n        :attr:`transforms` and the combination of :attr:`transform` and :attr:`target_transform` are mutually exclusive.\n    \"\"\"\n\n    _repr_indent = 4\n\n    def __init__(\n        self,\n        root: Optional[str] = None,\n        transforms: Optional[Callable] = None,\n        transform: Optional[Callable] = None,\n        target_transform: Optional[Callable] = None,\n    ) -> None:\n        _log_api_usage_once(self)\n        if isinstance(root, str):\n            root = os.path.expanduser(root)\n        self.root = root\n\n        has_transforms = transforms is not None\n        has_separate_transform = transform is not None or target_transform is not None\n        if has_transforms and has_separate_transform:\n            raise ValueError(\"Only transforms or transform/target_transform can be passed as argument\")\n\n        # for backwards-compatibility\n        self.transform = transform\n        self.target_transform = target_transform\n\n        if has_separate_transform:\n            transforms = StandardTransform(transform, target_transform)\n        self.transforms = transforms\n\n    def __getitem__(self, index: int) -> Any:\n        \"\"\"\n        Args:\n            index (int): Index\n\n        Returns:\n            (Any): Sample and meta data, optionally transformed by the respective transforms.\n        \"\"\"\n        raise NotImplementedError\n\n    def __len__(self) -> int:\n        raise NotImplementedError\n\n    def __repr__(self) -> str:\n        head = \"Dataset \" + self.__class__.__name__\n        body = [f\"Number of datapoints: {self.__len__()}\"]\n        if self.root is not None:\n            body.append(f\"Root location: {self.root}\")\n        body += self.extra_repr().splitlines()\n        if hasattr(self, \"transforms\") and self.transforms is not None:\n            body += [repr(self.transforms)]\n        lines = [head] + [\" \" * self._repr_indent + line for line in body]\n        return \"\\n\".join(lines)\n\n    def _format_transform_repr(self, transform: Callable, head: str) -> List[str]:\n        lines = transform.__repr__().splitlines()\n        return [f\"{head}{lines[0]}\"] + [\"{}{}\".format(\" \" * len(head), line) for line in lines[1:]]\n\n    def extra_repr(self) -> str:\n        return \"\"\n\n\nclass StandardTransform:\n    def __init__(self, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None) -> None:\n        self.transform = transform\n        self.target_transform = target_transform\n\n    def __call__(self, input: Any, target: Any) -> Tuple[Any, Any]:\n        if self.transform is not None:\n            input = self.transform(input)\n        if self.target_transform is not None:\n            target = self.target_transform(target)\n        return input, target\n\n    def _format_transform_repr(self, transform: Callable, head: str) -> List[str]:\n        lines = transform.__repr__().splitlines()\n        return [f\"{head}{lines[0]}\"] + [\"{}{}\".format(\" \" * len(head), line) for line in lines[1:]]\n\n    def __repr__(self) -> str:\n        body = [self.__class__.__name__]\n        if self.transform is not None:\n            body += self._format_transform_repr(self.transform, \"Transform: \")\n        if self.target_transform is not None:\n            body += self._format_transform_repr(self.target_transform, \"Target transform: \")\n\n        return \"\\n\".join(body)\n", "path": "torchvision/datasets/vision.py"}]}