We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Places365 dataset redundant path.join under _check_integrity
## 🐛 Bug

Redundant path.join at line 165 under _check_integrity in torchvision/datasets/places365.py

path.join is already called at line 116 and line 131 under load_categories and load_file_list

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/places365.py`
Content:
```
1 import os
2 from os import path
3 from typing import Any, Callable, Dict, List, Optional, Tuple
4 from urllib.parse import urljoin
5 
6 from .folder import default_loader
7 from .utils import verify_str_arg, check_integrity, download_and_extract_archive
8 from .vision import VisionDataset
9 
10 
11 class Places365(VisionDataset):
12     r"""`Places365 <http://places2.csail.mit.edu/index.html>`_ classification dataset.
13 
14     Args:
15         root (string): Root directory of the Places365 dataset.
16         split (string, optional): The dataset split. Can be one of ``train-standard`` (default), ``train-challendge``,
17             ``val``.
18         small (bool, optional): If ``True``, uses the small images, i. e. resized to 256 x 256 pixels, instead of the
19             high resolution ones.
20         download (bool, optional): If ``True``, downloads the dataset components and places them in ``root``. Already
21             downloaded archives are not downloaded again.
22         transform (callable, optional): A function/transform that  takes in an PIL image
23             and returns a transformed version. E.g, ``transforms.RandomCrop``
24         target_transform (callable, optional): A function/transform that takes in the
25             target and transforms it.
26         loader (callable, optional): A function to load an image given its path.
27 
28      Attributes:
29         classes (list): List of the class names.
30         class_to_idx (dict): Dict with items (class_name, class_index).
31         imgs (list): List of (image path, class_index) tuples
32         targets (list): The class_index value for each image in the dataset
33 
34     Raises:
35         RuntimeError: If ``download is False`` and the meta files, i. e. the devkit, are not present or corrupted.
36         RuntimeError: If ``download is True`` and the image archive is already extracted.
37     """
38     _SPLITS = ("train-standard", "train-challenge", "val")
39     _BASE_URL = "http://data.csail.mit.edu/places/places365/"
40     # {variant: (archive, md5)}
41     _DEVKIT_META = {
42         "standard": ("filelist_places365-standard.tar", "35a0585fee1fa656440f3ab298f8479c"),
43         "challenge": ("filelist_places365-challenge.tar", "70a8307e459c3de41690a7c76c931734"),
44     }
45     # (file, md5)
46     _CATEGORIES_META = ("categories_places365.txt", "06c963b85866bd0649f97cb43dd16673")
47     # {split: (file, md5)}
48     _FILE_LIST_META = {
49         "train-standard": ("places365_train_standard.txt", "30f37515461640559006b8329efbed1a"),
50         "train-challenge": ("places365_train_challenge.txt", "b2931dc997b8c33c27e7329c073a6b57"),
51         "val": ("places365_val.txt", "e9f2fd57bfd9d07630173f4e8708e4b1"),
52     }
53     # {(split, small): (file, md5)}
54     _IMAGES_META = {
55         ("train-standard", False): ("train_large_places365standard.tar", "67e186b496a84c929568076ed01a8aa1"),
56         ("train-challenge", False): ("train_large_places365challenge.tar", "605f18e68e510c82b958664ea134545f"),
57         ("val", False): ("val_large.tar", "9b71c4993ad89d2d8bcbdc4aef38042f"),
58         ("train-standard", True): ("train_256_places365standard.tar", "53ca1c756c3d1e7809517cc47c5561c5"),
59         ("train-challenge", True): ("train_256_places365challenge.tar", "741915038a5e3471ec7332404dfb64ef"),
60         ("val", True): ("val_256.tar", "e27b17d8d44f4af9a78502beb927f808"),
61     }
62 
63     def __init__(
64         self,
65         root: str,
66         split: str = "train-standard",
67         small: bool = False,
68         download: bool = False,
69         transform: Optional[Callable] = None,
70         target_transform: Optional[Callable] = None,
71         loader: Callable[[str], Any] = default_loader,
72     ) -> None:
73         super().__init__(root, transform=transform, target_transform=target_transform)
74 
75         self.split = self._verify_split(split)
76         self.small = small
77         self.loader = loader
78 
79         self.classes, self.class_to_idx = self.load_categories(download)
80         self.imgs, self.targets = self.load_file_list(download)
81 
82         if download:
83             self.download_images()
84 
85     def __getitem__(self, index: int) -> Tuple[Any, Any]:
86         file, target = self.imgs[index]
87         image = self.loader(file)
88 
89         if self.transforms is not None:
90             image, target = self.transforms(image, target)
91 
92         return image, target
93 
94     def __len__(self) -> int:
95         return len(self.imgs)
96 
97     @property
98     def variant(self) -> str:
99         return "challenge" if "challenge" in self.split else "standard"
100 
101     @property
102     def images_dir(self) -> str:
103         size = "256" if self.small else "large"
104         if self.split.startswith("train"):
105             dir = f"data_{size}_{self.variant}"
106         else:
107             dir = f"{self.split}_{size}"
108         return path.join(self.root, dir)
109 
110     def load_categories(self, download: bool = True) -> Tuple[List[str], Dict[str, int]]:
111         def process(line: str) -> Tuple[str, int]:
112             cls, idx = line.split()
113             return cls, int(idx)
114 
115         file, md5 = self._CATEGORIES_META
116         file = path.join(self.root, file)
117         if not self._check_integrity(file, md5, download):
118             self.download_devkit()
119 
120         with open(file, "r") as fh:
121             class_to_idx = dict(process(line) for line in fh)
122 
123         return sorted(class_to_idx.keys()), class_to_idx
124 
125     def load_file_list(self, download: bool = True) -> Tuple[List[Tuple[str, int]], List[int]]:
126         def process(line: str, sep="/") -> Tuple[str, int]:
127             image, idx = line.split()
128             return path.join(self.images_dir, image.lstrip(sep).replace(sep, os.sep)), int(idx)
129 
130         file, md5 = self._FILE_LIST_META[self.split]
131         file = path.join(self.root, file)
132         if not self._check_integrity(file, md5, download):
133             self.download_devkit()
134 
135         with open(file, "r") as fh:
136             images = [process(line) for line in fh]
137 
138         _, targets = zip(*images)
139         return images, list(targets)
140 
141     def download_devkit(self) -> None:
142         file, md5 = self._DEVKIT_META[self.variant]
143         download_and_extract_archive(urljoin(self._BASE_URL, file), self.root, md5=md5)
144 
145     def download_images(self) -> None:
146         if path.exists(self.images_dir):
147             raise RuntimeError(
148                 f"The directory {self.images_dir} already exists. If you want to re-download or re-extract the images, "
149                 f"delete the directory."
150             )
151 
152         file, md5 = self._IMAGES_META[(self.split, self.small)]
153         download_and_extract_archive(urljoin(self._BASE_URL, file), self.root, md5=md5)
154 
155         if self.split.startswith("train"):
156             os.rename(self.images_dir.rsplit("_", 1)[0], self.images_dir)
157 
158     def extra_repr(self) -> str:
159         return "\n".join(("Split: {split}", "Small: {small}")).format(**self.__dict__)
160 
161     def _verify_split(self, split: str) -> str:
162         return verify_str_arg(split, "split", self._SPLITS)
163 
164     def _check_integrity(self, file: str, md5: str, download: bool) -> bool:
165         integrity = check_integrity(path.join(self.root, file), md5=md5)
166         if not integrity and not download:
167             raise RuntimeError(
168                 f"The file {file} does not exist or is corrupted. You can set download=True to download it."
169             )
170         return integrity
171
```
--- 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/places365.py b/torchvision/datasets/places365.py
--- a/torchvision/datasets/places365.py
+++ b/torchvision/datasets/places365.py
@@ -162,7 +162,7 @@
         return verify_str_arg(split, "split", self._SPLITS)
 
     def _check_integrity(self, file: str, md5: str, download: bool) -> bool:
-        integrity = check_integrity(path.join(self.root, file), md5=md5)
+        integrity = check_integrity(file, md5=md5)
         if not integrity and not download:
             raise RuntimeError(
                 f"The file {file} does not exist or is corrupted. You can set download=True to download it."

{"golden_diff": "diff --git a/torchvision/datasets/places365.py b/torchvision/datasets/places365.py\n--- a/torchvision/datasets/places365.py\n+++ b/torchvision/datasets/places365.py\n@@ -162,7 +162,7 @@\n         return verify_str_arg(split, \"split\", self._SPLITS)\n \n     def _check_integrity(self, file: str, md5: str, download: bool) -> bool:\n-        integrity = check_integrity(path.join(self.root, file), md5=md5)\n+        integrity = check_integrity(file, md5=md5)\n         if not integrity and not download:\n             raise RuntimeError(\n                 f\"The file {file} does not exist or is corrupted. You can set download=True to download it.\"\n", "issue": "Places365 dataset redundant path.join under _check_integrity\n## \ud83d\udc1b Bug\r\n\r\nRedundant path.join at line 165 under _check_integrity in torchvision/datasets/places365.py\r\n\r\npath.join is already called at line 116 and line 131 under load_categories and load_file_list\n\ncc @pmeier\n", "before_files": [{"content": "import os\nfrom os import path\nfrom typing import Any, Callable, Dict, List, Optional, Tuple\nfrom urllib.parse import urljoin\n\nfrom .folder import default_loader\nfrom .utils import verify_str_arg, check_integrity, download_and_extract_archive\nfrom .vision import VisionDataset\n\n\nclass Places365(VisionDataset):\n    r\"\"\"`Places365 <http://places2.csail.mit.edu/index.html>`_ classification dataset.\n\n    Args:\n        root (string): Root directory of the Places365 dataset.\n        split (string, optional): The dataset split. Can be one of ``train-standard`` (default), ``train-challendge``,\n            ``val``.\n        small (bool, optional): If ``True``, uses the small images, i. e. resized to 256 x 256 pixels, instead of the\n            high resolution ones.\n        download (bool, optional): If ``True``, downloads the dataset components and places them in ``root``. Already\n            downloaded archives are not downloaded again.\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        loader (callable, optional): A function to load an image given its path.\n\n     Attributes:\n        classes (list): List of the class names.\n        class_to_idx (dict): Dict with items (class_name, class_index).\n        imgs (list): List of (image path, class_index) tuples\n        targets (list): The class_index value for each image in the dataset\n\n    Raises:\n        RuntimeError: If ``download is False`` and the meta files, i. e. the devkit, are not present or corrupted.\n        RuntimeError: If ``download is True`` and the image archive is already extracted.\n    \"\"\"\n    _SPLITS = (\"train-standard\", \"train-challenge\", \"val\")\n    _BASE_URL = \"http://data.csail.mit.edu/places/places365/\"\n    # {variant: (archive, md5)}\n    _DEVKIT_META = {\n        \"standard\": (\"filelist_places365-standard.tar\", \"35a0585fee1fa656440f3ab298f8479c\"),\n        \"challenge\": (\"filelist_places365-challenge.tar\", \"70a8307e459c3de41690a7c76c931734\"),\n    }\n    # (file, md5)\n    _CATEGORIES_META = (\"categories_places365.txt\", \"06c963b85866bd0649f97cb43dd16673\")\n    # {split: (file, md5)}\n    _FILE_LIST_META = {\n        \"train-standard\": (\"places365_train_standard.txt\", \"30f37515461640559006b8329efbed1a\"),\n        \"train-challenge\": (\"places365_train_challenge.txt\", \"b2931dc997b8c33c27e7329c073a6b57\"),\n        \"val\": (\"places365_val.txt\", \"e9f2fd57bfd9d07630173f4e8708e4b1\"),\n    }\n    # {(split, small): (file, md5)}\n    _IMAGES_META = {\n        (\"train-standard\", False): (\"train_large_places365standard.tar\", \"67e186b496a84c929568076ed01a8aa1\"),\n        (\"train-challenge\", False): (\"train_large_places365challenge.tar\", \"605f18e68e510c82b958664ea134545f\"),\n        (\"val\", False): (\"val_large.tar\", \"9b71c4993ad89d2d8bcbdc4aef38042f\"),\n        (\"train-standard\", True): (\"train_256_places365standard.tar\", \"53ca1c756c3d1e7809517cc47c5561c5\"),\n        (\"train-challenge\", True): (\"train_256_places365challenge.tar\", \"741915038a5e3471ec7332404dfb64ef\"),\n        (\"val\", True): (\"val_256.tar\", \"e27b17d8d44f4af9a78502beb927f808\"),\n    }\n\n    def __init__(\n        self,\n        root: str,\n        split: str = \"train-standard\",\n        small: bool = False,\n        download: bool = False,\n        transform: Optional[Callable] = None,\n        target_transform: Optional[Callable] = None,\n        loader: Callable[[str], Any] = default_loader,\n    ) -> None:\n        super().__init__(root, transform=transform, target_transform=target_transform)\n\n        self.split = self._verify_split(split)\n        self.small = small\n        self.loader = loader\n\n        self.classes, self.class_to_idx = self.load_categories(download)\n        self.imgs, self.targets = self.load_file_list(download)\n\n        if download:\n            self.download_images()\n\n    def __getitem__(self, index: int) -> Tuple[Any, Any]:\n        file, target = self.imgs[index]\n        image = self.loader(file)\n\n        if self.transforms is not None:\n            image, target = self.transforms(image, target)\n\n        return image, target\n\n    def __len__(self) -> int:\n        return len(self.imgs)\n\n    @property\n    def variant(self) -> str:\n        return \"challenge\" if \"challenge\" in self.split else \"standard\"\n\n    @property\n    def images_dir(self) -> str:\n        size = \"256\" if self.small else \"large\"\n        if self.split.startswith(\"train\"):\n            dir = f\"data_{size}_{self.variant}\"\n        else:\n            dir = f\"{self.split}_{size}\"\n        return path.join(self.root, dir)\n\n    def load_categories(self, download: bool = True) -> Tuple[List[str], Dict[str, int]]:\n        def process(line: str) -> Tuple[str, int]:\n            cls, idx = line.split()\n            return cls, int(idx)\n\n        file, md5 = self._CATEGORIES_META\n        file = path.join(self.root, file)\n        if not self._check_integrity(file, md5, download):\n            self.download_devkit()\n\n        with open(file, \"r\") as fh:\n            class_to_idx = dict(process(line) for line in fh)\n\n        return sorted(class_to_idx.keys()), class_to_idx\n\n    def load_file_list(self, download: bool = True) -> Tuple[List[Tuple[str, int]], List[int]]:\n        def process(line: str, sep=\"/\") -> Tuple[str, int]:\n            image, idx = line.split()\n            return path.join(self.images_dir, image.lstrip(sep).replace(sep, os.sep)), int(idx)\n\n        file, md5 = self._FILE_LIST_META[self.split]\n        file = path.join(self.root, file)\n        if not self._check_integrity(file, md5, download):\n            self.download_devkit()\n\n        with open(file, \"r\") as fh:\n            images = [process(line) for line in fh]\n\n        _, targets = zip(*images)\n        return images, list(targets)\n\n    def download_devkit(self) -> None:\n        file, md5 = self._DEVKIT_META[self.variant]\n        download_and_extract_archive(urljoin(self._BASE_URL, file), self.root, md5=md5)\n\n    def download_images(self) -> None:\n        if path.exists(self.images_dir):\n            raise RuntimeError(\n                f\"The directory {self.images_dir} already exists. If you want to re-download or re-extract the images, \"\n                f\"delete the directory.\"\n            )\n\n        file, md5 = self._IMAGES_META[(self.split, self.small)]\n        download_and_extract_archive(urljoin(self._BASE_URL, file), self.root, md5=md5)\n\n        if self.split.startswith(\"train\"):\n            os.rename(self.images_dir.rsplit(\"_\", 1)[0], self.images_dir)\n\n    def extra_repr(self) -> str:\n        return \"\\n\".join((\"Split: {split}\", \"Small: {small}\")).format(**self.__dict__)\n\n    def _verify_split(self, split: str) -> str:\n        return verify_str_arg(split, \"split\", self._SPLITS)\n\n    def _check_integrity(self, file: str, md5: str, download: bool) -> bool:\n        integrity = check_integrity(path.join(self.root, file), md5=md5)\n        if not integrity and not download:\n            raise RuntimeError(\n                f\"The file {file} does not exist or is corrupted. You can set download=True to download it.\"\n            )\n        return integrity\n", "path": "torchvision/datasets/places365.py"}], "after_files": [{"content": "import os\nfrom os import path\nfrom typing import Any, Callable, Dict, List, Optional, Tuple\nfrom urllib.parse import urljoin\n\nfrom .folder import default_loader\nfrom .utils import verify_str_arg, check_integrity, download_and_extract_archive\nfrom .vision import VisionDataset\n\n\nclass Places365(VisionDataset):\n    r\"\"\"`Places365 <http://places2.csail.mit.edu/index.html>`_ classification dataset.\n\n    Args:\n        root (string): Root directory of the Places365 dataset.\n        split (string, optional): The dataset split. Can be one of ``train-standard`` (default), ``train-challendge``,\n            ``val``.\n        small (bool, optional): If ``True``, uses the small images, i. e. resized to 256 x 256 pixels, instead of the\n            high resolution ones.\n        download (bool, optional): If ``True``, downloads the dataset components and places them in ``root``. Already\n            downloaded archives are not downloaded again.\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        loader (callable, optional): A function to load an image given its path.\n\n     Attributes:\n        classes (list): List of the class names.\n        class_to_idx (dict): Dict with items (class_name, class_index).\n        imgs (list): List of (image path, class_index) tuples\n        targets (list): The class_index value for each image in the dataset\n\n    Raises:\n        RuntimeError: If ``download is False`` and the meta files, i. e. the devkit, are not present or corrupted.\n        RuntimeError: If ``download is True`` and the image archive is already extracted.\n    \"\"\"\n    _SPLITS = (\"train-standard\", \"train-challenge\", \"val\")\n    _BASE_URL = \"http://data.csail.mit.edu/places/places365/\"\n    # {variant: (archive, md5)}\n    _DEVKIT_META = {\n        \"standard\": (\"filelist_places365-standard.tar\", \"35a0585fee1fa656440f3ab298f8479c\"),\n        \"challenge\": (\"filelist_places365-challenge.tar\", \"70a8307e459c3de41690a7c76c931734\"),\n    }\n    # (file, md5)\n    _CATEGORIES_META = (\"categories_places365.txt\", \"06c963b85866bd0649f97cb43dd16673\")\n    # {split: (file, md5)}\n    _FILE_LIST_META = {\n        \"train-standard\": (\"places365_train_standard.txt\", \"30f37515461640559006b8329efbed1a\"),\n        \"train-challenge\": (\"places365_train_challenge.txt\", \"b2931dc997b8c33c27e7329c073a6b57\"),\n        \"val\": (\"places365_val.txt\", \"e9f2fd57bfd9d07630173f4e8708e4b1\"),\n    }\n    # {(split, small): (file, md5)}\n    _IMAGES_META = {\n        (\"train-standard\", False): (\"train_large_places365standard.tar\", \"67e186b496a84c929568076ed01a8aa1\"),\n        (\"train-challenge\", False): (\"train_large_places365challenge.tar\", \"605f18e68e510c82b958664ea134545f\"),\n        (\"val\", False): (\"val_large.tar\", \"9b71c4993ad89d2d8bcbdc4aef38042f\"),\n        (\"train-standard\", True): (\"train_256_places365standard.tar\", \"53ca1c756c3d1e7809517cc47c5561c5\"),\n        (\"train-challenge\", True): (\"train_256_places365challenge.tar\", \"741915038a5e3471ec7332404dfb64ef\"),\n        (\"val\", True): (\"val_256.tar\", \"e27b17d8d44f4af9a78502beb927f808\"),\n    }\n\n    def __init__(\n        self,\n        root: str,\n        split: str = \"train-standard\",\n        small: bool = False,\n        download: bool = False,\n        transform: Optional[Callable] = None,\n        target_transform: Optional[Callable] = None,\n        loader: Callable[[str], Any] = default_loader,\n    ) -> None:\n        super().__init__(root, transform=transform, target_transform=target_transform)\n\n        self.split = self._verify_split(split)\n        self.small = small\n        self.loader = loader\n\n        self.classes, self.class_to_idx = self.load_categories(download)\n        self.imgs, self.targets = self.load_file_list(download)\n\n        if download:\n            self.download_images()\n\n    def __getitem__(self, index: int) -> Tuple[Any, Any]:\n        file, target = self.imgs[index]\n        image = self.loader(file)\n\n        if self.transforms is not None:\n            image, target = self.transforms(image, target)\n\n        return image, target\n\n    def __len__(self) -> int:\n        return len(self.imgs)\n\n    @property\n    def variant(self) -> str:\n        return \"challenge\" if \"challenge\" in self.split else \"standard\"\n\n    @property\n    def images_dir(self) -> str:\n        size = \"256\" if self.small else \"large\"\n        if self.split.startswith(\"train\"):\n            dir = f\"data_{size}_{self.variant}\"\n        else:\n            dir = f\"{self.split}_{size}\"\n        return path.join(self.root, dir)\n\n    def load_categories(self, download: bool = True) -> Tuple[List[str], Dict[str, int]]:\n        def process(line: str) -> Tuple[str, int]:\n            cls, idx = line.split()\n            return cls, int(idx)\n\n        file, md5 = self._CATEGORIES_META\n        file = path.join(self.root, file)\n        if not self._check_integrity(file, md5, download):\n            self.download_devkit()\n\n        with open(file, \"r\") as fh:\n            class_to_idx = dict(process(line) for line in fh)\n\n        return sorted(class_to_idx.keys()), class_to_idx\n\n    def load_file_list(self, download: bool = True) -> Tuple[List[Tuple[str, int]], List[int]]:\n        def process(line: str, sep=\"/\") -> Tuple[str, int]:\n            image, idx = line.split()\n            return path.join(self.images_dir, image.lstrip(sep).replace(sep, os.sep)), int(idx)\n\n        file, md5 = self._FILE_LIST_META[self.split]\n        file = path.join(self.root, file)\n        if not self._check_integrity(file, md5, download):\n            self.download_devkit()\n\n        with open(file, \"r\") as fh:\n            images = [process(line) for line in fh]\n\n        _, targets = zip(*images)\n        return images, list(targets)\n\n    def download_devkit(self) -> None:\n        file, md5 = self._DEVKIT_META[self.variant]\n        download_and_extract_archive(urljoin(self._BASE_URL, file), self.root, md5=md5)\n\n    def download_images(self) -> None:\n        if path.exists(self.images_dir):\n            raise RuntimeError(\n                f\"The directory {self.images_dir} already exists. If you want to re-download or re-extract the images, \"\n                f\"delete the directory.\"\n            )\n\n        file, md5 = self._IMAGES_META[(self.split, self.small)]\n        download_and_extract_archive(urljoin(self._BASE_URL, file), self.root, md5=md5)\n\n        if self.split.startswith(\"train\"):\n            os.rename(self.images_dir.rsplit(\"_\", 1)[0], self.images_dir)\n\n    def extra_repr(self) -> str:\n        return \"\\n\".join((\"Split: {split}\", \"Small: {small}\")).format(**self.__dict__)\n\n    def _verify_split(self, split: str) -> str:\n        return verify_str_arg(split, \"split\", self._SPLITS)\n\n    def _check_integrity(self, file: str, md5: str, download: bool) -> bool:\n        integrity = check_integrity(file, md5=md5)\n        if not integrity and not download:\n            raise RuntimeError(\n                f\"The file {file} does not exist or is corrupted. You can set download=True to download it.\"\n            )\n        return integrity\n", "path": "torchvision/datasets/places365.py"}]}