We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
todo-decorator-remove-solved
Removed the code as the issue is closed.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `torchtext/datasets/multi30k.py`
Content:
```
1 import os
2 from functools import partial
3 from typing import Union, Tuple
4 
5 from torchtext._internal.module_utils import is_module_available
6 from torchtext.data.datasets_utils import (
7     _wrap_split_argument,
8     _create_dataset_directory,
9 )
10 
11 if is_module_available("torchdata"):
12     from torchdata.datapipes.iter import FileOpener, IterableWrapper
13     from torchtext._download_hooks import HttpReader
14 
15 # TODO: Update URL to original once the server is back up (see https://github.com/pytorch/text/issues/1756)
16 URL = {
17     "train": r"https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/training.tar.gz",
18     "valid": r"https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/validation.tar.gz",
19     "test": r"https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/mmt16_task1_test.tar.gz",
20 }
21 
22 MD5 = {
23     "train": "20140d013d05dd9a72dfde46478663ba05737ce983f478f960c1123c6671be5e",
24     "valid": "a7aa20e9ebd5ba5adce7909498b94410996040857154dab029851af3a866da8c",
25     "test": "6d1ca1dba99e2c5dd54cae1226ff11c2551e6ce63527ebb072a1f70f72a5cd36",
26 }
27 
28 _PREFIX = {
29     "train": "train",
30     "valid": "val",
31     "test": "test",
32 }
33 
34 NUM_LINES = {
35     "train": 29000,
36     "valid": 1014,
37     "test": 1000,
38 }
39 
40 DATASET_NAME = "Multi30k"
41 
42 
43 def _filepath_fn(root, split, _=None):
44     return os.path.join(root, os.path.basename(URL[split]))
45 
46 
47 def _decompressed_filepath_fn(root, split, language_pair, i, _):
48     return os.path.join(root, f"{_PREFIX[split]}.{language_pair[i]}")
49 
50 
51 def _filter_fn(split, language_pair, i, x):
52     return f"{_PREFIX[split]}.{language_pair[i]}" in x[0]
53 
54 
55 @_create_dataset_directory(dataset_name=DATASET_NAME)
56 @_wrap_split_argument(("train", "valid", "test"))
57 def Multi30k(root: str, split: Union[Tuple[str], str], language_pair: Tuple[str] = ("de", "en")):
58     """Multi30k dataset
59 
60     .. warning::
61 
62         using datapipes is still currently subject to a few caveats. if you wish
63         to use this dataset with shuffling, multi-processing, or distributed
64         learning, please see :ref:`this note <datapipes_warnings>` for further
65         instructions.
66 
67     For additional details refer to https://www.statmt.org/wmt16/multimodal-task.html#task1
68 
69     Number of lines per split:
70         - train: 29000
71         - valid: 1014
72         - test: 1000
73 
74     Args:
75         root: Directory where the datasets are saved. Default: os.path.expanduser('~/.torchtext/cache')
76         split: split or splits to be returned. Can be a string or tuple of strings. Default: ('train', 'valid', 'test')
77         language_pair: tuple or list containing src and tgt language. Available options are ('de','en') and ('en', 'de')
78 
79     :return: DataPipe that yields tuple of source and target sentences
80     :rtype: (str, str)
81     """
82 
83     assert len(language_pair) == 2, "language_pair must contain only 2 elements: src and tgt language respectively"
84     assert tuple(sorted(language_pair)) == (
85         "de",
86         "en",
87     ), "language_pair must be either ('de','en') or ('en', 'de')"
88 
89     if not is_module_available("torchdata"):
90         raise ModuleNotFoundError(
91             "Package `torchdata` not found. Please install following instructions at https://github.com/pytorch/data"
92         )
93 
94     url_dp = IterableWrapper([URL[split]])
95 
96     cache_compressed_dp = url_dp.on_disk_cache(
97         filepath_fn=partial(_filepath_fn, root, split),
98         hash_dict={_filepath_fn(root, split): MD5[split]},
99         hash_type="sha256",
100     )
101     cache_compressed_dp = HttpReader(cache_compressed_dp).end_caching(mode="wb", same_filepath_fn=True)
102 
103     cache_compressed_dp_1, cache_compressed_dp_2 = cache_compressed_dp.fork(num_instances=2)
104 
105     src_cache_decompressed_dp = cache_compressed_dp_1.on_disk_cache(
106         filepath_fn=partial(_decompressed_filepath_fn, root, split, language_pair, 0)
107     )
108     src_cache_decompressed_dp = (
109         FileOpener(src_cache_decompressed_dp, mode="b")
110         .load_from_tar()
111         .filter(partial(_filter_fn, split, language_pair, 0))
112     )
113     src_cache_decompressed_dp = src_cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)
114 
115     tgt_cache_decompressed_dp = cache_compressed_dp_2.on_disk_cache(
116         filepath_fn=partial(_decompressed_filepath_fn, root, split, language_pair, 1)
117     )
118     tgt_cache_decompressed_dp = (
119         FileOpener(tgt_cache_decompressed_dp, mode="b")
120         .load_from_tar()
121         .filter(partial(_filter_fn, split, language_pair, 1))
122     )
123     tgt_cache_decompressed_dp = tgt_cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)
124 
125     src_data_dp = FileOpener(src_cache_decompressed_dp, encoding="utf-8").readlines(
126         return_path=False, strip_newline=True
127     )
128     tgt_data_dp = FileOpener(tgt_cache_decompressed_dp, encoding="utf-8").readlines(
129         return_path=False, strip_newline=True
130     )
131 
132     return src_data_dp.zip(tgt_data_dp).shuffle().set_shuffle(False).sharding_filter()
133
```
--- 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/torchtext/datasets/multi30k.py b/torchtext/datasets/multi30k.py
--- a/torchtext/datasets/multi30k.py
+++ b/torchtext/datasets/multi30k.py
@@ -12,11 +12,10 @@
     from torchdata.datapipes.iter import FileOpener, IterableWrapper
     from torchtext._download_hooks import HttpReader
 
-# TODO: Update URL to original once the server is back up (see https://github.com/pytorch/text/issues/1756)
 URL = {
-    "train": r"https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/training.tar.gz",
-    "valid": r"https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/validation.tar.gz",
-    "test": r"https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/mmt16_task1_test.tar.gz",
+    "train": "http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/training.tar.gz",
+    "valid": "http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/validation.tar.gz",
+    "test": "http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/mmt16_task1_test.tar.gz",
 }
 
 MD5 = {

{"golden_diff": "diff --git a/torchtext/datasets/multi30k.py b/torchtext/datasets/multi30k.py\n--- a/torchtext/datasets/multi30k.py\n+++ b/torchtext/datasets/multi30k.py\n@@ -12,11 +12,10 @@\n     from torchdata.datapipes.iter import FileOpener, IterableWrapper\n     from torchtext._download_hooks import HttpReader\n \n-# TODO: Update URL to original once the server is back up (see https://github.com/pytorch/text/issues/1756)\n URL = {\n-    \"train\": r\"https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/training.tar.gz\",\n-    \"valid\": r\"https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/validation.tar.gz\",\n-    \"test\": r\"https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/mmt16_task1_test.tar.gz\",\n+    \"train\": \"http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/training.tar.gz\",\n+    \"valid\": \"http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/validation.tar.gz\",\n+    \"test\": \"http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/mmt16_task1_test.tar.gz\",\n }\n \n MD5 = {\n", "issue": "todo-decorator-remove-solved\nRemoved the code as the issue is closed.\n", "before_files": [{"content": "import os\nfrom functools import partial\nfrom typing import Union, Tuple\n\nfrom torchtext._internal.module_utils import is_module_available\nfrom torchtext.data.datasets_utils import (\n    _wrap_split_argument,\n    _create_dataset_directory,\n)\n\nif is_module_available(\"torchdata\"):\n    from torchdata.datapipes.iter import FileOpener, IterableWrapper\n    from torchtext._download_hooks import HttpReader\n\n# TODO: Update URL to original once the server is back up (see https://github.com/pytorch/text/issues/1756)\nURL = {\n    \"train\": r\"https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/training.tar.gz\",\n    \"valid\": r\"https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/validation.tar.gz\",\n    \"test\": r\"https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/mmt16_task1_test.tar.gz\",\n}\n\nMD5 = {\n    \"train\": \"20140d013d05dd9a72dfde46478663ba05737ce983f478f960c1123c6671be5e\",\n    \"valid\": \"a7aa20e9ebd5ba5adce7909498b94410996040857154dab029851af3a866da8c\",\n    \"test\": \"6d1ca1dba99e2c5dd54cae1226ff11c2551e6ce63527ebb072a1f70f72a5cd36\",\n}\n\n_PREFIX = {\n    \"train\": \"train\",\n    \"valid\": \"val\",\n    \"test\": \"test\",\n}\n\nNUM_LINES = {\n    \"train\": 29000,\n    \"valid\": 1014,\n    \"test\": 1000,\n}\n\nDATASET_NAME = \"Multi30k\"\n\n\ndef _filepath_fn(root, split, _=None):\n    return os.path.join(root, os.path.basename(URL[split]))\n\n\ndef _decompressed_filepath_fn(root, split, language_pair, i, _):\n    return os.path.join(root, f\"{_PREFIX[split]}.{language_pair[i]}\")\n\n\ndef _filter_fn(split, language_pair, i, x):\n    return f\"{_PREFIX[split]}.{language_pair[i]}\" in x[0]\n\n\n@_create_dataset_directory(dataset_name=DATASET_NAME)\n@_wrap_split_argument((\"train\", \"valid\", \"test\"))\ndef Multi30k(root: str, split: Union[Tuple[str], str], language_pair: Tuple[str] = (\"de\", \"en\")):\n    \"\"\"Multi30k dataset\n\n    .. warning::\n\n        using datapipes is still currently subject to a few caveats. if you wish\n        to use this dataset with shuffling, multi-processing, or distributed\n        learning, please see :ref:`this note <datapipes_warnings>` for further\n        instructions.\n\n    For additional details refer to https://www.statmt.org/wmt16/multimodal-task.html#task1\n\n    Number of lines per split:\n        - train: 29000\n        - valid: 1014\n        - test: 1000\n\n    Args:\n        root: Directory where the datasets are saved. Default: os.path.expanduser('~/.torchtext/cache')\n        split: split or splits to be returned. Can be a string or tuple of strings. Default: ('train', 'valid', 'test')\n        language_pair: tuple or list containing src and tgt language. Available options are ('de','en') and ('en', 'de')\n\n    :return: DataPipe that yields tuple of source and target sentences\n    :rtype: (str, str)\n    \"\"\"\n\n    assert len(language_pair) == 2, \"language_pair must contain only 2 elements: src and tgt language respectively\"\n    assert tuple(sorted(language_pair)) == (\n        \"de\",\n        \"en\",\n    ), \"language_pair must be either ('de','en') or ('en', 'de')\"\n\n    if not is_module_available(\"torchdata\"):\n        raise ModuleNotFoundError(\n            \"Package `torchdata` not found. Please install following instructions at https://github.com/pytorch/data\"\n        )\n\n    url_dp = IterableWrapper([URL[split]])\n\n    cache_compressed_dp = url_dp.on_disk_cache(\n        filepath_fn=partial(_filepath_fn, root, split),\n        hash_dict={_filepath_fn(root, split): MD5[split]},\n        hash_type=\"sha256\",\n    )\n    cache_compressed_dp = HttpReader(cache_compressed_dp).end_caching(mode=\"wb\", same_filepath_fn=True)\n\n    cache_compressed_dp_1, cache_compressed_dp_2 = cache_compressed_dp.fork(num_instances=2)\n\n    src_cache_decompressed_dp = cache_compressed_dp_1.on_disk_cache(\n        filepath_fn=partial(_decompressed_filepath_fn, root, split, language_pair, 0)\n    )\n    src_cache_decompressed_dp = (\n        FileOpener(src_cache_decompressed_dp, mode=\"b\")\n        .load_from_tar()\n        .filter(partial(_filter_fn, split, language_pair, 0))\n    )\n    src_cache_decompressed_dp = src_cache_decompressed_dp.end_caching(mode=\"wb\", same_filepath_fn=True)\n\n    tgt_cache_decompressed_dp = cache_compressed_dp_2.on_disk_cache(\n        filepath_fn=partial(_decompressed_filepath_fn, root, split, language_pair, 1)\n    )\n    tgt_cache_decompressed_dp = (\n        FileOpener(tgt_cache_decompressed_dp, mode=\"b\")\n        .load_from_tar()\n        .filter(partial(_filter_fn, split, language_pair, 1))\n    )\n    tgt_cache_decompressed_dp = tgt_cache_decompressed_dp.end_caching(mode=\"wb\", same_filepath_fn=True)\n\n    src_data_dp = FileOpener(src_cache_decompressed_dp, encoding=\"utf-8\").readlines(\n        return_path=False, strip_newline=True\n    )\n    tgt_data_dp = FileOpener(tgt_cache_decompressed_dp, encoding=\"utf-8\").readlines(\n        return_path=False, strip_newline=True\n    )\n\n    return src_data_dp.zip(tgt_data_dp).shuffle().set_shuffle(False).sharding_filter()\n", "path": "torchtext/datasets/multi30k.py"}], "after_files": [{"content": "import os\nfrom functools import partial\nfrom typing import Union, Tuple\n\nfrom torchtext._internal.module_utils import is_module_available\nfrom torchtext.data.datasets_utils import (\n    _wrap_split_argument,\n    _create_dataset_directory,\n)\n\nif is_module_available(\"torchdata\"):\n    from torchdata.datapipes.iter import FileOpener, IterableWrapper\n    from torchtext._download_hooks import HttpReader\n\nURL = {\n    \"train\": \"http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/training.tar.gz\",\n    \"valid\": \"http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/validation.tar.gz\",\n    \"test\": \"http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/mmt16_task1_test.tar.gz\",\n}\n\nMD5 = {\n    \"train\": \"20140d013d05dd9a72dfde46478663ba05737ce983f478f960c1123c6671be5e\",\n    \"valid\": \"a7aa20e9ebd5ba5adce7909498b94410996040857154dab029851af3a866da8c\",\n    \"test\": \"6d1ca1dba99e2c5dd54cae1226ff11c2551e6ce63527ebb072a1f70f72a5cd36\",\n}\n\n_PREFIX = {\n    \"train\": \"train\",\n    \"valid\": \"val\",\n    \"test\": \"test\",\n}\n\nNUM_LINES = {\n    \"train\": 29000,\n    \"valid\": 1014,\n    \"test\": 1000,\n}\n\nDATASET_NAME = \"Multi30k\"\n\n\ndef _filepath_fn(root, split, _=None):\n    return os.path.join(root, os.path.basename(URL[split]))\n\n\ndef _decompressed_filepath_fn(root, split, language_pair, i, _):\n    return os.path.join(root, f\"{_PREFIX[split]}.{language_pair[i]}\")\n\n\ndef _filter_fn(split, language_pair, i, x):\n    return f\"{_PREFIX[split]}.{language_pair[i]}\" in x[0]\n\n\n@_create_dataset_directory(dataset_name=DATASET_NAME)\n@_wrap_split_argument((\"train\", \"valid\", \"test\"))\ndef Multi30k(root: str, split: Union[Tuple[str], str], language_pair: Tuple[str] = (\"de\", \"en\")):\n    \"\"\"Multi30k dataset\n\n    .. warning::\n\n        using datapipes is still currently subject to a few caveats. if you wish\n        to use this dataset with shuffling, multi-processing, or distributed\n        learning, please see :ref:`this note <datapipes_warnings>` for further\n        instructions.\n\n    For additional details refer to https://www.statmt.org/wmt16/multimodal-task.html#task1\n\n    Number of lines per split:\n        - train: 29000\n        - valid: 1014\n        - test: 1000\n\n    Args:\n        root: Directory where the datasets are saved. Default: os.path.expanduser('~/.torchtext/cache')\n        split: split or splits to be returned. Can be a string or tuple of strings. Default: ('train', 'valid', 'test')\n        language_pair: tuple or list containing src and tgt language. Available options are ('de','en') and ('en', 'de')\n\n    :return: DataPipe that yields tuple of source and target sentences\n    :rtype: (str, str)\n    \"\"\"\n\n    assert len(language_pair) == 2, \"language_pair must contain only 2 elements: src and tgt language respectively\"\n    assert tuple(sorted(language_pair)) == (\n        \"de\",\n        \"en\",\n    ), \"language_pair must be either ('de','en') or ('en', 'de')\"\n\n    if not is_module_available(\"torchdata\"):\n        raise ModuleNotFoundError(\n            \"Package `torchdata` not found. Please install following instructions at https://github.com/pytorch/data\"\n        )\n\n    url_dp = IterableWrapper([URL[split]])\n\n    cache_compressed_dp = url_dp.on_disk_cache(\n        filepath_fn=partial(_filepath_fn, root, split),\n        hash_dict={_filepath_fn(root, split): MD5[split]},\n        hash_type=\"sha256\",\n    )\n    cache_compressed_dp = HttpReader(cache_compressed_dp).end_caching(mode=\"wb\", same_filepath_fn=True)\n\n    cache_compressed_dp_1, cache_compressed_dp_2 = cache_compressed_dp.fork(num_instances=2)\n\n    src_cache_decompressed_dp = cache_compressed_dp_1.on_disk_cache(\n        filepath_fn=partial(_decompressed_filepath_fn, root, split, language_pair, 0)\n    )\n    src_cache_decompressed_dp = (\n        FileOpener(src_cache_decompressed_dp, mode=\"b\")\n        .load_from_tar()\n        .filter(partial(_filter_fn, split, language_pair, 0))\n    )\n    src_cache_decompressed_dp = src_cache_decompressed_dp.end_caching(mode=\"wb\", same_filepath_fn=True)\n\n    tgt_cache_decompressed_dp = cache_compressed_dp_2.on_disk_cache(\n        filepath_fn=partial(_decompressed_filepath_fn, root, split, language_pair, 1)\n    )\n    tgt_cache_decompressed_dp = (\n        FileOpener(tgt_cache_decompressed_dp, mode=\"b\")\n        .load_from_tar()\n        .filter(partial(_filter_fn, split, language_pair, 1))\n    )\n    tgt_cache_decompressed_dp = tgt_cache_decompressed_dp.end_caching(mode=\"wb\", same_filepath_fn=True)\n\n    src_data_dp = FileOpener(src_cache_decompressed_dp, encoding=\"utf-8\").readlines(\n        return_path=False, strip_newline=True\n    )\n    tgt_data_dp = FileOpener(tgt_cache_decompressed_dp, encoding=\"utf-8\").readlines(\n        return_path=False, strip_newline=True\n    )\n\n    return src_data_dp.zip(tgt_data_dp).shuffle().set_shuffle(False).sharding_filter()\n", "path": "torchtext/datasets/multi30k.py"}]}