We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
module 'torchmetrics.classification' has no attribute 'AUC'
## 🐛 Bug

Importing all the classification metrics causes the `AttributeError`:
`from torchmetrics.classification import *`

`AttributeError: module 'torchmetrics.classification' has no attribute 'AUC'`

Environment
torchmetrics 0.11.0
pytorch 1.13.0

In order to fix it someone should remove AUC from the list __all__ (src/torchmetrics/classification/__init__.py)

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `src/torchmetrics/classification/__init__.py`
Content:
```
1 # Copyright The PyTorch Lightning team.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
6 #
7 #     http://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 from torchmetrics.classification.confusion_matrix import (  # isort:skip
15     BinaryConfusionMatrix,
16     ConfusionMatrix,
17     MulticlassConfusionMatrix,
18     MultilabelConfusionMatrix,
19 )
20 from torchmetrics.classification.precision_recall_curve import (  # isort:skip
21     PrecisionRecallCurve,
22     BinaryPrecisionRecallCurve,
23     MulticlassPrecisionRecallCurve,
24     MultilabelPrecisionRecallCurve,
25 )
26 from torchmetrics.classification.stat_scores import (  # isort:skip
27     BinaryStatScores,
28     MulticlassStatScores,
29     MultilabelStatScores,
30     StatScores,
31 )
32 from torchmetrics.classification.accuracy import Accuracy, BinaryAccuracy, MulticlassAccuracy, MultilabelAccuracy
33 from torchmetrics.classification.auroc import AUROC, BinaryAUROC, MulticlassAUROC, MultilabelAUROC
34 from torchmetrics.classification.average_precision import (
35     AveragePrecision,
36     BinaryAveragePrecision,
37     MulticlassAveragePrecision,
38     MultilabelAveragePrecision,
39 )
40 from torchmetrics.classification.calibration_error import (
41     BinaryCalibrationError,
42     CalibrationError,
43     MulticlassCalibrationError,
44 )
45 from torchmetrics.classification.cohen_kappa import BinaryCohenKappa, CohenKappa, MulticlassCohenKappa
46 from torchmetrics.classification.dice import Dice
47 from torchmetrics.classification.exact_match import ExactMatch, MulticlassExactMatch, MultilabelExactMatch
48 from torchmetrics.classification.f_beta import (
49     BinaryF1Score,
50     BinaryFBetaScore,
51     F1Score,
52     FBetaScore,
53     MulticlassF1Score,
54     MulticlassFBetaScore,
55     MultilabelF1Score,
56     MultilabelFBetaScore,
57 )
58 from torchmetrics.classification.hamming import (
59     BinaryHammingDistance,
60     HammingDistance,
61     MulticlassHammingDistance,
62     MultilabelHammingDistance,
63 )
64 from torchmetrics.classification.hinge import BinaryHingeLoss, HingeLoss, MulticlassHingeLoss
65 from torchmetrics.classification.jaccard import (
66     BinaryJaccardIndex,
67     JaccardIndex,
68     MulticlassJaccardIndex,
69     MultilabelJaccardIndex,
70 )
71 from torchmetrics.classification.matthews_corrcoef import (
72     BinaryMatthewsCorrCoef,
73     MatthewsCorrCoef,
74     MulticlassMatthewsCorrCoef,
75     MultilabelMatthewsCorrCoef,
76 )
77 from torchmetrics.classification.precision_recall import (
78     BinaryPrecision,
79     BinaryRecall,
80     MulticlassPrecision,
81     MulticlassRecall,
82     MultilabelPrecision,
83     MultilabelRecall,
84     Precision,
85     Recall,
86 )
87 from torchmetrics.classification.ranking import (
88     MultilabelCoverageError,
89     MultilabelRankingAveragePrecision,
90     MultilabelRankingLoss,
91 )
92 from torchmetrics.classification.recall_at_fixed_precision import (
93     BinaryRecallAtFixedPrecision,
94     MulticlassRecallAtFixedPrecision,
95     MultilabelRecallAtFixedPrecision,
96 )
97 from torchmetrics.classification.roc import ROC, BinaryROC, MulticlassROC, MultilabelROC
98 from torchmetrics.classification.specificity import (
99     BinarySpecificity,
100     MulticlassSpecificity,
101     MultilabelSpecificity,
102     Specificity,
103 )
104 
105 __all__ = [
106     "BinaryConfusionMatrix",
107     "ConfusionMatrix",
108     "MulticlassConfusionMatrix",
109     "MultilabelConfusionMatrix",
110     "PrecisionRecallCurve",
111     "BinaryPrecisionRecallCurve",
112     "MulticlassPrecisionRecallCurve",
113     "MultilabelPrecisionRecallCurve",
114     "BinaryStatScores",
115     "MulticlassStatScores",
116     "MultilabelStatScores",
117     "StatScores",
118     "Accuracy",
119     "BinaryAccuracy",
120     "MulticlassAccuracy",
121     "MultilabelAccuracy",
122     "AUC",
123     "AUROC",
124     "BinaryAUROC",
125     "MulticlassAUROC",
126     "MultilabelAUROC",
127     "AveragePrecision",
128     "BinaryAveragePrecision",
129     "MulticlassAveragePrecision",
130     "MultilabelAveragePrecision",
131     "BinnedAveragePrecision",
132     "BinnedPrecisionRecallCurve",
133     "BinnedRecallAtFixedPrecision",
134     "BinaryCalibrationError",
135     "CalibrationError",
136     "MulticlassCalibrationError",
137     "BinaryCohenKappa",
138     "CohenKappa",
139     "MulticlassCohenKappa",
140     "Dice",
141     "ExactMatch",
142     "MulticlassExactMatch",
143     "MultilabelExactMatch",
144     "BinaryF1Score",
145     "BinaryFBetaScore",
146     "F1Score",
147     "FBetaScore",
148     "MulticlassF1Score",
149     "MulticlassFBetaScore",
150     "MultilabelF1Score",
151     "MultilabelFBetaScore",
152     "BinaryHammingDistance",
153     "HammingDistance",
154     "MulticlassHammingDistance",
155     "MultilabelHammingDistance",
156     "BinaryHingeLoss",
157     "HingeLoss",
158     "MulticlassHingeLoss",
159     "BinaryJaccardIndex",
160     "JaccardIndex",
161     "MulticlassJaccardIndex",
162     "MultilabelJaccardIndex",
163     "BinaryMatthewsCorrCoef",
164     "MatthewsCorrCoef",
165     "MulticlassMatthewsCorrCoef",
166     "MultilabelMatthewsCorrCoef",
167     "BinaryPrecision",
168     "BinaryRecall",
169     "MulticlassPrecision",
170     "MulticlassRecall",
171     "MultilabelPrecision",
172     "MultilabelRecall",
173     "Precision",
174     "Recall",
175     "CoverageError",
176     "LabelRankingAveragePrecision",
177     "LabelRankingLoss",
178     "MultilabelCoverageError",
179     "MultilabelRankingAveragePrecision",
180     "MultilabelRankingLoss",
181     "BinaryRecallAtFixedPrecision",
182     "MulticlassRecallAtFixedPrecision",
183     "MultilabelRecallAtFixedPrecision",
184     "ROC",
185     "BinaryROC",
186     "MulticlassROC",
187     "MultilabelROC",
188     "BinarySpecificity",
189     "MulticlassSpecificity",
190     "MultilabelSpecificity",
191     "Specificity",
192 ]
193
```
--- 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/src/torchmetrics/classification/__init__.py b/src/torchmetrics/classification/__init__.py
--- a/src/torchmetrics/classification/__init__.py
+++ b/src/torchmetrics/classification/__init__.py
@@ -119,7 +119,6 @@
     "BinaryAccuracy",
     "MulticlassAccuracy",
     "MultilabelAccuracy",
-    "AUC",
     "AUROC",
     "BinaryAUROC",
     "MulticlassAUROC",

{"golden_diff": "diff --git a/src/torchmetrics/classification/__init__.py b/src/torchmetrics/classification/__init__.py\n--- a/src/torchmetrics/classification/__init__.py\n+++ b/src/torchmetrics/classification/__init__.py\n@@ -119,7 +119,6 @@\n     \"BinaryAccuracy\",\n     \"MulticlassAccuracy\",\n     \"MultilabelAccuracy\",\n-    \"AUC\",\n     \"AUROC\",\n     \"BinaryAUROC\",\n     \"MulticlassAUROC\",\n", "issue": "module 'torchmetrics.classification' has no attribute 'AUC'\n## \ud83d\udc1b Bug\r\n\r\nImporting all the classification metrics causes the `AttributeError`:\r\n`from torchmetrics.classification import *`\r\n\r\n`AttributeError: module 'torchmetrics.classification' has no attribute 'AUC'`\r\n\r\nEnvironment\r\ntorchmetrics 0.11.0\r\npytorch 1.13.0\r\n\r\nIn order to fix it someone should remove AUC from the list __all__ (src/torchmetrics/classification/__init__.py)\n", "before_files": [{"content": "# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom torchmetrics.classification.confusion_matrix import (  # isort:skip\n    BinaryConfusionMatrix,\n    ConfusionMatrix,\n    MulticlassConfusionMatrix,\n    MultilabelConfusionMatrix,\n)\nfrom torchmetrics.classification.precision_recall_curve import (  # isort:skip\n    PrecisionRecallCurve,\n    BinaryPrecisionRecallCurve,\n    MulticlassPrecisionRecallCurve,\n    MultilabelPrecisionRecallCurve,\n)\nfrom torchmetrics.classification.stat_scores import (  # isort:skip\n    BinaryStatScores,\n    MulticlassStatScores,\n    MultilabelStatScores,\n    StatScores,\n)\nfrom torchmetrics.classification.accuracy import Accuracy, BinaryAccuracy, MulticlassAccuracy, MultilabelAccuracy\nfrom torchmetrics.classification.auroc import AUROC, BinaryAUROC, MulticlassAUROC, MultilabelAUROC\nfrom torchmetrics.classification.average_precision import (\n    AveragePrecision,\n    BinaryAveragePrecision,\n    MulticlassAveragePrecision,\n    MultilabelAveragePrecision,\n)\nfrom torchmetrics.classification.calibration_error import (\n    BinaryCalibrationError,\n    CalibrationError,\n    MulticlassCalibrationError,\n)\nfrom torchmetrics.classification.cohen_kappa import BinaryCohenKappa, CohenKappa, MulticlassCohenKappa\nfrom torchmetrics.classification.dice import Dice\nfrom torchmetrics.classification.exact_match import ExactMatch, MulticlassExactMatch, MultilabelExactMatch\nfrom torchmetrics.classification.f_beta import (\n    BinaryF1Score,\n    BinaryFBetaScore,\n    F1Score,\n    FBetaScore,\n    MulticlassF1Score,\n    MulticlassFBetaScore,\n    MultilabelF1Score,\n    MultilabelFBetaScore,\n)\nfrom torchmetrics.classification.hamming import (\n    BinaryHammingDistance,\n    HammingDistance,\n    MulticlassHammingDistance,\n    MultilabelHammingDistance,\n)\nfrom torchmetrics.classification.hinge import BinaryHingeLoss, HingeLoss, MulticlassHingeLoss\nfrom torchmetrics.classification.jaccard import (\n    BinaryJaccardIndex,\n    JaccardIndex,\n    MulticlassJaccardIndex,\n    MultilabelJaccardIndex,\n)\nfrom torchmetrics.classification.matthews_corrcoef import (\n    BinaryMatthewsCorrCoef,\n    MatthewsCorrCoef,\n    MulticlassMatthewsCorrCoef,\n    MultilabelMatthewsCorrCoef,\n)\nfrom torchmetrics.classification.precision_recall import (\n    BinaryPrecision,\n    BinaryRecall,\n    MulticlassPrecision,\n    MulticlassRecall,\n    MultilabelPrecision,\n    MultilabelRecall,\n    Precision,\n    Recall,\n)\nfrom torchmetrics.classification.ranking import (\n    MultilabelCoverageError,\n    MultilabelRankingAveragePrecision,\n    MultilabelRankingLoss,\n)\nfrom torchmetrics.classification.recall_at_fixed_precision import (\n    BinaryRecallAtFixedPrecision,\n    MulticlassRecallAtFixedPrecision,\n    MultilabelRecallAtFixedPrecision,\n)\nfrom torchmetrics.classification.roc import ROC, BinaryROC, MulticlassROC, MultilabelROC\nfrom torchmetrics.classification.specificity import (\n    BinarySpecificity,\n    MulticlassSpecificity,\n    MultilabelSpecificity,\n    Specificity,\n)\n\n__all__ = [\n    \"BinaryConfusionMatrix\",\n    \"ConfusionMatrix\",\n    \"MulticlassConfusionMatrix\",\n    \"MultilabelConfusionMatrix\",\n    \"PrecisionRecallCurve\",\n    \"BinaryPrecisionRecallCurve\",\n    \"MulticlassPrecisionRecallCurve\",\n    \"MultilabelPrecisionRecallCurve\",\n    \"BinaryStatScores\",\n    \"MulticlassStatScores\",\n    \"MultilabelStatScores\",\n    \"StatScores\",\n    \"Accuracy\",\n    \"BinaryAccuracy\",\n    \"MulticlassAccuracy\",\n    \"MultilabelAccuracy\",\n    \"AUC\",\n    \"AUROC\",\n    \"BinaryAUROC\",\n    \"MulticlassAUROC\",\n    \"MultilabelAUROC\",\n    \"AveragePrecision\",\n    \"BinaryAveragePrecision\",\n    \"MulticlassAveragePrecision\",\n    \"MultilabelAveragePrecision\",\n    \"BinnedAveragePrecision\",\n    \"BinnedPrecisionRecallCurve\",\n    \"BinnedRecallAtFixedPrecision\",\n    \"BinaryCalibrationError\",\n    \"CalibrationError\",\n    \"MulticlassCalibrationError\",\n    \"BinaryCohenKappa\",\n    \"CohenKappa\",\n    \"MulticlassCohenKappa\",\n    \"Dice\",\n    \"ExactMatch\",\n    \"MulticlassExactMatch\",\n    \"MultilabelExactMatch\",\n    \"BinaryF1Score\",\n    \"BinaryFBetaScore\",\n    \"F1Score\",\n    \"FBetaScore\",\n    \"MulticlassF1Score\",\n    \"MulticlassFBetaScore\",\n    \"MultilabelF1Score\",\n    \"MultilabelFBetaScore\",\n    \"BinaryHammingDistance\",\n    \"HammingDistance\",\n    \"MulticlassHammingDistance\",\n    \"MultilabelHammingDistance\",\n    \"BinaryHingeLoss\",\n    \"HingeLoss\",\n    \"MulticlassHingeLoss\",\n    \"BinaryJaccardIndex\",\n    \"JaccardIndex\",\n    \"MulticlassJaccardIndex\",\n    \"MultilabelJaccardIndex\",\n    \"BinaryMatthewsCorrCoef\",\n    \"MatthewsCorrCoef\",\n    \"MulticlassMatthewsCorrCoef\",\n    \"MultilabelMatthewsCorrCoef\",\n    \"BinaryPrecision\",\n    \"BinaryRecall\",\n    \"MulticlassPrecision\",\n    \"MulticlassRecall\",\n    \"MultilabelPrecision\",\n    \"MultilabelRecall\",\n    \"Precision\",\n    \"Recall\",\n    \"CoverageError\",\n    \"LabelRankingAveragePrecision\",\n    \"LabelRankingLoss\",\n    \"MultilabelCoverageError\",\n    \"MultilabelRankingAveragePrecision\",\n    \"MultilabelRankingLoss\",\n    \"BinaryRecallAtFixedPrecision\",\n    \"MulticlassRecallAtFixedPrecision\",\n    \"MultilabelRecallAtFixedPrecision\",\n    \"ROC\",\n    \"BinaryROC\",\n    \"MulticlassROC\",\n    \"MultilabelROC\",\n    \"BinarySpecificity\",\n    \"MulticlassSpecificity\",\n    \"MultilabelSpecificity\",\n    \"Specificity\",\n]\n", "path": "src/torchmetrics/classification/__init__.py"}], "after_files": [{"content": "# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom torchmetrics.classification.confusion_matrix import (  # isort:skip\n    BinaryConfusionMatrix,\n    ConfusionMatrix,\n    MulticlassConfusionMatrix,\n    MultilabelConfusionMatrix,\n)\nfrom torchmetrics.classification.precision_recall_curve import (  # isort:skip\n    PrecisionRecallCurve,\n    BinaryPrecisionRecallCurve,\n    MulticlassPrecisionRecallCurve,\n    MultilabelPrecisionRecallCurve,\n)\nfrom torchmetrics.classification.stat_scores import (  # isort:skip\n    BinaryStatScores,\n    MulticlassStatScores,\n    MultilabelStatScores,\n    StatScores,\n)\nfrom torchmetrics.classification.accuracy import Accuracy, BinaryAccuracy, MulticlassAccuracy, MultilabelAccuracy\nfrom torchmetrics.classification.auroc import AUROC, BinaryAUROC, MulticlassAUROC, MultilabelAUROC\nfrom torchmetrics.classification.average_precision import (\n    AveragePrecision,\n    BinaryAveragePrecision,\n    MulticlassAveragePrecision,\n    MultilabelAveragePrecision,\n)\nfrom torchmetrics.classification.calibration_error import (\n    BinaryCalibrationError,\n    CalibrationError,\n    MulticlassCalibrationError,\n)\nfrom torchmetrics.classification.cohen_kappa import BinaryCohenKappa, CohenKappa, MulticlassCohenKappa\nfrom torchmetrics.classification.dice import Dice\nfrom torchmetrics.classification.exact_match import ExactMatch, MulticlassExactMatch, MultilabelExactMatch\nfrom torchmetrics.classification.f_beta import (\n    BinaryF1Score,\n    BinaryFBetaScore,\n    F1Score,\n    FBetaScore,\n    MulticlassF1Score,\n    MulticlassFBetaScore,\n    MultilabelF1Score,\n    MultilabelFBetaScore,\n)\nfrom torchmetrics.classification.hamming import (\n    BinaryHammingDistance,\n    HammingDistance,\n    MulticlassHammingDistance,\n    MultilabelHammingDistance,\n)\nfrom torchmetrics.classification.hinge import BinaryHingeLoss, HingeLoss, MulticlassHingeLoss\nfrom torchmetrics.classification.jaccard import (\n    BinaryJaccardIndex,\n    JaccardIndex,\n    MulticlassJaccardIndex,\n    MultilabelJaccardIndex,\n)\nfrom torchmetrics.classification.matthews_corrcoef import (\n    BinaryMatthewsCorrCoef,\n    MatthewsCorrCoef,\n    MulticlassMatthewsCorrCoef,\n    MultilabelMatthewsCorrCoef,\n)\nfrom torchmetrics.classification.precision_recall import (\n    BinaryPrecision,\n    BinaryRecall,\n    MulticlassPrecision,\n    MulticlassRecall,\n    MultilabelPrecision,\n    MultilabelRecall,\n    Precision,\n    Recall,\n)\nfrom torchmetrics.classification.ranking import (\n    MultilabelCoverageError,\n    MultilabelRankingAveragePrecision,\n    MultilabelRankingLoss,\n)\nfrom torchmetrics.classification.recall_at_fixed_precision import (\n    BinaryRecallAtFixedPrecision,\n    MulticlassRecallAtFixedPrecision,\n    MultilabelRecallAtFixedPrecision,\n)\nfrom torchmetrics.classification.roc import ROC, BinaryROC, MulticlassROC, MultilabelROC\nfrom torchmetrics.classification.specificity import (\n    BinarySpecificity,\n    MulticlassSpecificity,\n    MultilabelSpecificity,\n    Specificity,\n)\n\n__all__ = [\n    \"BinaryConfusionMatrix\",\n    \"ConfusionMatrix\",\n    \"MulticlassConfusionMatrix\",\n    \"MultilabelConfusionMatrix\",\n    \"PrecisionRecallCurve\",\n    \"BinaryPrecisionRecallCurve\",\n    \"MulticlassPrecisionRecallCurve\",\n    \"MultilabelPrecisionRecallCurve\",\n    \"BinaryStatScores\",\n    \"MulticlassStatScores\",\n    \"MultilabelStatScores\",\n    \"StatScores\",\n    \"Accuracy\",\n    \"BinaryAccuracy\",\n    \"MulticlassAccuracy\",\n    \"MultilabelAccuracy\",\n    \"AUROC\",\n    \"BinaryAUROC\",\n    \"MulticlassAUROC\",\n    \"MultilabelAUROC\",\n    \"AveragePrecision\",\n    \"BinaryAveragePrecision\",\n    \"MulticlassAveragePrecision\",\n    \"MultilabelAveragePrecision\",\n    \"BinnedAveragePrecision\",\n    \"BinnedPrecisionRecallCurve\",\n    \"BinnedRecallAtFixedPrecision\",\n    \"BinaryCalibrationError\",\n    \"CalibrationError\",\n    \"MulticlassCalibrationError\",\n    \"BinaryCohenKappa\",\n    \"CohenKappa\",\n    \"MulticlassCohenKappa\",\n    \"Dice\",\n    \"ExactMatch\",\n    \"MulticlassExactMatch\",\n    \"MultilabelExactMatch\",\n    \"BinaryF1Score\",\n    \"BinaryFBetaScore\",\n    \"F1Score\",\n    \"FBetaScore\",\n    \"MulticlassF1Score\",\n    \"MulticlassFBetaScore\",\n    \"MultilabelF1Score\",\n    \"MultilabelFBetaScore\",\n    \"BinaryHammingDistance\",\n    \"HammingDistance\",\n    \"MulticlassHammingDistance\",\n    \"MultilabelHammingDistance\",\n    \"BinaryHingeLoss\",\n    \"HingeLoss\",\n    \"MulticlassHingeLoss\",\n    \"BinaryJaccardIndex\",\n    \"JaccardIndex\",\n    \"MulticlassJaccardIndex\",\n    \"MultilabelJaccardIndex\",\n    \"BinaryMatthewsCorrCoef\",\n    \"MatthewsCorrCoef\",\n    \"MulticlassMatthewsCorrCoef\",\n    \"MultilabelMatthewsCorrCoef\",\n    \"BinaryPrecision\",\n    \"BinaryRecall\",\n    \"MulticlassPrecision\",\n    \"MulticlassRecall\",\n    \"MultilabelPrecision\",\n    \"MultilabelRecall\",\n    \"Precision\",\n    \"Recall\",\n    \"CoverageError\",\n    \"LabelRankingAveragePrecision\",\n    \"LabelRankingLoss\",\n    \"MultilabelCoverageError\",\n    \"MultilabelRankingAveragePrecision\",\n    \"MultilabelRankingLoss\",\n    \"BinaryRecallAtFixedPrecision\",\n    \"MulticlassRecallAtFixedPrecision\",\n    \"MultilabelRecallAtFixedPrecision\",\n    \"ROC\",\n    \"BinaryROC\",\n    \"MulticlassROC\",\n    \"MultilabelROC\",\n    \"BinarySpecificity\",\n    \"MulticlassSpecificity\",\n    \"MultilabelSpecificity\",\n    \"Specificity\",\n]\n", "path": "src/torchmetrics/classification/__init__.py"}]}