anmolsjoshi: @vfdev-5 I added this functionality to _BaseRegressionEpoch as well. 
diff --git a/ignite/contrib/metrics/average_precision.py b/ignite/contrib/metrics/average_precision.py
index 5e81bcef..df7a4d96 100644
--- a/ignite/contrib/metrics/average_precision.py
+++ b/ignite/contrib/metrics/average_precision.py
@@ -22,6 +22,10 @@ class AveragePrecision(EpochMetric):
             :class:`~ignite.engine.Engine`'s `process_function`'s output into the
             form expected by the metric. This can be useful if, for example, you have a multi-output model and
             you want to compute the metric with respect to one of the outputs.
+        check_compute_fn (bool): Optional default False. If True, `sklearn.metrics.average_precision_score
+            <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html
+            #sklearn.metrics.average_precision_score>`_ is run on the first batch of data to ensure there are
+            no issues. User will be warned in case there are any issues computing the function.
 
     AveragePrecision expects y to be comprised of 0's and 1's. y_pred must either be probability estimates or
     confidence values. To apply an activation to y_pred, use output_transform as shown below:
@@ -37,5 +41,7 @@ class AveragePrecision(EpochMetric):
 
     """
 
-    def __init__(self, output_transform=lambda x: x):
-        super(AveragePrecision, self).__init__(average_precision_compute_fn, output_transform=output_transform)
+    def __init__(self, output_transform=lambda x: x, check_compute_fn: bool = False):
+        super(AveragePrecision, self).__init__(
+            average_precision_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn
+        )
diff --git a/ignite/contrib/metrics/precision_recall_curve.py b/ignite/contrib/metrics/precision_recall_curve.py
index 50a75317..9e1ecfdb 100644
--- a/ignite/contrib/metrics/precision_recall_curve.py
+++ b/ignite/contrib/metrics/precision_recall_curve.py
@@ -23,6 +23,10 @@ class PrecisionRecallCurve(EpochMetric):
             :class:`~ignite.engine.Engine`'s `process_function`'s output into the
             form expected by the metric. This can be useful if, for example, you have a multi-output model and
             you want to compute the metric with respect to one of the outputs.
+        check_compute_fn (bool): Optional default False. If True, `sklearn.metrics.precision_recall_curve
+            <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html
+            #sklearn.metrics.precision_recall_curve>`_ is run on the first batch of data to ensure there are
+            no issues. User will be warned in case there are any issues computing the function.
 
     PrecisionRecallCurve expects y to be comprised of 0's and 1's. y_pred must either be probability estimates
     or confidence values. To apply an activation to y_pred, use output_transform as shown below:
@@ -38,5 +42,7 @@ class PrecisionRecallCurve(EpochMetric):
 
     """
 
-    def __init__(self, output_transform=lambda x: x):
-        super(PrecisionRecallCurve, self).__init__(precision_recall_curve_compute_fn, output_transform=output_transform)
+    def __init__(self, output_transform=lambda x: x, check_compute_fn: bool = False):
+        super(PrecisionRecallCurve, self).__init__(
+            precision_recall_curve_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn
+        )
diff --git a/ignite/contrib/metrics/regression/_base.py b/ignite/contrib/metrics/regression/_base.py
index ef70856e..ba08441a 100644
--- a/ignite/contrib/metrics/regression/_base.py
+++ b/ignite/contrib/metrics/regression/_base.py
@@ -56,8 +56,10 @@ class _BaseRegressionEpoch(EpochMetric):
     # `update` method check the shapes and call internal overloaded method `_update`.
     # Class internally stores complete history of predictions and targets of type float32.
 
-    def __init__(self, compute_fn, output_transform=lambda x: x):
-        super(_BaseRegressionEpoch, self).__init__(compute_fn=compute_fn, output_transform=output_transform)
+    def __init__(self, compute_fn, output_transform=lambda x: x, check_compute_fn: bool = True):
+        super(_BaseRegressionEpoch, self).__init__(
+            compute_fn=compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn
+        )
 
     def _check_type(self, output):
         _check_output_types(output)
diff --git a/ignite/contrib/metrics/roc_auc.py b/ignite/contrib/metrics/roc_auc.py
index e732bfda..272106e6 100644
--- a/ignite/contrib/metrics/roc_auc.py
+++ b/ignite/contrib/metrics/roc_auc.py
@@ -31,9 +31,13 @@ class ROC_AUC(EpochMetric):
 
     Args:
         output_transform (callable, optional): a callable that is used to transform the
-            :class:`~ignite.engine.Engine`'s `process_function`'s output into the
+            :class:`~ignite.engine.Engine`'s ``process_function``'s output into the
             form expected by the metric. This can be useful if, for example, you have a multi-output model and
             you want to compute the metric with respect to one of the outputs.
+        check_compute_fn (bool): Optional default False. If True, `sklearn.metrics.roc_curve
+            <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#
+            sklearn.metrics.roc_auc_score>`_ is run on the first batch of data to ensure there are
+            no issues. User will be warned in case there are any issues computing the function.
 
     ROC_AUC expects y to be comprised of 0's and 1's. y_pred must either be probability estimates or confidence
     values. To apply an activation to y_pred, use output_transform as shown below:
@@ -49,8 +53,10 @@ class ROC_AUC(EpochMetric):
 
     """
 
-    def __init__(self, output_transform=lambda x: x):
-        super(ROC_AUC, self).__init__(roc_auc_compute_fn, output_transform=output_transform)
+    def __init__(self, output_transform=lambda x: x, check_compute_fn: bool = False):
+        super(ROC_AUC, self).__init__(
+            roc_auc_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn
+        )
 
 
 class RocCurve(EpochMetric):
@@ -61,9 +67,13 @@ class RocCurve(EpochMetric):
 
     Args:
         output_transform (callable, optional): a callable that is used to transform the
-            :class:`~ignite.engine.Engine`'s `process_function`'s output into the
+            :class:`~ignite.engine.Engine`'s ``process_function``'s output into the
             form expected by the metric. This can be useful if, for example, you have a multi-output model and
             you want to compute the metric with respect to one of the outputs.
+        check_compute_fn (bool): Optional default False. If True, `sklearn.metrics.roc_curve
+            <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#
+            sklearn.metrics.roc_curve>`_ is run on the first batch of data to ensure there are
+            no issues. User will be warned in case there are any issues computing the function.
 
     RocCurve expects y to be comprised of 0's and 1's. y_pred must either be probability estimates or confidence
     values. To apply an activation to y_pred, use output_transform as shown below:
@@ -79,5 +89,7 @@ class RocCurve(EpochMetric):
 
     """
 
-    def __init__(self, output_transform=lambda x: x):
-        super(RocCurve, self).__init__(roc_auc_curve_compute_fn, output_transform=output_transform)
+    def __init__(self, output_transform=lambda x: x, check_compute_fn: bool = False):
+        super(RocCurve, self).__init__(
+            roc_auc_curve_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn
+        )
diff --git a/ignite/metrics/epoch_metric.py b/ignite/metrics/epoch_metric.py
index b4655608..65b69fea 100644
--- a/ignite/metrics/epoch_metric.py
+++ b/ignite/metrics/epoch_metric.py
@@ -34,16 +34,21 @@ class EpochMetric(Metric):
             :class:`~ignite.engine.Engine`'s `process_function`'s output into the
             form expected by the metric. This can be useful if, for example, you have a multi-output model and
             you want to compute the metric with respect to one of the outputs.
+        check_compute_fn (bool): if True, compute_fn is run on the first batch of data to ensure there are no
+            issues. If issues exist, user is warned that there might be an issue with the ``compute_fn``.
 
+    Warnings:
+        EpochMetricWarning: User is warned that there are issues with compute_fn on a batch of data processed.
     """
 
-    def __init__(self, compute_fn: Callable, output_transform: Callable = lambda x: x):
+    def __init__(self, compute_fn: Callable, output_transform: Callable = lambda x: x, check_compute_fn: bool = True):
 
         if not callable(compute_fn):
             raise TypeError("Argument compute_fn should be callable.")
 
         super(EpochMetric, self).__init__(output_transform=output_transform, device="cpu")
         self.compute_fn = compute_fn
+        self._check_compute_fn = check_compute_fn
 
     def reset(self) -> None:
         self._predictions = []
@@ -95,7 +100,7 @@ class EpochMetric(Metric):
         self._targets.append(y)
 
         # Check once the signature and execution of compute_fn
-        if len(self._predictions) == 1:
+        if len(self._predictions) == 1 and self._check_compute_fn:
             try:
                 self.compute_fn(self._predictions[0], self._targets[0])
             except Exception as e:

EpochMetric signature check caused error
## 🐛 Bug description
I tried to re-implement the `ROC_AUC` like the one in `contrib.metrics.roc_auc`, and passed some extra kwarg options to support for multiclass and multilabel classification. However, when I run my code, an Error "Number of classes in y_true not equal to the number of  columns in 'y_score' " was raised by `scikit-learn`. I checked the` y_true` of the whole epoch, and the number of unique values was equal to the number of columns of `y_score`. Next, I checked the source code of EpochMetric, and found that in `update` function, it tried to execute the `compute_fn` at the first iteration.  I supposed this was the cause: the` y_true` of a single batch might not contain all the classes of the dataset, therefore `scikit-learn` would throws the error mentioned above. The same problem occured for `AveragePrecision`.

## Environment

 - PyTorch Version (e.g., 1.5): 1.5
 - Ignite Version (e.g., 0.3.0): 0.3.0
 - OS (e.g., Linux): Ubuntu 18.04
 - How you installed Ignite (`conda`, `pip`, source): pip
 - Python version: 3.7.6
 - Any other relevant information: None

diff --git a/tests/ignite/contrib/metrics/regression/test__base.py b/tests/ignite/contrib/metrics/regression/test__base.py
index 89722591..ef7e02c8 100644
--- a/tests/ignite/contrib/metrics/regression/test__base.py
+++ b/tests/ignite/contrib/metrics/regression/test__base.py
@@ -3,6 +3,7 @@ import pytest
 import torch
 
 from ignite.contrib.metrics.regression._base import _BaseRegression, _BaseRegressionEpoch
+from ignite.metrics.epoch_metric import EpochMetricWarning
 
 
 def test_base_regression_shapes():
@@ -84,3 +85,18 @@ def test_base_regression_compute_fn():
     # Wrong compute function
     with pytest.raises(TypeError):
         _BaseRegressionEpoch(12345)
+
+
+def test_check_compute_fn():
+    def compute_fn(y_preds, y_targets):
+        raise Exception
+
+    em = _BaseRegressionEpoch(compute_fn, check_compute_fn=True)
+
+    em.reset()
+    output1 = (torch.rand(4, 1).float(), torch.randint(0, 2, size=(4, 1), dtype=torch.float32))
+    with pytest.warns(EpochMetricWarning, match=r"Probably, there can be a problem with `compute_fn`"):
+        em.update(output1)
+
+    em = _BaseRegressionEpoch(compute_fn, check_compute_fn=False)
+    em.update(output1)
diff --git a/tests/ignite/contrib/metrics/test_precision_recall_curve.py b/tests/ignite/contrib/metrics/test_precision_recall_curve.py
index 7a5d643a..eb696606 100644
--- a/tests/ignite/contrib/metrics/test_precision_recall_curve.py
+++ b/tests/ignite/contrib/metrics/test_precision_recall_curve.py
@@ -1,9 +1,11 @@
 import numpy as np
+import pytest
 import torch
 from sklearn.metrics import precision_recall_curve
 
 from ignite.contrib.metrics.precision_recall_curve import PrecisionRecallCurve
 from ignite.engine import Engine
+from ignite.metrics.epoch_metric import EpochMetricWarning
 
 
 def test_precision_recall_curve():
@@ -89,3 +91,19 @@ def test_integration_precision_recall_curve_with_activated_output_transform():
     assert np.array_equal(recall, sk_recall)
     # assert thresholds almost equal, due to numpy->torch->numpy conversion
     np.testing.assert_array_almost_equal(thresholds, sk_thresholds)
+
+
+def test_check_compute_fn():
+    y_pred = torch.zeros((8, 13))
+    y_pred[:, 1] = 1
+    y_true = torch.zeros_like(y_pred)
+    output = (y_pred, y_true)
+
+    em = PrecisionRecallCurve(check_compute_fn=True)
+
+    em.reset()
+    with pytest.warns(EpochMetricWarning, match=r"Probably, there can be a problem with `compute_fn`"):
+        em.update(output)
+
+    em = PrecisionRecallCurve(check_compute_fn=False)
+    em.update(output)
diff --git a/tests/ignite/contrib/metrics/test_roc_auc.py b/tests/ignite/contrib/metrics/test_roc_auc.py
index 92ce92d7..c3674652 100644
--- a/tests/ignite/contrib/metrics/test_roc_auc.py
+++ b/tests/ignite/contrib/metrics/test_roc_auc.py
@@ -1,9 +1,11 @@
 import numpy as np
+import pytest
 import torch
 from sklearn.metrics import roc_auc_score
 
 from ignite.contrib.metrics import ROC_AUC
 from ignite.engine import Engine
+from ignite.metrics.epoch_metric import EpochMetricWarning
 
 
 def test_roc_auc_score():
@@ -110,3 +112,19 @@ def test_integration_roc_auc_score_with_activated_output_transform():
     roc_auc = engine.run(data, max_epochs=1).metrics["roc_auc"]
 
     assert roc_auc == np_roc_auc
+
+
+def test_check_compute_fn():
+    y_pred = torch.zeros((8, 13))
+    y_pred[:, 1] = 1
+    y_true = torch.zeros_like(y_pred)
+    output = (y_pred, y_true)
+
+    em = ROC_AUC(check_compute_fn=True)
+
+    em.reset()
+    with pytest.warns(EpochMetricWarning, match=r"Probably, there can be a problem with `compute_fn`"):
+        em.update(output)
+
+    em = ROC_AUC(check_compute_fn=False)
+    em.update(output)
diff --git a/tests/ignite/contrib/metrics/test_roc_curve.py b/tests/ignite/contrib/metrics/test_roc_curve.py
index 696078b4..4aa718a8 100644
--- a/tests/ignite/contrib/metrics/test_roc_curve.py
+++ b/tests/ignite/contrib/metrics/test_roc_curve.py
@@ -1,9 +1,11 @@
 import numpy as np
+import pytest
 import torch
 from sklearn.metrics import roc_curve
 
 from ignite.contrib.metrics.roc_auc import RocCurve
 from ignite.engine import Engine
+from ignite.metrics.epoch_metric import EpochMetricWarning
 
 
 def test_roc_curve():
@@ -89,3 +91,19 @@ def test_integration_roc_curve_with_activated_output_transform():
     assert np.array_equal(tpr, sk_tpr)
     # assert thresholds almost equal, due to numpy->torch->numpy conversion
     np.testing.assert_array_almost_equal(thresholds, sk_thresholds)
+
+
+def test_check_compute_fn():
+    y_pred = torch.zeros((8, 13))
+    y_pred[:, 1] = 1
+    y_true = torch.zeros_like(y_pred)
+    output = (y_pred, y_true)
+
+    em = RocCurve(check_compute_fn=True)
+
+    em.reset()
+    with pytest.warns(EpochMetricWarning, match=r"Probably, there can be a problem with `compute_fn`"):
+        em.update(output)
+
+    em = RocCurve(check_compute_fn=False)
+    em.update(output)
diff --git a/tests/ignite/metrics/test_epoch_metric.py b/tests/ignite/metrics/test_epoch_metric.py
index b110a4eb..64829cb4 100644
--- a/tests/ignite/metrics/test_epoch_metric.py
+++ b/tests/ignite/metrics/test_epoch_metric.py
@@ -144,6 +144,21 @@ def _test_warning():
         EpochMetric(compute_fn)
 
 
+def test_check_compute_fn():
+    def compute_fn(y_preds, y_targets):
+        raise Exception
+
+    em = EpochMetric(compute_fn, check_compute_fn=True)
+
+    em.reset()
+    output1 = (torch.rand(4, 3), torch.randint(0, 2, size=(4, 3), dtype=torch.long))
+    with pytest.warns(EpochMetricWarning, match=r"Probably, there can be a problem with `compute_fn`"):
+        em.update(output1)
+
+    em = EpochMetric(compute_fn, check_compute_fn=False)
+    em.update(output1)
+
+
 @pytest.mark.distributed
 @pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
 @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU")

pip install -e .
parse_log_pytest
requirements.txt
pytest
apt-get update apt-get install -y gcc pip install torch torchvision -f https://download.pytorch.org/whl/cpu/torch_stable.html -U
3.7
requirements-dev.txt
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
absl-py==2.1.0
alembic==1.12.1
arrow==1.2.3
attrs==24.2.0
boto3==1.33.13
botocore==1.33.13
bravado==11.1.0
bravado-core==6.1.1
cached-property==1.5.2
cachetools==5.5.2
certifi @ file:///croot/certifi_1671487769961/work/certifi
charset-normalizer==3.4.1
click==7.1.2
cloudpickle==2.2.1
codecov==2.1.13
coverage==7.2.7
cycler==0.11.0
databricks-cli==0.18.0
dill==0.3.7
docker==6.1.3
entrypoints==0.4
exceptiongroup==1.2.2
execnet==2.0.2
Flask==1.1.4
fonttools==4.38.0
fqdn==1.5.1
funcsigs==1.0.2
furl==2.1.4
future==1.0.0
gitdb==4.0.12
GitPython==3.1.44
google-auth==2.38.0
google-auth-oauthlib==0.4.6
greenlet==3.1.1
grpcio==1.62.3
gunicorn==20.1.0
gym==0.26.2
gym-notices==0.0.8
hestia==0.6.0
humanfriendly==10.0
idna==3.10
importlib-metadata==5.2.0
importlib-resources==5.12.0
iniconfig==2.0.0
isoduration==20.11.0
itsdangerous==1.1.0
Jinja2==2.10.3
jmespath==1.0.1
joblib==1.3.2
jsonpatch==1.33
jsonpointer==3.0.0
jsonref==1.1.0
jsonschema==4.17.3
kiwisolver==1.4.5
Mako==1.2.4
Markdown==3.4.4
MarkupSafe==2.1.5
marshmallow==3.0.0rc5
matplotlib==3.5.3
mlflow==1.30.1
monotonic==1.6
msgpack==1.0.5
neptune-client==1.11.1
networkx==2.6.3
numpy==1.21.6
oauthlib==3.2.2
orderedmultidict==1.0.1
packaging==21.3
pandas==1.3.5
pathlib2==2.3.7.post1
Pillow==9.5.0
pkgutil_resolve_name==1.3.10
pluggy==1.2.0
polyaxon-client==0.6.1
polyaxon-schemas==0.6.1
polystores==0.2.5
prometheus-client==0.17.1
prometheus_flask_exporter==0.23.2
protobuf==3.20.3
psutil==7.0.0
pyasn1==0.5.1
pyasn1-modules==0.3.0
PyJWT==2.8.0
pynvml==11.5.3
pyparsing==3.1.4
pyrsistent==0.19.3
pytest==7.4.4
pytest-cov==4.1.0
pytest-xdist==3.5.0
python-dateutil==2.9.0.post0
-e git+https://github.com/pytorch/ignite.git@a10bf6e298740557111783020870867f8a15eaa6#egg=pytorch_ignite
pytz==2022.7.1
PyYAML==6.0.1
querystring-parser==1.2.4
requests==2.31.0
requests-file==2.1.0
requests-oauthlib==2.0.0
requests-toolbelt==1.0.0
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rhea==0.5.5
rsa==4.9
s3transfer==0.8.2
scikit-learn==1.0.2
scipy==1.7.3
simplejson==3.20.1
six==1.17.0
smmap==5.0.2
SQLAlchemy==1.4.54
sqlparse==0.4.4
swagger-spec-validator==3.0.3
tabulate==0.9.0
tensorboard==2.11.2
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorboardX==2.6.2.2
threadpoolctl==3.1.0
tomli==2.0.1
torch==1.13.1+cpu
torchvision==0.14.1+cpu
tornado==6.2
tqdm==4.67.1
trains==0.16.4
typing_extensions==4.7.1
uri-template==1.3.0
urllib3==1.26.20
visdom==0.2.4
webcolors==1.13
websocket-client==1.6.1
Werkzeug==1.0.1
zipp==3.15.0

name: ignite
channels:
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - ca-certificates=2025.2.25=h06a4308_0
  - certifi=2022.12.7=py37h06a4308_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.4.4=h6a678d5_1
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - ncurses=6.4=h6a678d5_0
  - openssl=1.1.1w=h7f8727e_0
  - pip=22.3.1=py37h06a4308_0
  - python=3.7.16=h7a1cb2a_0
  - readline=8.2=h5eee18b_0
  - setuptools=65.6.3=py37h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - wheel=0.38.4=py37h06a4308_0
  - xz=5.6.4=h5eee18b_1
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - absl-py==2.1.0
      - alembic==1.12.1
      - arrow==1.2.3
      - attrs==24.2.0
      - boto3==1.33.13
      - botocore==1.33.13
      - bravado==11.1.0
      - bravado-core==6.1.1
      - cached-property==1.5.2
      - cachetools==5.5.2
      - charset-normalizer==3.4.1
      - click==7.1.2
      - cloudpickle==2.2.1
      - codecov==2.1.13
      - coverage==7.2.7
      - cycler==0.11.0
      - databricks-cli==0.18.0
      - dill==0.3.7
      - docker==6.1.3
      - entrypoints==0.4
      - exceptiongroup==1.2.2
      - execnet==2.0.2
      - flask==1.1.4
      - fonttools==4.38.0
      - fqdn==1.5.1
      - funcsigs==1.0.2
      - furl==2.1.4
      - future==1.0.0
      - gitdb==4.0.12
      - gitpython==3.1.44
      - google-auth==2.38.0
      - google-auth-oauthlib==0.4.6
      - greenlet==3.1.1
      - grpcio==1.62.3
      - gunicorn==20.1.0
      - gym==0.26.2
      - gym-notices==0.0.8
      - hestia==0.6.0
      - humanfriendly==10.0
      - idna==3.10
      - importlib-metadata==5.2.0
      - importlib-resources==5.12.0
      - iniconfig==2.0.0
      - isoduration==20.11.0
      - itsdangerous==1.1.0
      - jinja2==2.10.3
      - jmespath==1.0.1
      - joblib==1.3.2
      - jsonpatch==1.33
      - jsonpointer==3.0.0
      - jsonref==1.1.0
      - jsonschema==4.17.3
      - kiwisolver==1.4.5
      - mako==1.2.4
      - markdown==3.4.4
      - markupsafe==2.1.5
      - marshmallow==3.0.0rc5
      - matplotlib==3.5.3
      - mlflow==1.30.1
      - monotonic==1.6
      - msgpack==1.0.5
      - neptune-client==1.11.1
      - networkx==2.6.3
      - numpy==1.21.6
      - oauthlib==3.2.2
      - orderedmultidict==1.0.1
      - packaging==21.3
      - pandas==1.3.5
      - pathlib2==2.3.7.post1
      - pillow==9.5.0
      - pkgutil-resolve-name==1.3.10
      - pluggy==1.2.0
      - polyaxon-client==0.6.1
      - polyaxon-schemas==0.6.1
      - polystores==0.2.5
      - prometheus-client==0.17.1
      - prometheus-flask-exporter==0.23.2
      - protobuf==3.20.3
      - psutil==7.0.0
      - pyasn1==0.5.1
      - pyasn1-modules==0.3.0
      - pyjwt==2.8.0
      - pynvml==11.5.3
      - pyparsing==3.1.4
      - pyrsistent==0.19.3
      - pytest==7.4.4
      - pytest-cov==4.1.0
      - pytest-xdist==3.5.0
      - python-dateutil==2.9.0.post0
      - pytorch-ignite==0.4.0
      - pytz==2022.7.1
      - pyyaml==6.0.1
      - querystring-parser==1.2.4
      - requests==2.31.0
      - requests-file==2.1.0
      - requests-oauthlib==2.0.0
      - requests-toolbelt==1.0.0
      - rfc3339-validator==0.1.4
      - rfc3986-validator==0.1.1
      - rhea==0.5.5
      - rsa==4.9
      - s3transfer==0.8.2
      - scikit-learn==1.0.2
      - scipy==1.7.3
      - simplejson==3.20.1
      - six==1.17.0
      - smmap==5.0.2
      - sqlalchemy==1.4.54
      - sqlparse==0.4.4
      - swagger-spec-validator==3.0.3
      - tabulate==0.9.0
      - tensorboard==2.11.2
      - tensorboard-data-server==0.6.1
      - tensorboard-plugin-wit==1.8.1
      - tensorboardx==2.6.2.2
      - threadpoolctl==3.1.0
      - tomli==2.0.1
      - torch==1.13.1+cpu
      - torchvision==0.14.1+cpu
      - tornado==6.2
      - tqdm==4.67.1
      - trains==0.16.4
      - typing-extensions==4.7.1
      - uri-template==1.3.0
      - urllib3==1.26.20
      - visdom==0.2.4
      - webcolors==1.13
      - websocket-client==1.6.1
      - werkzeug==1.0.1
      - zipp==3.15.0
prefix: /opt/conda/envs/ignite

tests/ignite/contrib/metrics/regression/test__base.py::test_check_compute_fn tests/ignite/contrib/metrics/test_precision_recall_curve.py::test_check_compute_fn tests/ignite/contrib/metrics/test_roc_auc.py::test_check_compute_fn tests/ignite/contrib/metrics/test_roc_curve.py::test_check_compute_fn tests/ignite/metrics/test_epoch_metric.py::test_check_compute_fn
tests/ignite/contrib/metrics/regression/test__base.py::test_base_regression_shapes tests/ignite/contrib/metrics/regression/test__base.py::test_base_regression_epoch_shapes tests/ignite/contrib/metrics/regression/test__base.py::test_base_regression_compute_fn tests/ignite/contrib/metrics/test_precision_recall_curve.py::test_precision_recall_curve tests/ignite/contrib/metrics/test_precision_recall_curve.py::test_integration_precision_recall_curve_with_output_transform tests/ignite/contrib/metrics/test_precision_recall_curve.py::test_integration_precision_recall_curve_with_activated_output_transform tests/ignite/contrib/metrics/test_roc_auc.py::test_roc_auc_score tests/ignite/contrib/metrics/test_roc_auc.py::test_roc_auc_score_2 tests/ignite/contrib/metrics/test_roc_auc.py::test_integration_roc_auc_score_with_output_transform tests/ignite/contrib/metrics/test_roc_auc.py::test_integration_roc_auc_score_with_activated_output_transform tests/ignite/contrib/metrics/test_roc_curve.py::test_roc_curve tests/ignite/contrib/metrics/test_roc_curve.py::test_integration_roc_curve_with_output_transform tests/ignite/contrib/metrics/test_roc_curve.py::test_integration_roc_curve_with_activated_output_transform tests/ignite/metrics/test_epoch_metric.py::test_epoch_metric_wrong_setup_or_input tests/ignite/metrics/test_epoch_metric.py::test_epoch_metric tests/ignite/metrics/test_epoch_metric.py::test_mse_epoch_metric tests/ignite/metrics/test_epoch_metric.py::test_bad_compute_fn tests/ignite/metrics/test_epoch_metric.py::test_distrib_cpu
ignite/contrib/metrics/roc_auc.py ignite/contrib/metrics/average_precision.py ignite/contrib/metrics/precision_recall_curve.py ignite/contrib/metrics/regression/_base.py ignite/metrics/epoch_metric.py
ignite/contrib/metrics/roc_auc.py ignite/contrib/metrics/average_precision.py ignite/contrib/metrics/precision_recall_curve.py ignite/contrib/metrics/regression/_base.py ignite/metrics/epoch_metric.py