diff --git a/ignite/contrib/handlers/tensorboard_logger.py b/ignite/contrib/handlers/tensorboard_logger.py
index 39734a42..92afdaee 100644
--- a/ignite/contrib/handlers/tensorboard_logger.py
+++ b/ignite/contrib/handlers/tensorboard_logger.py
@@ -140,6 +140,9 @@ class WeightsScalarHandler(BaseWeightsScalarHandler):
 
         global_step = engine.state.get_event_attrib_value(event_name)
         for name, p in self.model.named_parameters():
+            if p.grad is None:
+                continue
+
             name = name.replace('.', '/')
             logger.writer.add_scalar("weights_{}/{}".format(self.reduction.__name__, name),
                                      self.reduction(p.data),
@@ -177,6 +180,9 @@ class WeightsHistHandler(BaseWeightsHistHandler):
 
         global_step = engine.state.get_event_attrib_value(event_name)
         for name, p in self.model.named_parameters():
+            if p.grad is None:
+                continue
+
             name = name.replace('.', '/')
             logger.writer.add_histogram(tag="weights/{}".format(name),
                                         values=p.data.detach().cpu().numpy(),
@@ -216,6 +222,9 @@ class GradsScalarHandler(BaseWeightsScalarHandler):
 
         global_step = engine.state.get_event_attrib_value(event_name)
         for name, p in self.model.named_parameters():
+            if p.grad is None:
+                continue
+
             name = name.replace('.', '/')
             logger.writer.add_scalar("grads_{}/{}".format(self.reduction.__name__, name),
                                      self.reduction(p.grad),
@@ -252,6 +261,9 @@ class GradsHistHandler(BaseWeightsHistHandler):
 
         global_step = engine.state.get_event_attrib_value(event_name)
         for name, p in self.model.named_parameters():
+            if p.grad is None:
+                continue
+
             name = name.replace('.', '/')
             logger.writer.add_histogram(tag="grads/{}".format(name),
                                         values=p.grad.detach().cpu().numpy(),

Tensorboard Logger does not work with layer freezing
Consider the following (simplified and self-contained) notebook:
https://gist.github.com/marrrcin/629227cf33d88af6192048871daa3a09
which uses mostly the code from Ignite examples to create `trainer` and attach `TensorboardLogger` to training events.

If you use pre-trained model and freeze some layers, you end up with code that will not work, as Ignite tries to collect gradient histograms/scalars for every model parameter, even though some of them are already disabled from updating.

The exception thrown is the following:

```
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-5ffffec84f3c> in <module>
     65     print("Done.")
     66 
---> 67 example()

<ipython-input-3-5ffffec84f3c> in example()
     62 
     63 
---> 64     trainer.run(DataLoader(FakeDataset(), batch_size=16), max_epochs=5)
     65     print("Done.")
     66 

/anaconda3/envs/ignite/lib/python3.7/site-packages/pytorch_ignite-0.2.0-py3.7.egg/ignite/engine/engine.py in run(self, data, max_epochs)
    357         except BaseException as e:
    358             self._logger.error("Engine run is terminating due to exception: %s.", str(e))
--> 359             self._handle_exception(e)
    360 
    361         return self.state

/anaconda3/envs/ignite/lib/python3.7/site-packages/pytorch_ignite-0.2.0-py3.7.egg/ignite/engine/engine.py in _handle_exception(self, e)
    322             self._fire_event(Events.EXCEPTION_RAISED, e)
    323         else:
--> 324             raise e
    325 
    326     def run(self, data, max_epochs=1):

/anaconda3/envs/ignite/lib/python3.7/site-packages/pytorch_ignite-0.2.0-py3.7.egg/ignite/engine/engine.py in run(self, data, max_epochs)
    344                 self.state.epoch += 1
    345                 self._fire_event(Events.EPOCH_STARTED)
--> 346                 hours, mins, secs = self._run_once_on_dataset()
    347                 self._logger.info("Epoch[%s] Complete. Time taken: %02d:%02d:%02d", self.state.epoch, hours, mins, secs)
    348                 if self.should_terminate:

/anaconda3/envs/ignite/lib/python3.7/site-packages/pytorch_ignite-0.2.0-py3.7.egg/ignite/engine/engine.py in _run_once_on_dataset(self)
    311         except BaseException as e:
    312             self._logger.error("Current run is terminating due to exception: %s.", str(e))
--> 313             self._handle_exception(e)
    314 
    315         time_taken = time.time() - start_time

/anaconda3/envs/ignite/lib/python3.7/site-packages/pytorch_ignite-0.2.0-py3.7.egg/ignite/engine/engine.py in _handle_exception(self, e)
    322             self._fire_event(Events.EXCEPTION_RAISED, e)
    323         else:
--> 324             raise e
    325 
    326     def run(self, data, max_epochs=1):

/anaconda3/envs/ignite/lib/python3.7/site-packages/pytorch_ignite-0.2.0-py3.7.egg/ignite/engine/engine.py in _run_once_on_dataset(self)
    304                 self._fire_event(Events.ITERATION_STARTED)
    305                 self.state.output = self._process_function(self, batch)
--> 306                 self._fire_event(Events.ITERATION_COMPLETED)
    307                 if self.should_terminate or self.should_terminate_single_epoch:
    308                     self.should_terminate_single_epoch = False

/anaconda3/envs/ignite/lib/python3.7/site-packages/pytorch_ignite-0.2.0-py3.7.egg/ignite/engine/engine.py in _fire_event(self, event_name, *event_args, **event_kwargs)
    257             for func, args, kwargs in self._event_handlers[event_name]:
    258                 kwargs.update(event_kwargs)
--> 259                 func(self, *(event_args + args), **kwargs)
    260 
    261     def fire_event(self, event_name):

/anaconda3/envs/ignite/lib/python3.7/site-packages/pytorch_ignite-0.2.0-py3.7.egg/ignite/contrib/handlers/tensorboard_logger.py in __call__(self, engine, logger, event_name)
    219             name = name.replace('.', '/')
    220             logger.writer.add_scalar("grads_{}/{}".format(self.reduction.__name__, name),
--> 221                                      self.reduction(p.grad),
    222                                      global_step)
    223 

/anaconda3/envs/ignite/lib/python3.7/site-packages/torch/functional.py in norm(input, p, dim, keepdim, out, dtype)
    676         (tensor(3.7417), tensor(11.2250))
    677     """
--> 678     ndim = input.dim()
    679 
    680     # catch default case

AttributeError: 'NoneType' object has no attribute 'dim'
```

diff --git a/tests/ignite/contrib/handlers/conftest.py b/tests/ignite/contrib/handlers/conftest.py
new file mode 100644
index 00000000..8f51d777
--- /dev/null
+++ b/tests/ignite/contrib/handlers/conftest.py
@@ -0,0 +1,44 @@
+import numpy as np
+import pytest
+import torch
+from mock import Mock
+
+
+@pytest.fixture()
+def norm_mock():
+    def norm(x):
+        return np.linalg.norm(x)
+    norm_mock = Mock(side_effect=norm, spec=norm)
+    norm_mock.configure_mock(**{"__name__": "norm"})
+    norm_mock.reset_mock()
+    return norm_mock
+
+
+@pytest.fixture()
+def dummy_model_factory():
+    class DummyModel(torch.nn.Module):
+        def __init__(self):
+            super(DummyModel, self).__init__()
+            self.fc1 = torch.nn.Linear(10, 10)
+            self.fc2 = torch.nn.Linear(12, 12)
+            self.fc1.weight.data.zero_()
+            self.fc1.bias.data.zero_()
+            self.fc2.weight.data.fill_(1.0)
+            self.fc2.bias.data.fill_(1.0)
+
+    def get_dummy_model(with_grads=True, with_frozen_layer=False):
+        model = DummyModel()
+        if with_grads:
+            model.fc2.weight.grad = torch.zeros_like(model.fc2.weight)
+            model.fc2.bias.grad = torch.zeros_like(model.fc2.bias)
+
+            if not with_frozen_layer:
+                model.fc1.weight.grad = torch.zeros_like(model.fc1.weight)
+                model.fc1.bias.grad = torch.zeros_like(model.fc1.bias)
+
+        if with_frozen_layer:
+            for param in model.fc1.parameters():
+                param.requires_grad = False
+        return model
+
+    return get_dummy_model
diff --git a/tests/ignite/contrib/handlers/test_tensorboard_logger.py b/tests/ignite/contrib/handlers/test_tensorboard_logger.py
index 5ceb0cfa..f32d3843 100644
--- a/tests/ignite/contrib/handlers/test_tensorboard_logger.py
+++ b/tests/ignite/contrib/handlers/test_tensorboard_logger.py
@@ -3,6 +3,7 @@ import tempfile
 import shutil
 import math
 
+import numpy as np
 import pytest
 
 from mock import MagicMock, call, ANY
@@ -183,20 +184,9 @@ def test_weights_scalar_handler_wrong_setup():
         wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
 
 
-def test_weights_scalar_handler():
+def test_weights_scalar_handler(dummy_model_factory):
 
-    class DummyModel(torch.nn.Module):
-
-        def __init__(self):
-            super(DummyModel, self).__init__()
-            self.fc1 = torch.nn.Linear(10, 10)
-            self.fc2 = torch.nn.Linear(12, 12)
-            self.fc1.weight.data.zero_()
-            self.fc1.bias.data.zero_()
-            self.fc2.weight.data.fill_(1.0)
-            self.fc2.bias.data.fill_(1.0)
-
-    model = DummyModel()
+    model = dummy_model_factory(with_grads=True, with_frozen_layer=False)
 
     wrapper = WeightsScalarHandler(model)
     mock_logger = MagicMock(spec=TensorboardLogger)
@@ -217,6 +207,34 @@ def test_weights_scalar_handler():
     ], any_order=True)
 
 
+def test_weights_scalar_handler_frozen_layers(dummy_model_factory):
+
+    model = dummy_model_factory(with_grads=True, with_frozen_layer=True)
+
+    wrapper = WeightsScalarHandler(model)
+    mock_logger = MagicMock(spec=TensorboardLogger)
+    mock_logger.writer = MagicMock()
+
+    mock_engine = MagicMock()
+    mock_engine.state = State()
+    mock_engine.state.epoch = 5
+
+    wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
+
+    mock_logger.writer.add_scalar.assert_has_calls([
+        call("weights_norm/fc2/weight", 12.0, 5),
+        call("weights_norm/fc2/bias", math.sqrt(12.0), 5),
+    ], any_order=True)
+
+    with pytest.raises(AssertionError):
+        mock_logger.writer.add_scalar.assert_has_calls([
+            call("weights_norm/fc1/weight", 12.0, 5),
+            call("weights_norm/fc1/bias", math.sqrt(12.0), 5),
+        ], any_order=True)
+
+    assert mock_logger.writer.add_scalar.call_count == 2
+
+
 def test_weights_hist_handler_wrong_setup():
 
     with pytest.raises(TypeError, match="Argument model should be of type torch.nn.Module"):
@@ -230,20 +248,9 @@ def test_weights_hist_handler_wrong_setup():
         wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
 
 
-def test_weights_hist_handler():
-
-    class DummyModel(torch.nn.Module):
-
-        def __init__(self):
-            super(DummyModel, self).__init__()
-            self.fc1 = torch.nn.Linear(10, 10)
-            self.fc2 = torch.nn.Linear(12, 12)
-            self.fc1.weight.data.zero_()
-            self.fc1.bias.data.zero_()
-            self.fc2.weight.data.fill_(1.0)
-            self.fc2.bias.data.fill_(1.0)
+def test_weights_hist_handler(dummy_model_factory):
 
-    model = DummyModel()
+    model = dummy_model_factory(with_grads=True, with_frozen_layer=False)
 
     wrapper = WeightsHistHandler(model)
     mock_logger = MagicMock(spec=TensorboardLogger)
@@ -264,6 +271,33 @@ def test_weights_hist_handler():
     ], any_order=True)
 
 
+def test_weights_hist_handler_frozen_layers(dummy_model_factory):
+
+    model = dummy_model_factory(with_grads=True, with_frozen_layer=True)
+
+    wrapper = WeightsHistHandler(model)
+    mock_logger = MagicMock(spec=TensorboardLogger)
+    mock_logger.writer = MagicMock()
+
+    mock_engine = MagicMock()
+    mock_engine.state = State()
+    mock_engine.state.epoch = 5
+
+    wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
+
+    mock_logger.writer.add_histogram.assert_has_calls([
+        call(tag="weights/fc2/weight", values=ANY, global_step=5),
+        call(tag="weights/fc2/bias", values=ANY, global_step=5),
+    ], any_order=True)
+
+    with pytest.raises(AssertionError):
+        mock_logger.writer.add_histogram.assert_has_calls([
+            call(tag="weights/fc1/weight", values=ANY, global_step=5),
+            call(tag="weights/fc1/bias", values=ANY, global_step=5),
+        ], any_order=True)
+    assert mock_logger.writer.add_histogram.call_count == 2
+
+
 def test_grads_scalar_handler_wrong_setup():
 
     with pytest.raises(TypeError, match="Argument model should be of type torch.nn.Module"):
@@ -280,43 +314,57 @@ def test_grads_scalar_handler_wrong_setup():
         wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
 
 
-def test_grads_scalar_handler():
+def test_grads_scalar_handler(dummy_model_factory, norm_mock):
+    model = dummy_model_factory(with_grads=True, with_frozen_layer=False)
 
-    class DummyModel(torch.nn.Module):
+    wrapper = GradsScalarHandler(model, reduction=norm_mock)
+    mock_logger = MagicMock(spec=TensorboardLogger)
+    mock_logger.writer = MagicMock()
+
+    mock_engine = MagicMock()
+    mock_engine.state = State()
+    mock_engine.state.epoch = 5
+    norm_mock.reset_mock()
 
-        def __init__(self):
-            super(DummyModel, self).__init__()
-            self.fc1 = torch.nn.Linear(10, 10)
-            self.fc2 = torch.nn.Linear(12, 12)
-            self.fc1.weight.data.zero_()
-            self.fc1.bias.data.zero_()
-            self.fc2.weight.data.fill_(1.0)
-            self.fc2.bias.data.fill_(1.0)
+    wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
+
+    mock_logger.writer.add_scalar.assert_has_calls([
+        call("grads_norm/fc1/weight", ANY, 5),
+        call("grads_norm/fc1/bias", ANY, 5),
+        call("grads_norm/fc2/weight", ANY, 5),
+        call("grads_norm/fc2/bias", ANY, 5),
+    ], any_order=True)
+    assert mock_logger.writer.add_scalar.call_count == 4
+    assert norm_mock.call_count == 4
 
-    model = DummyModel()
 
-    def norm(x):
-        return 0.0
+def test_grads_scalar_handler_frozen_layers(dummy_model_factory, norm_mock):
+    model = dummy_model_factory(with_grads=True, with_frozen_layer=True)
 
-    wrapper = GradsScalarHandler(model, reduction=norm)
+    wrapper = GradsScalarHandler(model, reduction=norm_mock)
     mock_logger = MagicMock(spec=TensorboardLogger)
     mock_logger.writer = MagicMock()
 
     mock_engine = MagicMock()
     mock_engine.state = State()
     mock_engine.state.epoch = 5
+    norm_mock.reset_mock()
 
     wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
 
-    assert mock_logger.writer.add_scalar.call_count == 4
-
     mock_logger.writer.add_scalar.assert_has_calls([
-        call("grads_norm/fc1/weight", ANY, 5),
-        call("grads_norm/fc1/bias", ANY, 5),
         call("grads_norm/fc2/weight", ANY, 5),
         call("grads_norm/fc2/bias", ANY, 5),
     ], any_order=True)
 
+    with pytest.raises(AssertionError):
+        mock_logger.writer.add_scalar.assert_has_calls([
+            call("grads_norm/fc1/weight", ANY, 5),
+            call("grads_norm/fc1/bias", ANY, 5),
+        ], any_order=True)
+    assert mock_logger.writer.add_scalar.call_count == 2
+    assert norm_mock.call_count == 2
+
 
 def test_grads_hist_handler_wrong_setup():
 
@@ -331,20 +379,8 @@ def test_grads_hist_handler_wrong_setup():
         wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
 
 
-def test_grads_hist_handler():
-
-    class DummyModel(torch.nn.Module):
-
-        def __init__(self):
-            super(DummyModel, self).__init__()
-            self.fc1 = torch.nn.Linear(10, 10)
-            self.fc2 = torch.nn.Linear(12, 12)
-            self.fc1.weight.data.zero_()
-            self.fc1.bias.data.zero_()
-            self.fc2.weight.data.fill_(1.0)
-            self.fc2.bias.data.fill_(1.0)
-
-    model = DummyModel()
+def test_grads_hist_handler(dummy_model_factory):
+    model = dummy_model_factory(with_grads=True, with_frozen_layer=False)
 
     wrapper = GradsHistHandler(model)
     mock_logger = MagicMock(spec=TensorboardLogger)
@@ -354,12 +390,6 @@ def test_grads_hist_handler():
     mock_engine.state = State()
     mock_engine.state.epoch = 5
 
-    model.fc1.weight.grad = torch.zeros_like(model.fc1.weight)
-    model.fc1.bias.grad = torch.zeros_like(model.fc1.bias)
-
-    model.fc2.weight.grad = torch.zeros_like(model.fc2.weight)
-    model.fc2.bias.grad = torch.zeros_like(model.fc2.bias)
-
     wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
 
     assert mock_logger.writer.add_histogram.call_count == 4
@@ -371,6 +401,32 @@ def test_grads_hist_handler():
     ], any_order=True)
 
 
+def test_grads_hist_frozen_layers(dummy_model_factory):
+    model = dummy_model_factory(with_grads=True, with_frozen_layer=True)
+
+    wrapper = GradsHistHandler(model)
+    mock_logger = MagicMock(spec=TensorboardLogger)
+    mock_logger.writer = MagicMock()
+
+    mock_engine = MagicMock()
+    mock_engine.state = State()
+    mock_engine.state.epoch = 5
+
+    wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
+
+    assert mock_logger.writer.add_histogram.call_count == 2
+    mock_logger.writer.add_histogram.assert_has_calls([
+        call(tag="grads/fc2/weight", values=ANY, global_step=5),
+        call(tag="grads/fc2/bias", values=ANY, global_step=5),
+    ], any_order=True)
+
+    with pytest.raises(AssertionError):
+        mock_logger.writer.add_histogram.assert_has_calls([
+            call(tag="grads/fc1/weight", values=ANY, global_step=5),
+            call(tag="grads/fc1/bias", values=ANY, global_step=5),
+        ], any_order=True)
+
+
 def test_integration(dirname):
 
     n_epochs = 5

pip install -e .
parse_log_pytest
pytest
numpy mock pytest codecov pytest-cov matplotlib pandas gym tqdm scikit-learn tensorboardX visdom polyaxon-client mlflow
apt-get update apt-get install -y gcc
3.6
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
alembic==1.7.7
attrs @ file:///opt/conda/conda-bld/attrs_1642510447205/work
certifi==2021.5.30
charset-normalizer==2.0.12
click==7.1.2
cloudpickle==2.2.1
codecov==2.1.13
coverage==6.2
cycler==0.11.0
databricks-cli==0.17.8
dataclasses==0.8
decorator==4.4.2
docker==5.0.3
entrypoints==0.4
Flask==1.1.4
gitdb==4.0.9
GitPython==3.1.18
greenlet==2.0.2
gunicorn==21.2.0
gym==0.26.2
gym-notices==0.0.8
hestia==0.6.0
idna==3.10
importlib-metadata @ file:///tmp/build/80754af9/importlib-metadata_1631916693255/work
importlib-resources==5.4.0
iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work
itsdangerous==1.1.0
Jinja2==2.10.3
joblib==1.1.1
jsonpatch==1.32
jsonpointer==2.3
kiwisolver==1.3.1
Mako==1.1.6
MarkupSafe==2.0.1
marshmallow==3.0.0rc5
matplotlib==3.3.4
mlflow==1.23.1
mock==5.2.0
more-itertools @ file:///tmp/build/80754af9/more-itertools_1637733554872/work
networkx==2.5.1
numpy==1.19.5
oauthlib==3.2.2
packaging @ file:///tmp/build/80754af9/packaging_1637314298585/work
pandas==1.1.5
Pillow==8.4.0
pluggy @ file:///tmp/build/80754af9/pluggy_1615976315926/work
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==4.21.0
psutil==7.0.0
py @ file:///opt/conda/conda-bld/py_1644396412707/work
PyJWT==2.4.0
pyparsing @ file:///tmp/build/80754af9/pyparsing_1635766073266/work
pytest==6.2.4
pytest-cov==4.0.0
python-dateutil==2.9.0.post0
-e git+https://github.com/pytorch/ignite.git@ec881241ed716fc7127c3818a8c997ab2f6e1155#egg=pytorch_ignite
pytz==2025.2
PyYAML==6.0.1
querystring-parser==1.2.4
requests==2.27.1
requests-toolbelt==1.0.0
rhea==0.5.5
scikit-learn==0.24.2
scipy==1.5.4
six==1.17.0
smmap==5.0.0
SQLAlchemy==1.4.54
sqlparse==0.4.4
tabulate==0.8.10
tensorboardX==2.6.2.2
threadpoolctl==3.1.0
toml @ file:///tmp/build/80754af9/toml_1616166611790/work
tomli==1.2.3
torch==1.10.2
tornado==6.1
tqdm==4.64.1
typing_extensions @ file:///opt/conda/conda-bld/typing_extensions_1647553014482/work
urllib3==1.26.20
visdom==0.2.4
websocket-client==1.3.1
Werkzeug==1.0.1
zipp @ file:///tmp/build/80754af9/zipp_1633618647012/work

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
  - attrs=21.4.0=pyhd3eb1b0_0
  - ca-certificates=2025.2.25=h06a4308_0
  - certifi=2021.5.30=py36h06a4308_0
  - importlib-metadata=4.8.1=py36h06a4308_0
  - importlib_metadata=4.8.1=hd3eb1b0_0
  - iniconfig=1.1.1=pyhd3eb1b0_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.3=he6710b0_2
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - more-itertools=8.12.0=pyhd3eb1b0_0
  - ncurses=6.4=h6a678d5_0
  - openssl=1.1.1w=h7f8727e_0
  - packaging=21.3=pyhd3eb1b0_0
  - pip=21.2.2=py36h06a4308_0
  - pluggy=0.13.1=py36h06a4308_0
  - py=1.11.0=pyhd3eb1b0_0
  - pyparsing=3.0.4=pyhd3eb1b0_0
  - pytest=6.2.4=py36h06a4308_2
  - python=3.6.13=h12debd9_1
  - readline=8.2=h5eee18b_0
  - setuptools=58.0.4=py36h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - toml=0.10.2=pyhd3eb1b0_0
  - typing_extensions=4.1.1=pyh06a4308_0
  - wheel=0.37.1=pyhd3eb1b0_0
  - xz=5.6.4=h5eee18b_1
  - zipp=3.6.0=pyhd3eb1b0_0
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - alembic==1.7.7
      - charset-normalizer==2.0.12
      - click==7.1.2
      - cloudpickle==2.2.1
      - codecov==2.1.13
      - coverage==6.2
      - cycler==0.11.0
      - databricks-cli==0.17.8
      - dataclasses==0.8
      - decorator==4.4.2
      - docker==5.0.3
      - entrypoints==0.4
      - flask==1.1.4
      - gitdb==4.0.9
      - gitpython==3.1.18
      - greenlet==2.0.2
      - gunicorn==21.2.0
      - gym==0.26.2
      - gym-notices==0.0.8
      - hestia==0.6.0
      - idna==3.10
      - importlib-resources==5.4.0
      - itsdangerous==1.1.0
      - jinja2==2.10.3
      - joblib==1.1.1
      - jsonpatch==1.32
      - jsonpointer==2.3
      - kiwisolver==1.3.1
      - mako==1.1.6
      - markupsafe==2.0.1
      - marshmallow==3.0.0rc5
      - matplotlib==3.3.4
      - mlflow==1.23.1
      - mock==5.2.0
      - networkx==2.5.1
      - numpy==1.19.5
      - oauthlib==3.2.2
      - pandas==1.1.5
      - pillow==8.4.0
      - 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==4.21.0
      - psutil==7.0.0
      - pyjwt==2.4.0
      - pytest-cov==4.0.0
      - python-dateutil==2.9.0.post0
      - pytz==2025.2
      - pyyaml==6.0.1
      - querystring-parser==1.2.4
      - requests==2.27.1
      - requests-toolbelt==1.0.0
      - rhea==0.5.5
      - scikit-learn==0.24.2
      - scipy==1.5.4
      - six==1.17.0
      - smmap==5.0.0
      - sqlalchemy==1.4.54
      - sqlparse==0.4.4
      - tabulate==0.8.10
      - tensorboardx==2.6.2.2
      - threadpoolctl==3.1.0
      - tomli==1.2.3
      - torch==1.10.2
      - tornado==6.1
      - tqdm==4.64.1
      - urllib3==1.26.20
      - visdom==0.2.4
      - websocket-client==1.3.1
      - werkzeug==1.0.1
prefix: /opt/conda/envs/ignite

tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_weights_scalar_handler_frozen_layers tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_weights_hist_handler_frozen_layers tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_grads_scalar_handler_frozen_layers tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_grads_hist_frozen_layers
tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_optimizer_params_handler_wrong_setup tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_optimizer_params tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_output_handler_with_wrong_logger_type tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_output_handler_output_transform tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_output_handler_metric_names tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_output_handler_both tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_weights_scalar_handler_wrong_setup tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_weights_scalar_handler tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_weights_hist_handler_wrong_setup tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_weights_hist_handler tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_grads_scalar_handler_wrong_setup tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_grads_scalar_handler tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_grads_hist_handler_wrong_setup tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_grads_hist_handler tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_integration tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_integration_as_context_manager tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_no_tensorboardX
ignite/contrib/handlers/tensorboard_logger.py
ignite/contrib/handlers/tensorboard_logger.py