Bibonaut: @vfdev-5 What is the best way to create tests with optional tag but without full duplication of affected existing tests? Do you have a good idea? Otherwise I would just clone them and add optional tags. 
vfdev-5: @Bibonaut Visdom logger reimplements `BaseWeightsScalarHandler`, could you please add `tag` to its `WeightsScalarHandler` and `GradsScalarHandler`.

About tests factorization, maybe a solution like that could work :
```
def test_something():
   
    def _test(tag):
         # test with/without tag

    _test(tag=None)
    _test(tag="some tag")
```


Bibonaut: @vfdev-5 I've added tags to Visdom logger and added tests for Tensorboard logger and Visdom logger. Is there anything to do?
vfdev-5: @Bibonaut I think it is good for the issue. Thanks for the PR !
diff --git a/ignite/contrib/handlers/base_logger.py b/ignite/contrib/handlers/base_logger.py
index f965aaf9..de5c4c37 100644
--- a/ignite/contrib/handlers/base_logger.py
+++ b/ignite/contrib/handlers/base_logger.py
@@ -130,7 +130,7 @@ class BaseWeightsScalarHandler(BaseHandler):
     Helper handler to log model's weights as scalars.
     """
 
-    def __init__(self, model, reduction=torch.norm):
+    def __init__(self, model, reduction=torch.norm, tag=None):
         if not isinstance(model, torch.nn.Module):
             raise TypeError("Argument model should be of type torch.nn.Module, "
                             "but given {}".format(type(model)))
@@ -149,6 +149,7 @@ class BaseWeightsScalarHandler(BaseHandler):
 
         self.model = model
         self.reduction = reduction
+        self.tag = tag
 
 
 class BaseWeightsHistHandler(BaseHandler):
@@ -156,9 +157,10 @@ class BaseWeightsHistHandler(BaseHandler):
     Helper handler to log model's weights as histograms.
     """
 
-    def __init__(self, model):
+    def __init__(self, model, tag=None):
         if not isinstance(model, torch.nn.Module):
             raise TypeError("Argument model should be of type torch.nn.Module, "
                             "but given {}".format(type(model)))
 
         self.model = model
+        self.tag = tag
diff --git a/ignite/contrib/handlers/tensorboard_logger.py b/ignite/contrib/handlers/tensorboard_logger.py
index c5bf910f..b1cb76f0 100644
--- a/ignite/contrib/handlers/tensorboard_logger.py
+++ b/ignite/contrib/handlers/tensorboard_logger.py
@@ -168,10 +168,11 @@ class WeightsScalarHandler(BaseWeightsScalarHandler):
     Args:
         model (torch.nn.Module): model to log weights
         reduction (callable): function to reduce parameters into scalar
+        tag (str, optional): common title for all produced plots. For example, 'generator'
 
     """
-    def __init__(self, model, reduction=torch.norm):
-        super(WeightsScalarHandler, self).__init__(model, reduction)
+    def __init__(self, model, reduction=torch.norm, tag=None):
+        super(WeightsScalarHandler, self).__init__(model, reduction, tag=tag)
 
     def __call__(self, engine, logger, event_name):
 
@@ -179,12 +180,13 @@ class WeightsScalarHandler(BaseWeightsScalarHandler):
             raise RuntimeError("Handler 'WeightsScalarHandler' works only with TensorboardLogger")
 
         global_step = engine.state.get_event_attrib_value(event_name)
+        tag_prefix = "{}/".format(self.tag) if self.tag else ""
         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),
+            logger.writer.add_scalar("{}weights_{}/{}".format(tag_prefix, self.reduction.__name__, name),
                                      self.reduction(p.data),
                                      global_step)
 
@@ -208,23 +210,25 @@ class WeightsHistHandler(BaseWeightsHistHandler):
 
     Args:
         model (torch.nn.Module): model to log weights
+        tag (str, optional): common title for all produced plots. For example, 'generator'
 
     """
 
-    def __init__(self, model):
-        super(WeightsHistHandler, self).__init__(model)
+    def __init__(self, model, tag=None):
+        super(WeightsHistHandler, self).__init__(model, tag=tag)
 
     def __call__(self, engine, logger, event_name):
         if not isinstance(logger, TensorboardLogger):
             raise RuntimeError("Handler 'WeightsHistHandler' works only with TensorboardLogger")
 
         global_step = engine.state.get_event_attrib_value(event_name)
+        tag_prefix = "{}/".format(self.tag) if self.tag else ""
         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),
+            logger.writer.add_histogram(tag="{}weights/{}".format(tag_prefix, name),
                                         values=p.data.detach().cpu().numpy(),
                                         global_step=global_step)
 
@@ -251,22 +255,24 @@ class GradsScalarHandler(BaseWeightsScalarHandler):
     Args:
         model (torch.nn.Module): model to log weights
         reduction (callable): function to reduce parameters into scalar
+        tag (str, optional): common title for all produced plots. For example, 'generator'
 
     """
-    def __init__(self, model, reduction=torch.norm):
-        super(GradsScalarHandler, self).__init__(model, reduction)
+    def __init__(self, model, reduction=torch.norm, tag=None):
+        super(GradsScalarHandler, self).__init__(model, reduction, tag=tag)
 
     def __call__(self, engine, logger, event_name):
         if not isinstance(logger, TensorboardLogger):
             raise RuntimeError("Handler 'GradsScalarHandler' works only with TensorboardLogger")
 
         global_step = engine.state.get_event_attrib_value(event_name)
+        tag_prefix = "{}/".format(self.tag) if self.tag else ""
         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),
+            logger.writer.add_scalar("{}grads_{}/{}".format(tag_prefix, self.reduction.__name__, name),
                                      self.reduction(p.grad),
                                      global_step)
 
@@ -290,22 +296,24 @@ class GradsHistHandler(BaseWeightsHistHandler):
 
     Args:
         model (torch.nn.Module): model to log weights
+        tag (str, optional): common title for all produced plots. For example, 'generator'
 
     """
-    def __init__(self, model):
-        super(GradsHistHandler, self).__init__(model)
+    def __init__(self, model, tag=None):
+        super(GradsHistHandler, self).__init__(model, tag=tag)
 
     def __call__(self, engine, logger, event_name):
         if not isinstance(logger, TensorboardLogger):
             raise RuntimeError("Handler 'GradsHistHandler' works only with TensorboardLogger")
 
         global_step = engine.state.get_event_attrib_value(event_name)
+        tag_prefix = "{}/".format(self.tag) if self.tag else ""
         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),
+            logger.writer.add_histogram(tag="{}grads/{}".format(tag_prefix, name),
                                         values=p.grad.detach().cpu().numpy(),
                                         global_step=global_step)
 
diff --git a/ignite/contrib/handlers/visdom_logger.py b/ignite/contrib/handlers/visdom_logger.py
index 9561432a..88a2b51e 100644
--- a/ignite/contrib/handlers/visdom_logger.py
+++ b/ignite/contrib/handlers/visdom_logger.py
@@ -234,11 +234,12 @@ class WeightsScalarHandler(BaseWeightsScalarHandler, _BaseVisDrawer):
     Args:
         model (torch.nn.Module): model to log weights
         reduction (callable): function to reduce parameters into scalar
+        tag (str, optional): common title for all produced plots. For example, 'generator'
         show_legend (bool, optional): flag to show legend in the window
     """
 
-    def __init__(self, model, reduction=torch.norm, show_legend=False):
-        super(WeightsScalarHandler, self).__init__(model, reduction)
+    def __init__(self, model, reduction=torch.norm, tag=None, show_legend=False):
+        super(WeightsScalarHandler, self).__init__(model, reduction, tag=tag)
         _BaseVisDrawer.__init__(self, show_legend=show_legend)
 
     def __call__(self, engine, logger, event_name):
@@ -247,9 +248,10 @@ class WeightsScalarHandler(BaseWeightsScalarHandler, _BaseVisDrawer):
             raise RuntimeError("Handler 'WeightsScalarHandler' works only with VisdomLogger")
 
         global_step = engine.state.get_event_attrib_value(event_name)
+        tag_prefix = "{}/".format(self.tag) if self.tag else ""
         for name, p in self.model.named_parameters():
             name = name.replace('.', '/')
-            k = "weights_{}/{}".format(self.reduction.__name__, name)
+            k = "{}weights_{}/{}".format(tag_prefix, self.reduction.__name__, name)
             v = float(self.reduction(p.data))
             self.add_scalar(logger, k, v, event_name, global_step)
 
@@ -278,12 +280,13 @@ class GradsScalarHandler(BaseWeightsScalarHandler, _BaseVisDrawer):
     Args:
         model (torch.nn.Module): model to log weights
         reduction (callable): function to reduce parameters into scalar
+        tag (str, optional): common title for all produced plots. For example, 'generator'
         show_legend (bool, optional): flag to show legend in the window
 
     """
 
-    def __init__(self, model, reduction=torch.norm, show_legend=False):
-        super(GradsScalarHandler, self).__init__(model, reduction)
+    def __init__(self, model, reduction=torch.norm, tag=None, show_legend=False):
+        super(GradsScalarHandler, self).__init__(model, reduction, tag)
         _BaseVisDrawer.__init__(self, show_legend=show_legend)
 
     def __call__(self, engine, logger, event_name):
@@ -291,9 +294,10 @@ class GradsScalarHandler(BaseWeightsScalarHandler, _BaseVisDrawer):
             raise RuntimeError("Handler 'GradsScalarHandler' works only with VisdomLogger")
 
         global_step = engine.state.get_event_attrib_value(event_name)
+        tag_prefix = "{}/".format(self.tag) if self.tag else ""
         for name, p in self.model.named_parameters():
             name = name.replace('.', '/')
-            k = "grads_{}/{}".format(self.reduction.__name__, name)
+            k = "{}grads_{}/{}".format(tag_prefix, self.reduction.__name__, name)
             v = float(self.reduction(p.grad))
             self.add_scalar(logger, k, v, event_name, global_step)
 

TensorboardLogger - tag/prefix for weights, grads and optimizer logging
### Problem
I have to log multiple models/optimizers with TensorboardLogger and I want them to be displayed in tensorboard in separate *subpanels* with distinct names/tags. 
At the moment, it is not possible to provide a custom tag or prefix for some handlers that log model parameters, because the tag is deduced from `model.named_parameters()`. 

As a consequence, parameters of multiple models are plottet to one graph.

### Solution
The affected handlers allow passing a custom prefix/tag to the function that is prepended to the deduced tag. 

E.g. for `GradsHistHandler` (snippet):
```python
logger.writer.add_histogram(tag="grads/{}".format(name),
                                        values=p.grad.detach().cpu().numpy(),
                                        global_step=global_step)
```

It can be changed to 
```python
logger.writer.add_histogram(tag=self.prefix + "grads/{}".format(name),
                                        values=p.grad.detach().cpu().numpy(),
                                        global_step=global_step)
```

Affected handlers in `ignite.contrib.handlers.tensorboard_logger`:
- ~~`OptimizerParamsHandler`~~
- `WeightsScalarHandler`
- `WeightsHistHandler`
- `GradsScalarHandler`
- `GradsHistHandler`


What do you think? 
If wanted, I can work on this. 


diff --git a/tests/ignite/contrib/handlers/test_tensorboard_logger.py b/tests/ignite/contrib/handlers/test_tensorboard_logger.py
index e38e8089..70e2ba77 100644
--- a/tests/ignite/contrib/handlers/test_tensorboard_logger.py
+++ b/tests/ignite/contrib/handlers/test_tensorboard_logger.py
@@ -230,23 +230,30 @@ def test_weights_scalar_handler(dummy_model_factory):
 
     model = dummy_model_factory(with_grads=True, with_frozen_layer=False)
 
-    wrapper = WeightsScalarHandler(model)
-    mock_logger = MagicMock(spec=TensorboardLogger)
-    mock_logger.writer = MagicMock()
+    # define test wrapper to test with and without optional tag
+    def _test(tag=None):
+        wrapper = WeightsScalarHandler(model, tag=tag)
+        mock_logger = MagicMock(spec=TensorboardLogger)
+        mock_logger.writer = MagicMock()
 
-    mock_engine = MagicMock()
-    mock_engine.state = State()
-    mock_engine.state.epoch = 5
+        mock_engine = MagicMock()
+        mock_engine.state = State()
+        mock_engine.state.epoch = 5
 
-    wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
+        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("weights_norm/fc1/weight", 0.0, 5),
-        call("weights_norm/fc1/bias", 0.0, 5),
-        call("weights_norm/fc2/weight", 12.0, 5),
-        call("weights_norm/fc2/bias", math.sqrt(12.0), 5),
-    ], any_order=True)
+        tag_prefix = "{}/".format(tag) if tag else ""
+
+        assert mock_logger.writer.add_scalar.call_count == 4
+        mock_logger.writer.add_scalar.assert_has_calls([
+            call(tag_prefix + "weights_norm/fc1/weight", 0.0, 5),
+            call(tag_prefix + "weights_norm/fc1/bias", 0.0, 5),
+            call(tag_prefix + "weights_norm/fc2/weight", 12.0, 5),
+            call(tag_prefix + "weights_norm/fc2/bias", math.sqrt(12.0), 5),
+        ], any_order=True)
+
+    _test()
+    _test(tag="tag")
 
 
 def test_weights_scalar_handler_frozen_layers(dummy_model_factory):
@@ -294,23 +301,30 @@ def test_weights_hist_handler(dummy_model_factory):
 
     model = dummy_model_factory(with_grads=True, with_frozen_layer=False)
 
-    wrapper = WeightsHistHandler(model)
-    mock_logger = MagicMock(spec=TensorboardLogger)
-    mock_logger.writer = MagicMock()
+    # define test wrapper to test with and without optional tag
+    def _test(tag=None):
+        wrapper = WeightsHistHandler(model, tag=tag)
+        mock_logger = MagicMock(spec=TensorboardLogger)
+        mock_logger.writer = MagicMock()
 
-    mock_engine = MagicMock()
-    mock_engine.state = State()
-    mock_engine.state.epoch = 5
+        mock_engine = MagicMock()
+        mock_engine.state = State()
+        mock_engine.state.epoch = 5
 
-    wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
+        wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
 
-    assert mock_logger.writer.add_histogram.call_count == 4
-    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),
-        call(tag="weights/fc2/weight", values=ANY, global_step=5),
-        call(tag="weights/fc2/bias", values=ANY, global_step=5),
-    ], any_order=True)
+        tag_prefix = "{}/".format(tag) if tag else ""
+
+        assert mock_logger.writer.add_histogram.call_count == 4
+        mock_logger.writer.add_histogram.assert_has_calls([
+            call(tag=tag_prefix + "weights/fc1/weight", values=ANY, global_step=5),
+            call(tag=tag_prefix + "weights/fc1/bias", values=ANY, global_step=5),
+            call(tag=tag_prefix + "weights/fc2/weight", values=ANY, global_step=5),
+            call(tag=tag_prefix + "weights/fc2/bias", values=ANY, global_step=5),
+        ], any_order=True)
+
+    _test()
+    _test(tag="tag")
 
 
 def test_weights_hist_handler_frozen_layers(dummy_model_factory):
@@ -359,25 +373,32 @@ def test_grads_scalar_handler_wrong_setup():
 def test_grads_scalar_handler(dummy_model_factory, norm_mock):
     model = dummy_model_factory(with_grads=True, with_frozen_layer=False)
 
-    wrapper = GradsScalarHandler(model, reduction=norm_mock)
-    mock_logger = MagicMock(spec=TensorboardLogger)
-    mock_logger.writer = MagicMock()
+    # define test wrapper to test with and without optional tag
+    def _test(tag=None):
+        wrapper = GradsScalarHandler(model, reduction=norm_mock, tag=tag)
+        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()
+        mock_engine = MagicMock()
+        mock_engine.state = State()
+        mock_engine.state.epoch = 5
+        norm_mock.reset_mock()
 
-    wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
+        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
+        tag_prefix = "{}/".format(tag) if tag else ""
+
+        mock_logger.writer.add_scalar.assert_has_calls([
+            call(tag_prefix + "grads_norm/fc1/weight", ANY, 5),
+            call(tag_prefix + "grads_norm/fc1/bias", ANY, 5),
+            call(tag_prefix + "grads_norm/fc2/weight", ANY, 5),
+            call(tag_prefix + "grads_norm/fc2/bias", ANY, 5),
+        ], any_order=True)
+        assert mock_logger.writer.add_scalar.call_count == 4
+        assert norm_mock.call_count == 4
+
+    _test()
+    _test(tag="tag")
 
 
 def test_grads_scalar_handler_frozen_layers(dummy_model_factory, norm_mock):
@@ -424,23 +445,30 @@ def test_grads_hist_handler_wrong_setup():
 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)
-    mock_logger.writer = MagicMock()
+    # define test wrapper to test with and without optional tag
+    def _test(tag=None):
+        wrapper = GradsHistHandler(model, tag=tag)
+        mock_logger = MagicMock(spec=TensorboardLogger)
+        mock_logger.writer = MagicMock()
 
-    mock_engine = MagicMock()
-    mock_engine.state = State()
-    mock_engine.state.epoch = 5
+        mock_engine = MagicMock()
+        mock_engine.state = State()
+        mock_engine.state.epoch = 5
 
-    wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
+        wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
 
-    assert mock_logger.writer.add_histogram.call_count == 4
-    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),
-        call(tag="grads/fc2/weight", values=ANY, global_step=5),
-        call(tag="grads/fc2/bias", values=ANY, global_step=5),
-    ], any_order=True)
+        tag_prefix = "{}/".format(tag) if tag else ""
+
+        assert mock_logger.writer.add_histogram.call_count == 4
+        mock_logger.writer.add_histogram.assert_has_calls([
+            call(tag=tag_prefix + "grads/fc1/weight", values=ANY, global_step=5),
+            call(tag=tag_prefix + "grads/fc1/bias", values=ANY, global_step=5),
+            call(tag=tag_prefix + "grads/fc2/weight", values=ANY, global_step=5),
+            call(tag=tag_prefix + "grads/fc2/bias", values=ANY, global_step=5),
+        ], any_order=True)
+
+    _test()
+    _test(tag="tag")
 
 
 def test_grads_hist_frozen_layers(dummy_model_factory):
diff --git a/tests/ignite/contrib/handlers/test_visdom_logger.py b/tests/ignite/contrib/handlers/test_visdom_logger.py
index b26b6a3a..afa7f54b 100644
--- a/tests/ignite/contrib/handlers/test_visdom_logger.py
+++ b/tests/ignite/contrib/handlers/test_visdom_logger.py
@@ -390,34 +390,44 @@ def test_weights_scalar_handler():
 
     model = DummyModel()
 
-    wrapper = WeightsScalarHandler(model)
-    mock_logger = MagicMock(spec=VisdomLogger)
-    mock_logger.vis = MagicMock()
-    mock_logger.executor = _DummyExecutor()
+    # define test wrapper to test with and without optional tag
+    def _test(tag=None):
+        wrapper = WeightsScalarHandler(model, tag=tag)
+        mock_logger = MagicMock(spec=VisdomLogger)
+        mock_logger.vis = MagicMock()
+        mock_logger.executor = _DummyExecutor()
 
-    mock_engine = MagicMock()
-    mock_engine.state = State()
-    mock_engine.state.epoch = 5
-
-    wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
+        mock_engine = MagicMock()
+        mock_engine.state = State()
+        mock_engine.state.epoch = 5
 
-    assert mock_logger.vis.line.call_count == 4
-    mock_logger.vis.line.assert_has_calls([
-        call(X=[5, ], Y=[0.0, ], env=mock_logger.vis.env,
-             win=None, update=None,
-             opts=wrapper.windows["weights_norm/fc1/weight"]['opts'], name="weights_norm/fc1/weight"),
-        call(X=[5, ], Y=[0.0, ], env=mock_logger.vis.env,
-             win=None, update=None,
-             opts=wrapper.windows["weights_norm/fc1/bias"]['opts'], name="weights_norm/fc1/bias"),
-
-        call(X=[5, ], Y=[12.0, ], env=mock_logger.vis.env,
-             win=None, update=None,
-             opts=wrapper.windows["weights_norm/fc2/weight"]['opts'], name="weights_norm/fc2/weight"),
-        call(X=[5, ], Y=ANY, env=mock_logger.vis.env,
-             win=None, update=None,
-             opts=wrapper.windows["weights_norm/fc2/bias"]['opts'], name="weights_norm/fc2/bias"),
+        wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
 
-    ], any_order=True)
+        tag_prefix = "{}/".format(tag) if tag else ""
+
+        assert mock_logger.vis.line.call_count == 4
+        mock_logger.vis.line.assert_has_calls([
+            call(X=[5, ], Y=[0.0, ], env=mock_logger.vis.env,
+                 win=None, update=None,
+                 opts=wrapper.windows[tag_prefix + "weights_norm/fc1/weight"]['opts'],
+                 name=tag_prefix + "weights_norm/fc1/weight"),
+            call(X=[5, ], Y=[0.0, ], env=mock_logger.vis.env,
+                 win=None, update=None,
+                 opts=wrapper.windows[tag_prefix + "weights_norm/fc1/bias"]['opts'],
+                 name=tag_prefix + "weights_norm/fc1/bias"),
+
+            call(X=[5, ], Y=[12.0, ], env=mock_logger.vis.env,
+                 win=None, update=None,
+                 opts=wrapper.windows[tag_prefix + "weights_norm/fc2/weight"]['opts'],
+                 name=tag_prefix + "weights_norm/fc2/weight"),
+            call(X=[5, ], Y=ANY, env=mock_logger.vis.env,
+                 win=None, update=None,
+                 opts=wrapper.windows[tag_prefix + "weights_norm/fc2/bias"]['opts'],
+                 name=tag_prefix + "weights_norm/fc2/bias"),
+        ], any_order=True)
+
+    _test()
+    _test(tag="tag")
 
 
 def test_weights_scalar_handler_custom_reduction():
@@ -502,35 +512,44 @@ def test_grads_scalar_handler():
     def norm(x):
         return 0.0
 
-    wrapper = GradsScalarHandler(model, reduction=norm)
-    mock_logger = MagicMock(spec=VisdomLogger)
-    mock_logger.vis = MagicMock()
-    mock_logger.executor = _DummyExecutor()
+    # define test wrapper to test with and without optional tag
+    def _test(tag=None):
+        wrapper = GradsScalarHandler(model, reduction=norm, tag=tag)
+        mock_logger = MagicMock(spec=VisdomLogger)
+        mock_logger.vis = MagicMock()
+        mock_logger.executor = _DummyExecutor()
 
-    mock_engine = MagicMock()
-    mock_engine.state = State()
-    mock_engine.state.epoch = 5
-
-    wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
+        mock_engine = MagicMock()
+        mock_engine.state = State()
+        mock_engine.state.epoch = 5
 
-    assert mock_logger.vis.line.call_count == 4
-
-    mock_logger.vis.line.assert_has_calls([
-        call(X=[5, ], Y=ANY, env=mock_logger.vis.env,
-             win=None, update=None,
-             opts=wrapper.windows["grads_norm/fc1/weight"]['opts'], name="grads_norm/fc1/weight"),
-        call(X=[5, ], Y=ANY, env=mock_logger.vis.env,
-             win=None, update=None,
-             opts=wrapper.windows["grads_norm/fc1/bias"]['opts'], name="grads_norm/fc1/bias"),
-
-        call(X=[5, ], Y=ANY, env=mock_logger.vis.env,
-             win=None, update=None,
-             opts=wrapper.windows["grads_norm/fc2/weight"]['opts'], name="grads_norm/fc2/weight"),
-        call(X=[5, ], Y=ANY, env=mock_logger.vis.env,
-             win=None, update=None,
-             opts=wrapper.windows["grads_norm/fc2/bias"]['opts'], name="grads_norm/fc2/bias"),
+        wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
 
-    ], any_order=True)
+        tag_prefix = "{}/".format(tag) if tag else ""
+
+        assert mock_logger.vis.line.call_count == 4
+        mock_logger.vis.line.assert_has_calls([
+            call(X=[5, ], Y=ANY, env=mock_logger.vis.env,
+                 win=None, update=None,
+                 opts=wrapper.windows[tag_prefix + "grads_norm/fc1/weight"]['opts'],
+                 name=tag_prefix + "grads_norm/fc1/weight"),
+            call(X=[5, ], Y=ANY, env=mock_logger.vis.env,
+                 win=None, update=None,
+                 opts=wrapper.windows[tag_prefix + "grads_norm/fc1/bias"]['opts'],
+                 name=tag_prefix + "grads_norm/fc1/bias"),
+
+            call(X=[5, ], Y=ANY, env=mock_logger.vis.env,
+                 win=None, update=None,
+                 opts=wrapper.windows[tag_prefix + "grads_norm/fc2/weight"]['opts'],
+                 name=tag_prefix + "grads_norm/fc2/weight"),
+            call(X=[5, ], Y=ANY, env=mock_logger.vis.env,
+                 win=None, update=None,
+                 opts=wrapper.windows[tag_prefix + "grads_norm/fc2/bias"]['opts'],
+                 name=tag_prefix + "grads_norm/fc2/bias"),
+        ], any_order=True)
+
+    _test()
+    _test(tag="tag")
 
 
 def test_integration_no_server():

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@024667f9265610e718a6a974e7c4f6b532ea6fd3#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 tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_weights_hist_handler tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_grads_scalar_handler tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_grads_hist_handler tests/ignite/contrib/handlers/test_visdom_logger.py::test_weights_scalar_handler tests/ignite/contrib/handlers/test_visdom_logger.py::test_grads_scalar_handler
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_output_handler_with_wrong_global_step_transform_output tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_output_handler_with_global_step_transform 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_frozen_layers 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_frozen_layers 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_frozen_layers 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_frozen_layers 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 tests/ignite/contrib/handlers/test_tensorboard_logger.py::test_init_typeerror_exception tests/ignite/contrib/handlers/test_visdom_logger.py::test_optimizer_params_handler_wrong_setup tests/ignite/contrib/handlers/test_visdom_logger.py::test_optimizer_params tests/ignite/contrib/handlers/test_visdom_logger.py::test_output_handler_with_wrong_logger_type tests/ignite/contrib/handlers/test_visdom_logger.py::test_output_handler_output_transform tests/ignite/contrib/handlers/test_visdom_logger.py::test_output_handler_metric_names tests/ignite/contrib/handlers/test_visdom_logger.py::test_output_handler_both tests/ignite/contrib/handlers/test_visdom_logger.py::test_output_handler_with_wrong_global_step_transform_output tests/ignite/contrib/handlers/test_visdom_logger.py::test_output_handler_with_global_step_transform tests/ignite/contrib/handlers/test_visdom_logger.py::test_weights_scalar_handler_wrong_setup tests/ignite/contrib/handlers/test_visdom_logger.py::test_weights_scalar_handler_custom_reduction tests/ignite/contrib/handlers/test_visdom_logger.py::test_grads_scalar_handler_wrong_setup tests/ignite/contrib/handlers/test_visdom_logger.py::test_integration_no_server tests/ignite/contrib/handlers/test_visdom_logger.py::test_no_visdom
ignite/contrib/handlers/base_logger.py ignite/contrib/handlers/tensorboard_logger.py ignite/contrib/handlers/visdom_logger.py
ignite/contrib/handlers/base_logger.py ignite/contrib/handlers/tensorboard_logger.py ignite/contrib/handlers/visdom_logger.py