Nightly test fix: add filter to warnings

docs/tutorials/gluon/hybrid.md

@@ -157,6 +157,7 @@ to gluon with `SymbolBlock`:
 import warnings
 
 with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
     net2 = gluon.SymbolBlock.imports('model-symbol.json', ['data'], 'model-0001.params')
 ```
 

docs/tutorials/gluon/save_load_params.md

@@ -262,6 +262,7 @@ Serialized Hybrid networks (saved as .JSON and .params file) can be loaded and u
 ```python
 import warnings
 with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
     deserialized_net = gluon.nn.SymbolBlock.imports("lenet-symbol.json", ['data'], "lenet-0001.params", ctx=ctx)
 ```
 

docs/tutorials/onnx/fine_tuning_gluon.md

@@ -281,6 +281,7 @@ We create a symbol block that is going to hold all our pre-trained layers, and a
 ```python
 import warnings
 with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
     pre_trained = gluon.nn.SymbolBlock(outputs=new_sym, inputs=mx.sym.var('data_0'))
 net_params = pre_trained.collect_params()
 for param in new_arg_params:

docs/tutorials/onnx/inference_on_onnx_model.md

@@ -146,6 +146,7 @@ And load them into a MXNet Gluon symbol block.
 ```python
 import warnings
 with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
     net = gluon.nn.SymbolBlock(outputs=sym, inputs=mx.sym.var('data_0'))
 net_params = net.collect_params()
 for param in arg_params:

docs/tutorials/python/module_to_gluon.md

@@ -334,7 +334,10 @@ print("Output probabilities: {}".format(prob))
 For the Gluon API, it is a lot simpler. You can just load a serialized model in a [`SymbolBlock`](https://mxnet.incubator.apache.org/api/python/gluon/gluon.html?highlight=symbolblo#mxnet.gluon.SymbolBlock) and run inference directly.
 
 ```python
-net = gluon.SymbolBlock.imports('module-model-symbol.json', ['data', 'softmax_label'], 'module-model-0005.params')
+import warnings
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    net = gluon.SymbolBlock.imports('module-model-symbol.json', ['data', 'softmax_label'], 'module-model-0005.params')
 prob = net(mx.nd.ones((1,1,28,28)), mx.nd.ones(1)) # note the second argument here to account for the softmax_label symbol
 print("Output probabilities: {}".format(prob.asnumpy()))
 ```