This commit simplifies the SummaryGraph API,
by removing from the client to burden to handle 
the differences between models with/without 
DataParallel layers.

DataParallel layers in PyTorch change the fully-qualified
names (FQNs) of PyTorch modules.  A module's FQN
unambiguously identifies a module within a model, by 
encoding the path to the module from the root of the 
model.  For example, ```module.layer2.1.conv1``` and 
```module.layer2.0.conv1``` are FQNs of two different
modules named ```conv1``` in some module.  
Because a module's FQN reflects the module's hierarchy,
adding/removing a DataParallel node also changes its FQN.

Distiller uses FQNs to refer to modules and parameters 
(e.g. from YAML files), and non-functional changes to the 
model hierarchy, such as using DataParallel modules are
handled by converting FQNs using `
``utils.{de,}normalize_module_name()```.

Before this commit, the SummaryGraph API assumed that 
the API client will convert layers names using 
```utils.normalize_module_name()``` before invoking the API.
This led to needlessly verbose client code, which was also
error-prone and harder to read and maintain.
This commit fixes these short-comings by relaxing the API, 
and handling the FQNN naming differences internally.

The thinning implementation is simplified somewhat
by refactoring to the new APIs lenient requirements.

Added named_params_layers method to SummaryGraph
that yields a 3-tuple of: layer name, param name, and param.
When using the new method, summary graph communicates the
true layer name in respect to the model it was initiated with.