But sometimes the control-flow is actually part of the model
       itself. For instance, a recurrent network is a loop over the
       (possibly dynamic) length of an input sequence.
 * In the returned "ScriptModule", operations that have different
   behaviors in "training" and "eval" modes will always behave as
   if it is in the mode it was in during tracing, no matter which
   mode the *ScriptModule* is in.

 In cases like these, tracing would not be appropriate and
 "scripting" is a better choice. If you trace such models, you may
 silently get incorrect results on subsequent invocations of the
 model. The tracer will try to emit warnings when doing something
 that may cause an incorrect trace to be produced.

Parameters:
      func (callable or torch.nn.Module) -- A Python
      function or torch.nn.Module that will be run with
      example_inputs. func arguments and return values  must be