[TorchScript] Fix Intro to TorchScript tutorial

[SplitInfinity]

Summary
There is a section of the Intro to TorchScript tutorial that is supposed
to illustrate that tracing cannot capture control flow but scripting can.
However, the generated IR for both cases is the same because the tutorial
only prints out the IR for the outer module, not the inner module that
contains the control flow. This commit fixes this issue by printing the IR
for both the inner and outer module to clearly show that tracing does not
capture control flow, but scripting does.

Testing
Ran the tutorial file.

Tracing Output:

def forward(self,
    argument_1: Tensor) -> None:
  return None

def forward(self,
    input: Tensor,
    h: Tensor) -> Tuple[Tensor, Tensor]:
  _0 = self.dg
  _1 = (self.linear).forward(input, )
  _2 = (_0).forward(_1, )
  _3 = torch.tanh(torch.add(_1, h, alpha=1))
  return (_3, _3)

Scripting Output:

def forward(self,
    x: Tensor) -> Tensor:
  _0 = bool(torch.gt(torch.sum(x, dtype=None), 0))
  if _0:
    _1 = x
  else:
    _1 = torch.neg(x)
  return _1

def forward(self,
    x: Tensor,
    h: Tensor) -> Tuple[Tensor, Tensor]:
  _0 = (self.dg).forward((self.linear).forward(x, ), )
  new_h = torch.tanh(torch.add(_0, h, alpha=1))
  return (new_h, new_h)

[SplitInfinity]

cc: Tutorial authors @jamesr66a @suo

For some reason GitHub won't let me add reviewers

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[indigoviolet]

lgtm, one comment inline. Thanks for fixing this!

[indigoviolet]

Suggestion: Add some text here and similarly below, to say which forward is for the decision gate and which is for the cell