Add non-linearity to hidden state in char-RNN generation tutorial

[adityasharmaaaaa]

Fixes #3953

Problem

The RNN.forward() in this tutorial passed the hidden state straight
through a linear layer with no activation:

hidden = self.i2h(input_combined)

This makes h_t a linear combination of h_{t-1}, the input, and the
category tensor at every timestep — nn.LogSoftmax on the output was
the only non-linear operation anywhere in the recurrence.

Fix

Added a nn.Tanh() on the hidden path:

self.tanh = nn.Tanh()
...
hidden = self.tanh(self.i2h(input_combined))

This is a minimal, targeted fix for the specific issue raised — it
doesn't change i2o, o2o, or dropout, so the rest of the
architecture and surrounding explanation stay valid. Also added one
sentence to the "Creating the Network" section explaining why the
non-linearity is there.

Testing

Ran the tutorial locally for the full 100,000 iterations. Loss trends
down as expected (starts ~2.98, settles in the 1.4–2.4 range with
normal SGD noise), and sampled names after training look reasonable
(e.g. Rovakin, Sarana, Chan), confirming the change doesn't
break training.

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/3956

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