[PyTorch] Fix module types (LazyLinear -> Linear)

[atgctg]

Description of changes:

In 6.3 Parameter Initialization, surprisingly LazyLinear modules have the type <class 'torch.nn.modules.linear.Linear'> during init, so the in-place updates in the PyTorch examples are skipped.

This PR fixes this by making the if statements check for the correct nn.Linear type.

Not applied:

def init_constant(module):
    if type(module) == nn.LazyLinear: # this is False
        nn.init.constant_(module.weight, 1)
        nn.init.zeros_(module.bias)
net.apply(init_constant)
net[0].weight.data[0], net[0].bias.data[0]

(tensor([ 0.1594,  0.9592, -1.4913,  0.8780]), tensor(0.))

Applied:

def init_constant(module):
    if type(module) == nn.Linear: # this is True
        nn.init.constant_(module.weight, 1)
        nn.init.zeros_(module.bias)
net.apply(init_constant)
net[0].weight.data[0], net[0].bias.data[0]

(tensor([1., 1., 1., 1.]), tensor(0.))

---

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[d2l-bot]

Job d2l-en/PR-2225/1 is complete.
Check the results at http://preview.d2l.ai/d2l-en/PR-2225/

[d2l-bot]

Job d2l-en/PR-2225/2 is complete.
Check the results at http://preview.d2l.ai/d2l-en/PR-2225/

[d2l-bot]

Job d2l-en/PR-2225/3 is complete.
Check the results at http://preview.d2l.ai/d2l-en/PR-2225/

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Job d2l-en/PR-2225/4 is complete.
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[AnirudhDagar]

Hi @atgctg thanks for the PR. Great catch with this bug, which was overlooked in #2038. It is actually not surprising for the lazy layers to be torch.nn.modules.linear.Linear type, since PyTorch devs designed the lazy modules intending to convert them to their non-lazy analogue after the first dry run (param init). See docs.

[astonzhang]

@atgctg thanks. Could you send another PR to replace your github id with your name in our acknowledgement:
https://github.com/d2l-ai/d2l-en/blob/master/chapter_preface/index.md