about 'fake 2D shape'

Hello,

It's an impressive work. I've noticed that in modeling/blocks.py line 140-143:
```python
# fake 2D shape
latent_tokens = latent_tokens.reshape(batch_size, self.width, self.num_latent_tokens, 1)
latent_tokens = self.conv_out(latent_tokens)
latent_tokens = latent_tokens.reshape(batch_size, self.token_size, 1, self.num_latent_tokens)
```
It's wired why organize 32 tokens in this way. Looks like you extract elements `[0, :, : : 32]` in latent_tokens with init shape [B, 32, 1024] (L-32 model) as the channel dim in output tokens. Why not use `permute` instead? Hope for an answer.
Thanks!

Start with modeling/blocks.py lines 140-143 and trace the shape of latent_tokens before and after each reshape and conv_out call. Compare that flow with the proposed permute approach and document the reason for the current layout, including whether the two operations are behaviorally equivalent.

Written by the indexing model from the issue text.