Address review feedback: clarify documentation and fix type consistency

[Copilot]

Addresses review comments on tutorials 08 and 09 by correcting misleading documentation and fixing a type inconsistency bug.

Tutorial 08: Fix nnx.eval_shape documentation

• Removed inaccurate "memory efficient" claims - nnx.eval_shape creates abstract structures but parameters are materialized immediately by replace_by_pure_dict
• Updated comments at lines 121, 237, and key takeaways to reflect actual behavior

Tutorial 09: Multiple improvements

Removed non-existent pattern from documentation:

• Deleted nnx.eval_shape from key concepts and takeaways - pattern not demonstrated in tutorial code

Clarified reconstruction requirements:

• Documented that graphdef reconstruction requires exact same initialization parameters (input_dim, hidden_dim, output_dim, seed)
• Explained parameter capture vs module-level constants in JAX tracing context

Fixed type bug:

# Before: type mismatch - Python float if n_steps=0, JAX array if n_steps>0
final_loss = None  # or 0.0

# After: consistent JAX array type
final_loss = jnp.array(0.0)

Improved function naming:

• train_split_learning_for_n_steps → initialize_and_train_split_learning (performs both init and training)

Enhanced dtype workaround documentation:

• Added detailed explanation of table_to_tensor dtype constraint with alternative patterns for non-integer labels

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[da-niao-dan]

lgtm