Ensures consistent serialization for Functional model

[JyotinderSingh]

Related issues: #21328
Previous fix: #20993

Original Bug

• Models created with nested list inputs (e.g., inputs=[[input_a]]) would have their structure flattened after a serialization round-trip.
• A previous fix for this issue removed an un-nesting step during deserialization. While it fixed the nested-input case, it introduced a regression where a standard single-input model (inputs=input_a) would have its model.input type change from KerasTensor to list upon loading.

The root cause was that the serialization format was ambiguous. It represented both a single tensor and a list containing one tensor identically.

Fix

This change makes the serialization format unambiguous. The logic in get_config is updated to:

1. Serialize a single KerasTensor input/output as a direct pointer.
2. Serialize a list, tuple, or dict input/output as a corresponding structure containing pointers.
3. The deserializer can now perfectly reconstruct the original I/O structure without naive un-nesting logic, as the format is no longer lossy.

[codecov-commenter]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 82.68%. Comparing base (3d2db56) to head (de15418).

Additional details and impacted files

@@            Coverage Diff             @@
##           master   #21341      +/-   ##
==========================================
- Coverage   82.68%   82.68%   -0.01%     
==========================================
  Files         565      565              
  Lines       54825    54819       -6     
  Branches     8515     8512       -3     
==========================================
- Hits        45333    45327       -6     
  Misses       7406     7406              
  Partials     2086     2086              

Flag	Coverage Δ
keras	82.49% <ø> (-0.01%)	⬇️
keras-jax	63.63% <ø> (-0.01%)	⬇️
keras-numpy	58.78% <ø> (+<0.01%)	⬆️
keras-openvino	33.13% <ø> (-0.01%)	⬇️
keras-tensorflow	64.02% <ø> (-0.01%)	⬇️
keras-torch	63.67% <ø> (-0.01%)	⬇️

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[hertschuh]

Thanks for the fix!