Remove post layer normalization from MetaCLIP2VisionEncoder

[sineeli]

The layer already present in backbone itself as per the structure.

Description of the change

Reference

Colab Notebook

Checklist

• I have added all the necessary unit tests for my change.
• I have verified that my change does not break existing code and works with all backends (TensorFlow, JAX, and PyTorch).
• My PR is based on the latest changes of the main branch (if unsure, rebase the code).
• I have followed the Keras Hub Model contribution guidelines in making these changes.
• I have followed the Keras Hub API design guidelines in making these changes.
• I have signed the Contributor License Agreement.

[gemini-code-assist]

Summary of Changes

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This pull request refines the MetaCLIP2VisionEncoder by eliminating a redundant post-layer normalization step. This adjustment ensures the model's architecture is more efficient and accurate by removing an unnecessary operation, aligning the implementation with the intended design where normalization is handled within the backbone itself.

Highlights

• Redundant Layer Removal: Removed the post_layer_norm attribute from the MetaCLIP2VisionEncoder class, as the layer was found to be redundant and already present within the backbone structure.

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Changelog

• keras_hub/src/models/metaclip_2/metaclip_2_vision_encoder.py
  • Removed the post_layer_norm attribute from the MetaCLIP2VisionEncoder class.

Activity

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Code Review

This pull request removes the post_layer_norm attribute from the MetaCLIP2VisionEncoder class. This layer was defined in the __init__ method but was not used in the model's forward pass, making it dead code. The removal is correct as the post-encoder layer normalization for the vision tower is handled within the MetaCLIP2Backbone after pooling.