Fixing dtype issue in siglip layer

[buildwithsuhana]

Fixing the ValueError: Incompatible type conversion requested to type int32 for `tf.Variable of type int64. which arose while running the SigLIPBackboneTest.test_backbone_basics test
Changed the dtype from int to int32

[gemini-code-assist]

Summary of Changes

Hello @buildwithsuhana, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses a critical type compatibility issue within the SigLIP layer implementation, specifically targeting a ValueError encountered during testing. The core problem stemmed from TensorFlow's expectation of int32 for certain tf.Variable operations while int64 was implicitly provided. The solution involves explicitly setting the data type of position_ids to "int32", thereby harmonizing the type expectations across different backend frameworks and ensuring the stability of the SigLIP backbone.

Highlights

• Type Compatibility Fix: Resolved a ValueError related to incompatible type conversion (int64 to int32) that occurred during the SigLIPBackboneTest.test_backbone_basics test.
• Explicit Dtype Declaration: Changed the dtype for position_ids from a generic int to an explicit "int32" within the build method of the SigLIP layers to ensure correct type handling, especially with TensorFlow.

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[gemini-code-assist]

Code Review

This pull request fixes a ValueError related to a dtype mismatch in TensorFlow by changing the dtype of position_ids from int to "int32". The change is correct and necessary to ensure backend compatibility. My review includes suggestions to update the accompanying comments, which have become outdated and misleading after this change, to maintain code clarity and consistency as per the style guide.