Fix for batch size of FeatureConfig.output_shape on TensorFlow.

[hertschuh]

The TensorFlow's FeatureConfig.output_shape expects the batch size to be per replica. However, keras_rs use the global batch size in input_shape and output_shape. This modifies the conversion code to take this into account.

[gemini-code-assist]

Summary of Changes

Hello @hertschuh, 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 an incompatibility in how batch sizes are handled when converting keras_rs FeatureConfig objects to TensorFlow's tf.tpu.experimental.embedding.FeatureConfig. Previously, keras_rs used a global batch size, while TensorFlow's TPU embedding API expects a per-replica batch size. This change modifies the conversion logic to correctly compute and pass the per-replica batch size, ensuring proper configuration for distributed training environments.

Highlights

• Batch Size Correction: The conversion logic for FeatureConfig.output_shape now correctly transforms the global batch size used by keras_rs into the per-replica batch size expected by TensorFlow's tf.tpu.experimental.embedding.FeatureConfig.
• Replicas Parameter Introduction: A new num_replicas_in_sync parameter has been introduced to the translate_keras_rs_configuration and translate_keras_rs_feature_config functions to facilitate the per-replica batch size calculation.
• Input Validation: A ValueError is now raised if the global batch size specified in FeatureConfig.output_shape is not evenly divisible by the number of replicas, preventing misconfigurations in distributed training.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point in your pull request via creating an issue comment (i.e. comment on the pull request page) using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in issue comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments or fill out our survey to provide feedback.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

The code changes introduce the ability to load multiple signed images to a registry using a bulk load feature. The changes adjust the output_shape batch size to be per-replica for TensorFlow's TPUEmbedding. I have added comments to improve the robustness of the new code by adding some input validation to prevent potential runtime errors, and a minor fix for an f-string in an error message.

[abheesht17]

Nice, LGTM!