Updated documentation regarding DistributedEmbedding batch size.

[hertschuh]

The batch size represented by the first dimension of input_shape and output_shape in FeatureConfig is the global batch size.

[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 focuses on improving the clarity and accuracy of documentation related to batch size interpretation within the DistributedEmbedding layer's FeatureConfig. The changes ensure that users understand that the input_shape and output_shape parameters refer to the global batch size in a distributed context, and updates corresponding code examples to align with this clarification.

Highlights

• Documentation Clarity: Clarified the documentation for FeatureConfig within DistributedEmbedding, specifically noting that the first dimension of input_shape and output_shape represents the global batch size. This is particularly important for distributed training environments, such as those utilizing TPUs, where the global batch size refers to the total number of samples dispatched across all devices.
• Code Example Alignment: Updated an internal code example in base_distributed_embedding.py to reflect the global batch size concept. The FeatureConfig instances in the example now explicitly use GLOBAL_BATCH_SIZE instead of PER_REPLICA_BATCH_SIZE for their input_shape and output_shape parameters, ensuring consistency with the clarified documentation.

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[gemini-code-assist]

Code Review

This pull request updates the documentation for DistributedEmbedding to clarify that the batch size in FeatureConfig refers to the global batch size, not the per-replica batch size. The changes include updating the docstring in FeatureConfig and an example in DistributedEmbedding.

[abheesht17]

Thanks!