# target inside "tensorflow/tools/toolchains/cross_compile/cc" so that Bazel
# can register it as an input to compile/link actions and send it to the remote
# VMs when needed.
# TODO(b/316932689): Avoid copying and replace with a local repository rule.
if [[ "$TFCI_MACOS_CROSS_COMPILE_ENABLE" == 1 ]]; then
  mkdir -p "${TFCI_MACOS_CROSS_COMPILE_SDK_DEST}/usr"
  mkdir -p "${TFCI_MACOS_CROSS_COMPILE_SDK_DEST}/System/Library/Frameworks/"

  // scheduled on that same threadpool, causing a deadlock in cases where the
  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a
  // CPU tensor to GPU).
  // Setting a larger thread pool does not help with the Swift caller, as we use
  // a different TFE context for each thread of execution (for running graph

        all transformations.
    *   The optimization `parallel_batch` now becomes default if not disabled by
        users, which will parallelize copying of batch elements.
    *   Added the ability for `TensorSliceDataset` to identify and handle inputs
        that are files. This enables creating hermetic SavedModels when using
        datasets created from files.

# Build the project
mvn clean install

# Run tests
mvn test

# Format code before committing
mvn formatter:format
mvn license:format
```

### Code Style

- Follow Java coding conventions
- Use proper JavaDoc comments for public APIs
- Include unit tests for new functionality
- Ensure all tests pass before submitting PR

## License

// Add to your logging configuration
logger.debug.org.codelibs.fess.suggest=DEBUG
```

## Contributing

1. Fork the repository
2. Create a feature branch: `git checkout -b feature/amazing-feature`
3. Follow coding standards: `mvn formatter:format`
4. Add tests for new functionality
5. Ensure all tests pass: `mvn test`
6. Check license headers: `mvn license:format`
7. Commit changes: `git commit -m 'Add amazing feature'`