of the contribution must be compared against the cost of maintaining the
    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in
    [tensorflow/addons](https://github.com/tensorflow/addons) to get some
    airtime before a decision is made regarding whether they are to be migrated
    to the core.

*   Add `execute_fn_for_device function` to dynamically choose the
    implementation based on underlying device placement.
*   Eager:
    *   Add `reduce_logsumexp` benchmark with experiment compile.
    *   Give `EagerTensor`s a meaningful `__array__` implementation.
    *   Add another version of defun matmul for performance analysis.
*   `tf.function`/AutoGraph:

test:release_linux_base --build_tests_only --keep_going --test_output=errors --verbose_failures=true
test:release_linux_base --local_test_jobs=HOST_CPUS
test:release_linux_base --test_env=LD_LIBRARY_PATH
# Give only the list of failed tests at the end of the log
test:release_linux_base --test_summary=short

# Use the Clang toolchain to compile
build:release_cpu_linux --config=release_linux_base