to the actual TensorFlow wheel.
3. Creates a `requirements_lock_<python_version>.txt` file.
4. Updates the `requirements_lock_<python_version>.txt` file using
a Bazel command.
5. Moves the updated `requirements_lock_<python_version>.txt` file
to the `../wheel_test/` directory.


### How it Works in the Presubmit Job

`_requirements_lock` files will be generated by the presubmit job. A detailed

    // is okay, as stat requests are typically fast, and concurrent requests are
    // often for the same file. Future work can split this up into one lock per
    // key if this proves to be a significant performance bottleneck.
    absl::MutexLock lock(&mu_);
    if (LookupLocked(key, value)) {
      return TF_SetStatus(status, TF_OK, "");
    }
    compute_func(key, value, status);

TensorFlow coding style.

#### General guidelines and philosophy for contribution

*   Include unit tests when you contribute new features, as they help to a)
    prove that your code works correctly, and b) guard against future breaking
    changes to lower the maintenance cost.
*   Bug fixes also generally require unit tests, because the presence of bugs

        the already-constructed model instead.
    *   Code that requires very tricky shape manipulation via converted op
        layers in order to work, where the Keras symbolic shape inference proves
        insufficient.
    *   Code that tries manually walking a `tf.keras.Model` layer by layer and
        assumes layers only ever have one positional argument. This assumption