EXPECT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  }

  void BuildExpectedGraph(bool grad_inputs_provided,
                          TF_Output* expected_grad_outputs) {
    // The expected graph looks like this if grad_inputs_provided.
    // If grad_inputs_provided is false, Const_0 will be a OnesLike op.
    //      ^             ^
    //    dy|           dx|        // MatMul Gradient Graph
    //      |             |

_current_module = _sys.modules[__name__]

if not hasattr(_current_module, "__path__"):
  __path__ = [_tf_api_dir]
elif _tf_api_dir not in __path__:
  __path__.append(_tf_api_dir)

# Hook external TensorFlow modules.

# Load tensorflow-io-gcs-filesystem if enabled
if (_os.getenv("TF_USE_MODULAR_FILESYSTEM", "0") == "true" or
    _os.getenv("TF_USE_MODULAR_FILESYSTEM", "0") == "1"):

    rare occasion we may reject it.

**3. Review**

-   For a valid PR, reviewer (person familiar with the code/functionality)
    checks if the PR looks good or needs additional changes.
-   If all looks good, the reviewer will approve the PR.
-   If a change is needed, the contributor is requested to make the suggested
    change.
-   You make the change and submit it for the review again.

    *   `tf.contrib.estimator.InMemoryEvaluatorHook` and
        tf.estimator.experimental.InMemoryEvaluatorHook`.
    *   `tf.contrib.estimator.make_stop_at_checkpoint_step_hook` with
        `tf.estimator.experimental.make_stop_at_checkpoint_step_hook`.
    *   Expose `tf.distribute.Strategy as the new name for
        tf.contrib.distribute.DistributionStrategy.
    *   Migrate linear optimizer from contrib to core.

  tensorflow::unwrap(ctx)->SetThreadLocalDevicePlacementPolicy(
      static_cast<tensorflow::ContextDevicePlacementPolicy>(policy));
}

// Note: this function looks up a thread local policy. So it should be called in
// the appropriate client thread. In particular, in async mode, it may not be
// safe to call this function from the async EagerExecutor threads.

setup_file() {
    bazel version  # Start the bazel server
}

# Do a bazel query specifically for the licenses checker. It searches for
# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then
# gathers the list of all packages (i.e. directories) which contain those
# targets.
license_query() {
 bazel cquery --experimental_cc_shared_library "$1" --keep_going \

  // Builds a tensor filled with ones with the same shape and dtype as `t`.
  virtual Status BuildOnesLike(const TapeTensor& t,
                               Gradient** result) const = 0;

  // Looks up the ID of a Gradient.
  virtual int64_t TensorId(Gradient* tensor) const = 0;

  // Converts a Gradient to a TapeTensor.
  virtual TapeTensor TapeTensorFromGradient(Gradient* gradient) const = 0;