*
 * @param <B> The concrete type of this builder (the 'self-type'). All the Builder methods of this
 *     class (such as {@link #named}) return this type, so that Builder methods of more derived
 *     classes can be chained onto them without casting.
 * @param <G> The type of the generator to be passed to testers in the generated test suite. An
 *     instance of G should somehow provide an instance of the class under test, plus any other

 *
 * @param <B> The concrete type of this builder (the 'self-type'). All the Builder methods of this
 *     class (such as {@link #named}) return this type, so that Builder methods of more derived
 *     classes can be chained onto them without casting.
 * @param <G> The type of the generator to be passed to testers in the generated test suite. An
 *     instance of G should somehow provide an instance of the class under test, plus any other

  }

  /**
   * This is an extraordinary test case. Here's what it's trying to simulate.
   * - 2 requests happen concurrently to a host that can be coalesced onto a single connection.
   * - Both request discover no existing connection. They both make a connection.
   * - The first request "wins the race".

	clear(config.Tiers)
	// Copy over the new tier configs
	maps.Copy(config.Tiers, newConfig.Tiers)
	config.lastRefreshedAt = UTCNow()
	return nil
}

// Save saves tier configuration onto objAPI
func (config *TierConfigMgr) Save(ctx context.Context, objAPI ObjectLayer) error {
	if objAPI == nil {
		return errServerNotInitialized
	}

	pr, opts, err := globalTierConfigMgr.configReader(ctx)

  EXPECT_TRUE(executed);
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  TFE_DeleteTensorHandle(retval);

  // Explicit placement still forces the op onto the requested device
  matmul.reset(MatMulOp(context.get(), hcustom0.get(), hcpu.get()));
  TFE_OpSetDevice(matmul.get(), "/job:localhost/replica:0/task:0/device:CPU:0",
                  status.get());

    you're running the same code that you're testing:

    *   Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/)
        `pip install -U tf-nightly`
    *   Rebase your pull request onto a recent pull from
        [TensorFlow's](https://github.com/tensorflow/tensorflow) master branch.

2.  If you are changing the code and the docstring of a class/function/method,
    then you will need to

   *
   * This role is played by storedPermitsToWaitTime(double storedPermits, double permitsToTake). The
   * underlying model is a continuous function mapping storedPermits (from 0.0 to maxStoredPermits)
   * onto the 1/rate (i.e. intervals) that is effective at the given storedPermits. "storedPermits"
   * essentially measure unused time; we spend unused time buying/storing permits. Rate is

	//	return append(b, make([]byte, n)...)
	// This avoids unnecessary zero-ing of the first len(b) bytes of the
	// allocated slice, but this pattern causes b to escape onto the heap.
	//
	// Instead use the append-make pattern with a nil slice to ensure that
	// we allocate buffers rounded up to the closest size class.
	c := len(b) + n // ensure enough space for n elements
	if c < 2*cap(b) {

    /**
     * User property for chained LRM: whether to ignore "availability check" in tail or not. Usually you do want
     * to ignore it. This property is mapped onto corresponding Resolver 2.x property, is like a synonym for it.
     * Default value: <code>true</code>.
     *
     * @since 3.9.0

  //
  // TFE_OpGetDevice(op) records the original placement of the operation. It may
  // be an empty string if no device was explicitly requested, but will
  // otherwise be the name of this custom device. Ops are placed onto a custom
  // device if any of their inputs are on that custom device, but custom devices
  // are free to set a bad status in order to require explicit placement.
  void (*execute)(const TFE_Op* op, int* num_outputs,