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

  // +optional
  optional bool automountServiceAccountToken = 21;

  // NodeName is a request to schedule this pod onto a specific node. If it is non-empty,
  // the scheduler simply schedules this pod onto that node, assuming that it fits resource
  // requirements.
  // +optional
  optional string nodeName = 10;

-		     ? (char *) memcpy (dest, src, n)			      \
-		     : strncpy (dest, src, n))				      \
-		  : strncpy (dest, src, n)))
-# endif
-#endif
-
-
 /* Append no more than N characters from SRC onto DEST.  */
 #ifndef _HAVE_STRING_ARCH_strncat
 # ifdef _USE_STRING_ARCH_strchr
@@ -780,380 +438,29 @@ __stpcpy_small (char *__dest,
 		       : (*((char *) __mempcpy (strchr (__dest, '\0'),	      \

	//	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) {

  TensorHandlePtr value_one(FloatTensorHandle(1., status.get()));
  TensorHandlePtr value_two(FloatTensorHandle(2., status.get()));
  {
    // Try to pack two TensorHandles onto a parallel device with a single
    // component.
    ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
    std::array<TFE_TensorHandle*, 2> components{value_one.get(),

  // starts by stopping at most 30% of those DaemonSet pods and then brings
  // up new DaemonSet pods in their place. Once the new pods are available,
  // it then proceeds onto other DaemonSet pods, thus ensuring that at least
  // 70% of original number of DaemonSet pods are available at all times during
  // the update.
  // +optional

     * <p>
     * Effective properties are computed from system, user and optionally project properties, layered with
     * defined precedence onto each other to achieve proper precedence. Precedence is defined as:
     * <ul>
     *     <li>System properties (lowest precedence)</li>
     *     <li>Project properties (optional)</li>

  //
  // 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,

  // starts by stopping at most 30% of those DaemonSet pods and then brings
  // up new DaemonSet pods in their place. Once the new pods are available,
  // it then proceeds onto other DaemonSet pods, thus ensuring that at least
  // 70% of original number of DaemonSet pods are available at all times during
  // the update.
  // +optional

As noted already, programmers are strongly encouraged to use appropriate synchronization
to avoid data races.
In the absence of data races, Go programs behave as if all the goroutines
were multiplexed onto a single processor.
This property is sometimes referred to as DRF-SC: data-race-free programs
execute in a sequentially consistent manner.
</p>

<p>
While programmers should write Go programs without data races,