return;
            }

            cpc.add(md);

            List<MetadataGraphEdge> exits = graph.getExcidentEdges(node);

            if (exits != null && !exits.isEmpty()) {
                MetadataGraphEdge[] sortedExits = exits.toArray(new MetadataGraphEdge[0]);
                Arrays.sort(sortedExits, (e1, e2) -> {
                    if (e1.getDepth() == e2.getDepth()) {

```sh
docker run \
  -p 9000:9000 \
  -p 9001:9001 \
  -e "MINIO_ROOT_USER=AKIAIOSFODNN7EXAMPLE" \
  -e "MINIO_ROOT_PASSWORD=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY" \

                // Perform operations with the handle
                assertNotNull(handle);
            }
            // Verify that close() was called on the handle when the block exits
            Mockito.verify(smbPipeHandle).close();
        }
    }

    @DisplayName("TestLocking increments numComplete")
    void testNumCompleteIncrement() {
        TestLocking t = new TestLocking();
        t.url = "smb://test/file.txt";
        t.numIter = 0; // Set to 0 so run() exits immediately

        // Run the method
        t.run();

        // Verify numComplete was incremented
        assertEquals(1, t.numComplete, "numComplete should be incremented after run");
    }

    @Nested

	t.msg = "hello, world"
	g = t
}

func main() {
	go setup()
	for g == nil {
	}
	print(g.msg)
}
</pre>

<p>
Even if <code>main</code> observes <code>g != nil</code> and exits its loop,
there is no guarantee that it will observe the initialized
value for <code>g.msg</code>.
</p>

<p>
In all these examples, the solution is the same:
use explicit synchronization.
</p>

- `pause`: Added a `-v` flag to the Windows variant of the pause binary, which
  prints the version of pause and exits. The Linux pause binary already has this flag. ([#125067](https://github.com/kubernetes/kubernetes/pull/125067), [@neolit123](https://github.com/neolit123))

### Failing Test

        error reporting for `tf.distribute.MultiWorkerMirroredStrategy`.
        Specifically, for the type of interruption with advance notice, it
        automatically saves a checkpoint, exits the program without raising an
        unrecoverable error, and restores the progress when training restarts.

*   `tf.math`:

    *   Added `tf.math.approx_max_k` and `tf.math.approx_min_k` which are the