* the least significant bit zero is chosen. (In such cases, both of the nearest representable
   * values are even integers; this method returns the one that is a multiple of a greater power of
   * two.)
   *
   * @throws ArithmeticException if {@code mode} is {@link RoundingMode#UNNECESSARY} and {@code x}
   *     is not precisely representable as a {@code double}
   * @since 30.0
   */

        assertDoesNotThrow(() -> transaction.writeParameterWordsWireFormat(dst, 0));
    }

    @Test
    @DisplayName("Test multiple setup count handling")
    void testMultipleSetupCount() {
        byte[] dst = new byte[256];

        // Set multiple setup words
        transaction.setSetupCount(3);
        transaction.setSetupWireFormatReturn(6); // 3 * 2 bytes

        // Execute the method

        String unicodePath = "文件夹\\文件.txt";
        request = new Smb2CreateRequest(mockConfig, unicodePath);
        assertEquals("\\文件夹\\文件.txt", request.getPath());
    }

    @Test
    @DisplayName("Test multiple path operations")
    void testMultiplePathOperations() {
        request = new Smb2CreateRequest(mockConfig, "initial\\path.txt");
        assertEquals("\\initial\\path.txt", request.getPath());

	"github.com/minio/minio/internal/ioutil"
)

func md5Header(data []byte) map[string]string {
	return map[string]string{"etag": getMD5Hash(data)}
}

// Wrapper for calling PutObject tests for both Erasure multiple disks and single node setup.
func TestObjectAPIPutObjectSingle(t *testing.T) {
	ExecExtendedObjectLayerTest(t, testObjectAPIPutObject)
}

// Tests validate correctness of PutObject.

in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs...

PersistentVolumeClaim.
  * Run multiple schedulers in parallel, e.g. one or more custom schedulers
alongside the default Kubernetes scheduler, using pod annotations to select
among the schedulers for each pod. Documentation is [here](http://kubernetes.io/docs/admin/multiple-schedulers.md), design doc is [here](docs/proposals/multiple-schedulers.md).

        assertEquals("test_stackTrace", stackTrace[0].getMethodName());
        assertEquals(this.getClass().getName(), stackTrace[0].getClassName());
    }

    public void test_multipleCatches() {
        // Test with multiple catch blocks
        boolean caughtSpecific = false;
        boolean caughtRuntime = false;

        try {
            throw new DictionaryExpiredException();
        } catch (DictionaryExpiredException e) {

            assertEquals("User is not found: testuser", e.getCause().getMessage());
        }
    }

    public void test_multipleInstances() {
        // Test that multiple instances are independent
        FessUserNotFoundException exception1 = new FessUserNotFoundException("user1");
        FessUserNotFoundException exception2 = new FessUserNotFoundException("user2");

        ApiResult result = errorResponse.result();

        assertNotNull(result);
        // Cannot directly access protected fields, just verify the result is not null
    }

    // Test multiple configurations
    public void test_multipleConfigurations() {
        // Test with exception included = true
        ComponentUtil.setFessConfig(new FessConfig.SimpleImpl() {

        assertTrue(result.contains(expectedHashCode));
    }

    // Test toString() format consistency
    public void test_toString_formatConsistency() {
        // Create multiple instances and verify consistent format
        FessUserTimeZoneProcessProvider provider1 = new FessUserTimeZoneProcessProvider();
        FessUserTimeZoneProcessProvider provider2 = new FessUserTimeZoneProcessProvider();