// We only get here if defining a macro inside a macro.
	// This imperfect implementation means we cannot tell the difference between
	//	#define A #define B(x) x
	// and
	//	#define A #define B (x) x
	// The first definition of B has an argument, the second doesn't. Because we let
	// text/scanner strip the blanks for us, this is extremely rare, hard to fix, and not worth it.
	return s.pos
}

 */
@ExtendWith(MockitoExtension.class)
class NetbiosAddressTest {

    @Test
    void testConstantValues() {
        // Test that constants are properly defined
        assertEquals("*\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000",
                NbtAddress.ANY_HOSTS_NAME);

		lines(
			"#ifdef A",
			"#ifdef B",
			"#define C 1234",
			"#else",
			"#define C 5678",
			"#endif",
			"#endif",
			"C",
		),
		"C.\n",
	},
	{
		"nested #define",
		lines(
			"#define A #define B THIS",
			"A",
			"B",
		),
		"THIS.\n",
	},
	{
		"nested #define with args",
		lines(
			"#define A #define B(x) x",
			"A",
			"B(THIS)",
		),

- The RuntimeClass API has been added. This feature is in alpha, and the RuntimeClass feature gate must be enabled in order to use it. The RuntimeClass API resource defines different classes of runtimes that may be used to run containers in the cluster. Pods can select a RuntimeClass to use via the RuntimeClassName field. ([#67737](https://github.com/kubernetes/kubernetes/pull/67737), [@tallclair](https://github.com/tallclair))...

    // According to the degree sum formula, this is equal to twice the number of edges.
    checkState((degreeSum & 1) == 0);
    return degreeSum >>> 1;
  }

  /**
   * An implementation of {@link BaseGraph#edges()} defined in terms of {@link Graph#nodes()} and
   * {@link #successors(Object)}.
   */
  @Override
  public Set<EndpointPair<N>> edges() {
    return new AbstractSet<EndpointPair<N>>() {
      @Override

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel { #define-a-submodel }

For example, we can define an `Image` model:

{* ../../docs_src/body_nested_models/tutorial004_py310.py hl[7:9] *}

### Use the submodel as a type { #use-the-submodel-as-a-type }

  ASSERT_EQ(TF_OK, code) << status_msg;
  TF_DeletePluggableDeviceLibraryHandle(lib);
#endif  // !defined(TENSORFLOW_NO_SHARED_OBJECTS)
#endif  // !defined(PLATFORM_WINDOWS)
}

TEST(CAPI_EXPERIMENTAL, LibraryNextPluggableDeviceLoadFunctions) {
  // TODO(penpornk): Enable this test on Windows.
#if !defined(PLATFORM_WINDOWS)
#if !defined(TENSORFLOW_NO_SHARED_OBJECTS)
  // Load the library.
  TF_Status* status = TF_NewStatus();

	switch word {
	case "BC", "BCL", "BEQ", "BGE", "BGT", "BL", "BLE", "BLT", "BNE", "BR", "BVC", "BVS", "BDNZ", "BDZ", "CALL", "JMP":
		return true
	}
	return false
}

// IsPPC64CMP reports whether the op (as defined by an ppc64.A* constant) is
// one of the CMP instructions that require special handling.
func IsPPC64CMP(op obj.As) bool {
	switch op {

## Configuring AD/LDAP on MinIO

            if it should exclude from the output the fields that had the same default
            value, even when they were explicitly set.
            """
        ),
    ] = False,
    exclude_none: Annotated[
        bool,
        Doc(
            """
            Pydantic's `exclude_none` parameter, passed to Pydantic models to define