)


# Taken from Pydantic v1 as is
def isoformat(o: Union[datetime.date, datetime.time]) -> str:
    return o.isoformat()


# Adapted from Pydantic v1
# TODO: pv2 should this return strings instead?
def decimal_encoder(dec_value: Decimal) -> Union[int, float]:
    """
    Encodes a Decimal as int if there's no exponent, otherwise float

    This is useful when we use ConstrainedDecimal to represent Numeric(x,0)

	}
}

func testDualWriteLock(t *testing.T, duration time.Duration) (locked bool) {
	ctx := t.Context()
	lrwm := NewLRWMutex()

	// fmt.Println("Getting initial write lock")
	if !lrwm.GetLock(ctx, "", "", time.Second) {
		panic("Failed to acquire initial write lock")
	}

	go func() {
		time.Sleep(2 * time.Second)
		lrwm.Unlock()

	// It MUST contain the OAuth 2.0 client_id of the Relying Party
	// as an audience value. It MAY also contain identifiers for
	// other audiences. In the general case, the aud value is an
	// array of case sensitive strings. In the common special case
	// when there is one audience, the aud value MAY be a single
	// case sensitive
	audValues, ok := policy.GetValuesFromClaims(mclaims, audClaim)
	if !ok {

// corresponding entry in the `dims` array must be -1.
//
// This does not overwrite the existing shape associated with `output`,
// but merges the input shape with the existing shape.  For example,
// setting a shape of [-1, 2] with an existing shape [2, -1] would set
// a final shape of [2, 2] based on shape merging semantics.
//
// Returns an error into `status` if:
//   * `output` is not in `graph`.

// String returns the contents of the unread portion of the buffer
// as a string. If the [Buffer] is a nil pointer, it returns "<nil>".
//
// To build strings more efficiently, see the [strings.Builder] type.
func (b *Buffer) String() string {
	if b == nil {
		// Special case, useful in debugging.
		return "<nil>"
	}
	return string(b.buf[b.off:])
}

        countConfig.setValue("456");
        optimizedHelper.setProperty(bean, "count", Integer.class, countConfig);
    }

    /**
     * Benchmark that tests cache benefits by repeatedly setting properties on the same class.
     * This better demonstrates the caching improvements.
     */
    @Benchmark
    public void benchmarkOriginalHelperRepeatedOperations() throws Exception {

    }
}

fun buildToolGradleParameters(
    isContinue: Boolean = true,
    maxParallelForks: String = "%maxParallelForks%",
): List<String> =
    listOf(
        // We pass the 'maxParallelForks' setting as 'workers.max' to limit the maximum number of executers even
        // if multiple test tasks run in parallel. We also pass it to the Gradle build as a maximum (maxParallelForks)

just getting started, GitHub has a
[how-to](https://help.github.com/articles/using-pull-requests/).

TensorFlow team members will be assigned to review your pull requests. Once the
pull requests are approved and pass continuous integration checks, a TensorFlow
team member will apply `ready to pull` label to your change. This means we are

                warnings.simplefilter(
                    "ignore", category=UnsupportedFieldAttributeWarning
                )
            # TODO: remove after dropping support for Python 3.8 and
            # setting the min Pydantic to v2.12.3 that adds asdict()
            field_dict = asdict(self.field_info)
            annotated_args = (
                field_dict["annotation"],
                *field_dict["metadata"],

internal
fun Project.createGenerateAutoTestedSamplesTestTask(sourceSet: SourceSet, testType: TestType) {
    val prefix = testType.prefix
    val sourceSets = the<SourceSetContainer>()
    val main by sourceSets.getting
    val sourceSet = sourceSets.getByName("${prefix}Test")

    val groovySourceDir = sourceSet.extensions.findByType<GroovySourceDirectorySet>()