for _, val := range vals {
		if val != "" && !strings.EqualFold(val, "false") {
			return true
		}
	}
	return false
}

func ToStringKey(values ...interface{}) string {
	results := make([]string, len(values))

	for idx, value := range values {
		if valuer, ok := value.(driver.Valuer); ok {
			value, _ = valuer.Value()
		}

		switch v := value.(type) {
		case string:
			results[idx] = v

import gradlebuild.docs.model.SimpleClassMetaDataRepository
import org.w3c.dom.Document
import org.w3c.dom.Element
/**
 * Transforms userguide source into docbook, replacing custom XML elements.
 *
 * Takes the following as input:
 * <ul>
 * <li>A source docbook XML file.</li>

fun isRunningDocsTestTask() =
    setOf(":docs:docsTest", "docs:docsTest")
        .any(gradle.startParameter.taskNames::contains)


/**
 * Returns a new provider that takes its value from at most one
 * of the given providers. If no input provider is present, the output
 * provider will not be present. If more than one input provider
 * has a value specified, the resulting provider will throw an

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses in Nested Data Structures { #dataclasses-in-nested-data-structures }

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

        or hasattr(origin, "__pydantic_core_schema__")
        or hasattr(origin, "__get_pydantic_core_schema__")
    )


def field_annotation_is_scalar(annotation: Any) -> bool:
    # handle Ellipsis here to make tuple[int, ...] work nicely
    return annotation is Ellipsis or not field_annotation_is_complex(annotation)


def field_annotation_is_scalar_sequence(annotation: Union[type[Any], None]) -> bool:

Lifecycle transition rules can be applied to buckets (both versioned and un-versioned) by specifying the tier name defined above as the transition storage class for the lifecycle rule.

## Implementation

```

### Set object lock

PutObject API allows setting per object retention mode and retention duration using `x-amz-object-lock-mode` and `x-amz-object-lock-retain-until-date` headers. This takes precedence over any bucket object lock configuration w.r.t retention.

```sh
aws s3api put-object --bucket testbucket --key lockme --object-lock-mode GOVERNANCE --object-lock-retain-until-date "2019-11-20"  --body /etc/issue
```

Eager mode lets users write imperative-style statements that can be easily
inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

	}
	res, err := obj.NewMultipartUpload(context.Background(), "bucket", "key", opts)
	if err != nil {
		t.Fatalf("%s: <ERROR> %s", instanceType, err)
	}
	uploadID := res.UploadID

	parts := make(map[int]string)
	metadata := make(map[string]string)
	for i := 1; i <= 10; i++ {
		randomPerm := rand.Perm(10)
		randomString := ""
		for _, num := range randomPerm {
			randomString += strconv.Itoa(num)
		}

works, in source code and object code form.

b) Subject to the terms of this Agreement, each Contributor hereby grants
Recipient a non-exclusive, worldwide, royalty-free patent license under Licensed
Patents to make, use, sell, offer to sell, import and otherwise transfer the
Contribution of such Contributor, if any, in source code and object code form.
This patent license shall apply to the combination of the Contribution and