Pointer(owner.createPointer())
    }

    override fun equals(other: Any?): Boolean = other is KtFe10DynamicFunctionDescValueParameterSymbol && other.owner == this.owner
    override fun hashCode(): Int = owner.hashCode()


    private class Pointer(val ownerPointer: KtSymbolPointer<KtFunctionSymbol>) : KtSymbolPointer<KtValueParameterSymbol>() {

import org.jetbrains.kotlin.analysis.api.lifetime.KtLifetimeToken
import org.jetbrains.kotlin.analysis.api.lifetime.withValidityAssertion
import org.jetbrains.kotlin.analysis.utils.printer.prettyPrint
import org.jetbrains.kotlin.name.StandardClassIds
import org.jetbrains.kotlin.types.KotlinType
import org.jetbrains.kotlin.types.UnwrappedType

internal interface KtFe10Type : KtLifetimeOwner, KtAnnotated {

handles are equal, allowing programs to deduplicate values and reduce
their memory footprint.
Comparing two `Handle[T]` values is efficient, reducing down to a simple

	ErrDryRunModeUnsupported = errors.New("dry run mode unsupported")
	// ErrInvalidDB invalid db
	ErrInvalidDB = errors.New("invalid db")
	// ErrInvalidValue invalid value
	ErrInvalidValue = errors.New("invalid value, should be pointer to struct or slice")
	// ErrInvalidValueOfLength invalid values do not match length
	ErrInvalidValueOfLength = errors.New("invalid association values, length doesn't match")

	"github.com/minio/minio/internal/logger"
	"github.com/minio/pkg/v2/policy"
)

const (
	jwtAlgorithm = "Bearer"

	// Default JWT token for web handlers is one day.
	defaultJWTExpiry = 24 * time.Hour

	// Inter-node JWT token expiry is 15 minutes.
	defaultInterNodeJWTExpiry = 15 * time.Minute
)

var (
	errInvalidAccessKeyID = errors.New("The access key ID you provided does not exist in our records")

    * If you know Python types you know how to use Pydantic.
* Plays nicely with your **<abbr title="Integrated Development Environment, similar to a code editor">IDE</abbr>/<abbr title="A program that checks for code errors">linter</abbr>/brain**:
    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:

* **更简单**：
    * 没有新的模式定义 micro-language 需要学习。
    * 如果你知道 Python types，你就知道如何使用 Pydantic。
* 和你 **<abbr title="集成开发环境，和代码编辑器类似">IDE</abbr>/<abbr title="一个检查代码错误的程序">linter</abbr>/brain** 适配:
    * 因为 pydantic 数据结构仅仅是你定义的类的实例；自动补全，linting，mypy 以及你的直觉应该可以和你验证的数据一起正常工作。
* 验证**复杂结构**:
    * 使用分层的 Pydantic 模型, Python `typing`的 `List` 和 `Dict` 等等。
    * 验证器使我们能够简单清楚的将复杂的数据模式定义、检查并记录为 JSON Schema。