import org.jetbrains.kotlin.analysis.low.level.api.fir.util.originalDeclaration
import org.jetbrains.kotlin.analysis.project.structure.DanglingFileResolutionMode
import org.jetbrains.kotlin.analysis.project.structure.KtDanglingFileModule
import org.jetbrains.kotlin.analysis.project.structure.KtModule
import org.jetbrains.kotlin.analysis.utils.printer.parentOfType
import org.jetbrains.kotlin.fileClasses.javaFileFacadeFqName

import org.jetbrains.kotlin.light.classes.symbol.annotations.annotateByKtType
import org.jetbrains.kotlin.load.java.structure.impl.JavaClassImpl
import org.jetbrains.kotlin.load.java.structure.impl.JavaTypeImpl
import org.jetbrains.kotlin.load.java.structure.impl.JavaTypeParameterImpl
import org.jetbrains.kotlin.load.java.structure.impl.source.JavaElementSourceFactory
import org.jetbrains.kotlin.load.kotlin.TypeMappingMode

**FastAPI** provides a convenience tool to structure your application while keeping all the flexibility.

!!! info
    If you come from Flask, this would be the equivalent of Flask's Blueprints.

## An example file structure

Let's say you have a file structure like this:

```
.
├── app
│   ├── __init__.py
│   ├── main.py
│   ├── dependencies.py

type formatErasureVersionDetect struct {
	Erasure struct {
		Version string `json:"version"`
	} `json:"xl"`
}

// Represents the V1 backend disk structure version
// under `.minio.sys` and actual data namespace.
// formatErasureV1 - structure holds format config version '1'.
type formatErasureV1 struct {
	formatMetaV1
	Erasure struct {
		Version string `json:"version"` // Version of 'xl' format.

		s3InputBytes:         s.getS3InputBytes(),         // Traffic S3 received
		s3OutputBytes:        s.getS3OutputBytes(),        // Traffic S3 sent
	}
}

// Prepare new ConnStats structure
func newConnStats() *connStats {
	return &connStats{}
}

type bucketS3RXTX struct {
	s3InputBytes  uint64
	s3OutputBytes uint64
}

type bucketHTTPAPIStats struct {
	currentS3Requests *HTTPAPIStats

import org.jetbrains.kotlin.analysis.low.level.api.fir.project.structure.llFirModuleData
import org.jetbrains.kotlin.analysis.low.level.api.fir.sessions.LLFirSession
import org.jetbrains.kotlin.analysis.low.level.api.fir.sessions.llFirSession
import org.jetbrains.kotlin.analysis.low.level.api.fir.util.codeFragment
import org.jetbrains.kotlin.analysis.project.structure.KtDanglingFileModule

type PostPolicyForm struct {
	Expiration time.Time // Expiration date and time of the POST policy.
	Conditions struct {  // Conditional policy structure.
		Policies []struct {
			Operator string
			Key      string
			Value    string
		}
		ContentLengthRange contentLengthRange
	}
}

// implemented to ensure that duplicate keys in JSON

// storage class kind supported.
func IsValid(sc string) bool {
	return sc == RRS || sc == STANDARD
}

// UnmarshalText unmarshals storage class from its textual form into
// storageClass structure.
func (sc *StorageClass) UnmarshalText(b []byte) error {
	scStr := string(b)
	if scStr == "" {
		return nil
	}
	s, err := parseStorageClass(scStr)
	if err != nil {
		return err
	}

// does various checks while doing so. `input_nodes` will contain the same
// information as input_tensors just in a different structure to make
// following processing easier. TODO(iga): Simplify this nested structure.
Status ProcessInputs(
    const TF_Graph* fn_body, const char* fn_name, int ninputs,
    const TF_Output* inputs, std::vector<OutputTensor>* input_tensors,

// I want to set up a local/remote repositories for testing but I don't want to have
// to change them when I change the layout of the repositories. So I want to generate
// the structure I want to test by using the artifact handler manager which dictates
// the layout used for a particular artifact type.

/**
 */
@Deprecated
class ArtifactResolverTest extends AbstractArtifactComponentTestCase {