///
/// All pointers defined in this structure point to memory allocated by the DSO
/// using an allocator provided by core TensorFlow when calling `TF_InitPlugin`.
///
/// IMPORTANT: To maintain binary compatibility, the layout of this structure
/// must not change! In the unlikely case that a new type of file needs to be
/// supported, add the new ops and metadata at the end of the structure.
typedef struct TF_FilesystemPluginOps {

### Response

If the token is valid and access is approved, the plugin must return a `200` (OK) HTTP status code.

A `200 OK` Response should have `application/json` content-type and body with the following structure:

```json
{
    "user": <string>,
    "maxValiditySeconds": <integer>,
    "claims": <key-value-pairs>
}
```

		stsLogIf(ctx, err, logger.ErrorKind)
	}
	encodedErrorResponse := encodeResponse(stsErrorResponse)
	writeResponse(w, stsErr.HTTPStatusCode, encodedErrorResponse, mimeXML)
}

// STSError structure
type STSError struct {
	Code           string
	Description    string
	HTTPStatusCode int
}

// STSErrorResponse - error response format
type STSErrorResponse struct {

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

In this example we'll create a temporary database only for the tests.

## File structure

We create a new file at `sql_app/tests/test_sql_app.py`.

So the new file structure looks like:

``` hl_lines="9-11"
.
└── sql_app
    ├── __init__.py
    ├── crud.py
    ├── database.py
    ├── main.py
    ├── models.py

import java.util.ArrayList;
import java.util.Collection;

/**
 * This is the main graph data structure used by the RepositorySystem to present tree and graph objects.
 *
 *
 */
@Deprecated
public class MetadataGraph {
    /** all graph nodes */
    Collection<MetadataGraphNode> nodes;

    /** entry point for tree-like structures */
    MetadataGraphNode entry;

    public MetadataGraph(MetadataGraphNode entry) {

# CI Pipeline Configuration

## Open & import the project

In your IDEA, `File` - `Open`, select `.teamcity/pom.xml`, `import as project`, and you'll have a Maven project.

## Project structure

Mostly a standard Maven project structure. The entry point `settings.kts` defines the TeamCity project.

There are 3 subprojects in the TeamCity project hierarchy: `Check` for Gradle builds, `Promotion` for releasing Gradle versions, `Util` for miscellaneous utilities.

# TensorFlow continuous integration

> **Warning** This folder is still under construction. It is part of an ongoing
> effort to improve the structure of CI and build related files within the
> TensorFlow repo. This warning will be removed when the contents of this
> directory are stable and appropriate documentation around its usage is in
> place.

Maintainer: TensorFlow DevInfra

### Project file structure

Let's say your project file structure looks like this:

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

Now create a directory to store those static files.

Your new file structure could look like this:

```
.
├── app
│   ├── __init__.py
│   ├── main.py
└── static/
```

**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