@app.get("/app")
def read_main(request: Request):
    return {"message": "Hello World", "root_path": request.scope.get("root_path")}


client = TestClient(app)


def test_main():
    response = client.get("/app")
    assert response.status_code == 200
    assert response.json() == {"message": "Hello World", "root_path": "/api/v1"}


def test_openapi():
    response = client.get("/openapi.json")

  // security reasons anyway. Therefore, the FRQ.close() method provides a way to stop the thread
  // explicitly. This test checks that calling that method does allow an app's ClassLoader to be
  // gc'd even if there is a still a FinalizableReferenceQueue in a static field. (Setting the field

            false
        fi
    done
}

@test "No duplicate files on Windows" {
    cat <<EOF
Please rename files so there are no repeats. For example, README.md and
Readme.md would be the same file on Windows. In this test, you would get a
warning for "readme.md" because it makes everything lowercase. There are
repeats of these filename(s) with different casing:
EOF

            throw new FessSystemException("Could not find extractorFactory.");
        }
        Extractor extractor = extractorFactory.getExtractor(responseData.getMimeType());
        if (extractor == null) {
            extractor = ComponentUtil.getComponent("tikaExtractor");
            if (extractor == null) {
                throw new FessSystemException("Could not find tikaExtractor.");
            }
        }

# Header Parameter Models

If you have a group of related **header parameters**, you can create a **Pydantic model** to declare them.

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

## Header Parameters with a Pydantic Model

    /*
     * For example, if concentration=2.0, the following takes the square root of
     * the uniformly-distributed random integer, then truncates any fractional
     * part, so higher integers would appear (in this case linearly) more often
     * than lower ones.
     */
    return (int) Math.pow(a, 1.0 / concentration);
  }

  @AfterExperiment
  void tearDown() {
    double req = requests.get();

```Python hl_lines="1  9  12"
{!../../docs_src/response_change_status_code/tutorial001.py!}
```

And then you can return any object you need, as you normally would (a `dict`, a database model, etc).

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.

# Query Parameter Models

If you have a group of **query parameters** that are related, you can create a **Pydantic model** to declare them.

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

## Query Parameters with a Pydantic Model

issue in TensorFlow Lite that is not about Converters, Play Services or Ops
body: - type: dropdown id: issue-type attributes: label: Issue Type description:
What type of issue would you like to report? multiple: false options: - Bug -
Build/Install - Performance - Support - Feature Request - Documentation Feature
Request - Documentation Bug - Others validations: required: true - type:

     */
    public abstract DirectoryProperty getDocumentationRenderedRoot();

    /**
     * The collection of rendered documentation.  This is everything laid out as it would be deployed/packaged.
     */
    public abstract ConfigurableFileCollection getRenderedDocumentation();

    // These are all helper methods for configuring the parts of the documentation (DSL ref, javadoc, user manual, etc).