corresponding JSON.
                * Filtering: the JSON sent to the client will only contain the data
                    (fields) defined in the `response_model`. If you returned an object
                    that contains an attribute `password` but the `response_model` does
                    not include that field, the JSON sent to the client would not have

    # TODO: in theory this would allow declaring types that could be lists of bytes
    # to be read from files and other types, but I'm not even sure it's a good idea
    # to support it as a first class "feature"
    assert is_bytes_sequence_annotation(Union[list[str], list[bytes]])


def test_is_uploadfile_sequence_annotation():
    # For coverage
    # TODO: in theory this would allow declaring types that could be lists of UploadFile

        + f"\n\nIt uses the prompt file https://github.com/fastapi/fastapi/blob/master/docs/{language}/llm-prompt.md."
        + "\n\nIn most cases, it's better to make PRs updating that file so that the LLM can do a better job generating the translations than suggesting changes in this PR."
    )
    pr = gh_repo.create_pull(title=message, body=body, base="master", head=branch_name)
    print(f"Created PR: {pr.number}")
    print("Finished")

        Optional[float],
        Doc(
            """
            Greater than. If set, value must be greater than this. Only applicable to
            numbers.
            """
        ),
    ] = None,
    ge: Annotated[
        Optional[float],
        Doc(
            """
            Greater than or equal. If set, value must be greater than or equal to
            this. Only applicable to numbers.
            """

client = TestClient(app)


def test_get_users():
    """Check that /users returns expected data"""
    response = client.get("/users")
    assert response.status_code == 200, response.text
    assert response.json() == [{"user_id": "u1"}, {"user_id": "u2"}]


def test_get_user():
    """Check that /users/{user_id} returns expected data"""
    response = client.get("/users/abc123")

It was deprecated from Starlette, but if you have code that used it, you can easily **migrate** to <a href="https://github.com/ciscorn/starlette-graphene3" class="external-link" target="_blank">starlette-graphene3</a>, that covers the same use case and has an **almost identical interface**.

/// tip

        decimal_places: Union[int, None] = _Unset,
        examples: Optional[list[Any]] = None,
        example: Annotated[
            Optional[Any],
            deprecated(
                "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
                "although still supported. Use examples instead."
            ),
        ] = _Unset,
        openapi_examples: Optional[dict[str, Example]] = None,

        bool,
        Doc(
            """
            Pydantic's `exclude_unset` parameter, passed to Pydantic models to define
            if it should exclude from the output the fields that were not explicitly
            set (and that only had their default values).
            """
        ),
    ] = False,
    exclude_defaults: Annotated[
        bool,
        Doc(
            """

# Initialize the TensorFlow repository and all dependencies.
#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

tf_workspace3()

    In this case, it's a list of `Item` dataclasses.

6. Here we are returning a dictionary that contains `items` which is a list of dataclasses.

    FastAPI is still capable of <abbr title="converting the data to a format that can be transmitted">serializing</abbr> the data to JSON.

7. Here the `response_model` is using a type annotation of a list of `Author` dataclasses.