And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

        endpoint: Callable[..., Any],
        *,
        name: Optional[str] = None,
        dependencies: Optional[Sequence[params.Depends]] = None,
        dependency_overrides_provider: Optional[Any] = None,
    ) -> None:
        self.path = path
        self.endpoint = endpoint
        self.name = get_name(endpoint) if name is None else name
        self.dependencies = list(dependencies or [])

            parameter = {
                "name": name,
                "in": param_type.value,
                "required": param.required,
                "schema": param_schema,
            }
            if field_info.description:
                parameter["description"] = field_info.description
            openapi_examples = getattr(field_info, "openapi_examples", None)

/// tip

**GraphQL** solves some very specific use cases.

It has **advantages** and **disadvantages** when compared to common **web APIs**.

Make sure you evaluate if the **benefits** for your use case compensate the **drawbacks**. 🤓

///

## GraphQL Libraries { #graphql-libraries }

class BaseUser(BaseModel):
    name: str


class User(BaseUser):
    surname: str


class DBUser(User):
    password_hash: str


class Item(BaseModel):
    name: str
    price: float


app = FastAPI()


@app.get("/no_response_model-no_annotation-return_model")
def no_response_model_no_annotation_return_model():
    return User(name="John", surname="Doe")

import jetbrains.buildServer.configs.kotlin.triggers.schedule

const val NIGHTLY_SNAPSHOT_BUILD_ID = "Promotion_Nightly"

/**
 * 0~23.
 * To avoid nightly promotion jobs running at the same time,
 * we run each branch on different hours.
 * master - 0:00
 * release - 1:00
 * release6x - 2:00
 * release7x - 3:00
 * ...
 * releaseNx - (N-4):00
 */

# buildifier: disable=load-on-top

workspace(name = "org_tensorflow")

# buildifier: disable=load-on-top

load("//third_party:repo.bzl", "tf_http_archive", "tf_mirror_urls")

tf_http_archive(
    name = "rules_shell",
    sha256 = "bc61ef94facc78e20a645726f64756e5e285a045037c7a61f65af2941f4c25e1",
    strip_prefix = "rules_shell-0.4.1",
    urls = tf_mirror_urls(

# See https://github.com/protocolbuffers/upb/blob/9effcbcb27f0a665f9f345030188c0b291e32482/upb/upb.c#L183.
common:rbe_linux_cpu --copt=-Wno-gnu-offsetof-extensions
# Python config is the same across all containers because the binary is the same
common:rbe_linux_cpu --python_path="/usr/bin/python3"
# These you may need to change for your own GCP project.

    «««
    /// note
    »»»

To translate it, keep the same line and add the translation after a vertical bar.

For example, if you were translating to Spanish, you would write:

    «««
    /// note | Nota
    »»»

Some examples in Spanish:

    Source:

        «««
        /// tip
        »»»

    Result:

def _should_embed_body_fields(fields: list[ModelField]) -> bool:
    if not fields:
        return False
    # More than one dependency could have the same field, it would show up as multiple
    # fields but it's the same one, so count them by name
    body_param_names_set = {field.name for field in fields}
    # A top level field has to be a single field, not multiple
    if len(body_param_names_set) > 1:
        return True