val javaParserVersion = "3.18.0"
// Note: this currently still contains 3/4 logic as we will temporarily have Groovy 3 for the build itself until we move to a Gradle built with Groovy 4
// It can be removed or changed to 4/5 logic (if necessary) at that point.
val groovyVersion = GroovySystem.getVersion()
val isGroovy4 = VersionNumber.parse(groovyVersion).major >= 4

            int,
            Doc(
                """
                The position in bytes to seek to in the file.
                """
            ),
        ],
    ) -> None:
        """
        Move to a position in the file.

        Any next read or write will be done from that position.

        To be awaitable, compatible with async, this is run in threadpool.
        """

In some cases, for pure data validation and processing, you can get performance improvements of **20x** or more. This means 2,000% or more. 🤯

            f" Please update the response model {type_!r}."
        )
    class_validators = class_validators or {}

    field_info = field_info or FieldInfo(annotation=type_, default=default, alias=alias)
    kwargs = {"mode": mode, "name": name, "field_info": field_info}
    try:
        return v2.ModelField(**kwargs)  # type: ignore[return-value,arg-type]
    except PydanticSchemaGenerationError:
        raise fastapi.exceptions.FastAPIError(

    encoded as JSON before it is sent to the client.

    You can also use it yourself, for example to convert objects before saving them
    in a database that supports only JSON.

    Read more about it in the
    [FastAPI docs for JSON Compatible Encoder](https://fastapi.tiangolo.com/tutorial/encoder/).
    """
    custom_encoder = custom_encoder or {}
    if custom_encoder:

                name=response_name,
                type_=self.response_model,
                mode="serialization",
            )
            # Create a clone of the field, so that a Pydantic submodel is not returned
            # as is just because it's an instance of a subclass of a more limited class
            # e.g. UserInDB (containing hashed_password) could be a subclass of User

Check the in-code annotation tips above to see more specific details.

## Learn More { #learn-more }

You can also combine `dataclasses` with other Pydantic models, inherit from them, include them in your own models, etc.

# TODO(rostam): Remove when `cc_shared_library` is enabled by default
common --experimental_cc_shared_library

# cc_shared_library ensures no library is linked statically more than once.
common --experimental_link_static_libraries_once=false

# Prevent regressions on those two incompatible changes
# TODO: remove those flags when they are flipped in the default Bazel version TF uses.

///

## Learn More { #learn-more }

You can learn more about **GraphQL** in the <a href="https://graphql.org/" class="external-link" target="_blank">official GraphQL documentation</a>.

    ) -> Any:
        # What calls this code passes a value that already called
        # self._type_adapter.validate_python(value)
        return self._type_adapter.dump_python(
            value,
            mode=mode,
            include=include,
            exclude=exclude,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,