google/generativeai/responder.py [285:343]:
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    format_ = schema.pop("format", None)
    if format_ is not None:
        schema["format_"] = format_

    items = schema.pop("items", None)
    if items is not None:
        schema["items"] = _rename_schema_fields(items)

    properties = schema.pop("properties", None)
    if properties is not None:
        schema["properties"] = {k: _rename_schema_fields(v) for k, v in properties.items()}

    return schema


class FunctionDeclaration:
    def __init__(self, *, name: str, description: str, parameters: dict[str, Any] | None = None):
        """A  class wrapping a `protos.FunctionDeclaration`, describes a function for `genai.GenerativeModel`'s `tools`."""
        self._proto = protos.FunctionDeclaration(
            name=name, description=description, parameters=_rename_schema_fields(parameters)
        )

    @property
    def name(self) -> str:
        return self._proto.name

    @property
    def description(self) -> str:
        return self._proto.description

    @property
    def parameters(self) -> protos.Schema:
        return self._proto.parameters

    @classmethod
    def from_proto(cls, proto) -> FunctionDeclaration:
        self = cls(name="", description="", parameters={})
        self._proto = proto
        return self

    def to_proto(self) -> protos.FunctionDeclaration:
        return self._proto

    @staticmethod
    def from_function(function: Callable[..., Any], descriptions: dict[str, str] | None = None):
        """Builds a `CallableFunctionDeclaration` from a python function.

        The function should have type annotations.

        This method is able to generate the schema for arguments annotated with types:

        `AllowedTypes = float | int | str | list[AllowedTypes] | dict`

        This method does not yet build a schema for `TypedDict`, that would allow you to specify the dictionary
        contents. But you can build these manually.
        """

        if descriptions is None:
            descriptions = {}
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google/generativeai/types/content_types.py [554:612]:
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    format_ = schema.pop("format", None)
    if format_ is not None:
        schema["format_"] = format_

    items = schema.pop("items", None)
    if items is not None:
        schema["items"] = _rename_schema_fields(items)

    properties = schema.pop("properties", None)
    if properties is not None:
        schema["properties"] = {k: _rename_schema_fields(v) for k, v in properties.items()}

    return schema


class FunctionDeclaration:
    def __init__(self, *, name: str, description: str, parameters: dict[str, Any] | None = None):
        """A  class wrapping a `protos.FunctionDeclaration`, describes a function for `genai.GenerativeModel`'s `tools`."""
        self._proto = protos.FunctionDeclaration(
            name=name, description=description, parameters=_rename_schema_fields(parameters)
        )

    @property
    def name(self) -> str:
        return self._proto.name

    @property
    def description(self) -> str:
        return self._proto.description

    @property
    def parameters(self) -> protos.Schema:
        return self._proto.parameters

    @classmethod
    def from_proto(cls, proto) -> FunctionDeclaration:
        self = cls(name="", description="", parameters={})
        self._proto = proto
        return self

    def to_proto(self) -> protos.FunctionDeclaration:
        return self._proto

    @staticmethod
    def from_function(function: Callable[..., Any], descriptions: dict[str, str] | None = None):
        """Builds a `CallableFunctionDeclaration` from a python function.

        The function should have type annotations.

        This method is able to generate the schema for arguments annotated with types:

        `AllowedTypes = float | int | str | list[AllowedTypes] | dict`

        This method does not yet build a schema for `TypedDict`, that would allow you to specify the dictionary
        contents. But you can build these manually.
        """

        if descriptions is None:
            descriptions = {}
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