should have descriptions of the parameters and Python functions should have
            Google Python style args descriptions in the docstring. Additionally,
            Python functions should only use primitive types (str, int, float, bool) or
            pydantic.BaseModels for arguments.
        llm: Language model to use, assumed to support the Ernie function-calling API.
        prompt: BasePromptTemplate to pass to the model.
        output_parser: BaseLLMOutputParser to use for parsing model outputs. By default
            will be inferred from the function types. If pydantic.BaseModels are passed
            in, then the OutputParser will try to parse outputs using those. Otherwise
            model outputs will simply be parsed as JSON. If multiple functions are
            passed in and they are not pydantic.BaseModels, the chain output will
            include both the name of the function that was returned and the arguments
            to pass to the function.
    Returns:
        A runnable sequence that will pass in the given functions to the model when run.
    Example:
        .. code-block:: python
                from typing import Optional
                from langchain.chains.ernie_functions import create_ernie_fn_chain
                from langchain.chat_models import ErnieBotChat
                from langchain.prompts import ChatPromptTemplate
                from langchain.pydantic_v1 import BaseModel, Field
                class RecordPerson(BaseModel):
                    \"\"\"Record some identifying information about a person.\"\"\"
                    name: str = Field(..., description="The person's name")
                    age: int = Field(..., description="The person's age")
                    fav_food: Optional[str] = Field(None, description="The person's favorite food")
                class RecordDog(BaseModel):
                    \"\"\"Record some identifying information about a dog.\"\"\"