solutions. PAL is a technique described in the paper "Program-Aided Language Models"
    (https://arxiv.org/pdf/2211.10435.pdf).
    """
    llm_chain: LLMChain
    stop: str = "\n\n"
    """Stop token to use when generating code."""
    get_answer_expr: str = "print(solution())"
    """Expression to use to get the answer from the generated code."""
    python_globals: Optional[Dict[str, Any]] = None
    """Python globals and locals to use when executing the generated code."""
    python_locals: Optional[Dict[str, Any]] = None
    """Python globals and locals to use when executing the generated code."""
    output_key: str = "result"  #: :meta private:
    return_intermediate_steps: bool = False
    """Whether to return intermediate steps in the generated code."""
    code_validations: PALValidation = Field(default_factory=PALValidation)
    """Validations to perform on the generated code."""
    timeout: Optional[int] = 10
    """Timeout in seconds for the generated code to execute."""
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    @property
    def input_keys(self) -> List[str]:
        """Return the singular input key.
        :meta private:
        """
        return self.llm_chain.prompt.input_variables
    @property
    def output_keys(self) -> List[str]:
        """Return the singular output key.
        :meta private:
        """
        if not self.return_intermediate_steps:
            return [self.output_key]
        else:
            return [self.output_key, "intermediate_steps"]
    def _call(
        self,