def formatted_llm_call(inputs: List[Variable], response_role_description: str, 
                       engine: EngineLM, format_string: str, 
                       fields: dict[str, str], system_prompt: Variable = None):
    """A functional version of the LLM call with formatted strings. 
    Just a wrapper around the FormattedLLMCall class.
    
    This function will call the LLM with the input and return the response, also register the grad_fn for backpropagation.

    :param inputs: Variables to use for the input. This should be a mapping of the fields to the variables.
    :type inputs: dict[str, Variable]
    :param response_role_description: Role description for the response variable, defaults to VARIABLE_OUTPUT_DEFAULT_ROLE
    :type response_role_description: str, optional
    :param engine: The engine to use for the LLM call.
    :type engine: EngineLM
    :param format_string: The format string to use for the input. For instance, "The capital of {country} is {capital}". For a format string like this, we'll expect to have the fields dictionary to have the keys "country" and "capital". Similarly, in the forward pass, we'll expect the input variables to have the keys "country" and "capital".
    :type format_string: str
    :param fields: The fields to use for the format string. For the above example, this would be {"country": {}, "capital": {}}. This is currently a dictionary in case we'd want to inject more information later on.
    :type fields: dict[str, str]
    :param system_prompt: The system prompt to use for the LLM call. Default value depends on the engine.
    :type system_prompt: Variable, optional
    :return: Sampled response from the LLM
    :rtype: Variable
    """
    call_object = FormattedLLMCall(engine=engine, format_string=format_string, fields=fields, system_prompt=system_prompt)
    return call_object(inputs, response_role_description)
A functional version of the LLM call with formatted strings. 
    Just a wrapper around the FormattedLLMCall class.
    
    This function will call the LLM with the input and return the response, also register the grad_fn for backpropagation.

    :param inputs: Variables to use for the input. This should be a mapping of the fields to the variables.
    :type inputs: dict[str, Variable]
    :param response_role_description: Role description for the response variable, defaults to VARIABLE_OUTPUT_DEFAULT_ROLE
    :type response_role_description: str, optional
    :param engine: The engine to use for the LLM call.
    :type engine: EngineLM
    :param format_string: The format string to use for the input. For instance, "The capital of {country} is {capital}". For a format string like this, we'll expect to have the fields dictionary to have the keys "country" and "capital". Similarly, in the forward pass, we'll expect the input variables to have the keys "country" and "capital".
    :type format_string: str
    :param fields: The fields to use for the format string. For the above example, this would be {"country": {}, "capital": {}}. This is currently a dictionary in case we'd want to inject more information later on.
    :type fields: dict[str, str]
    :param system_prompt: The system prompt to use for the LLM call. Default value depends on the engine.
    :type system_prompt: Variable, optional
    :return: Sampled response from the LLM
    :rtype: Variable
    
textgrad/autograd/functional.py