def forward(self, 
                inputs: List[Variable], 
                response_role_description: str = VARIABLE_OUTPUT_DEFAULT_ROLE) -> Variable:
        """
        Forward pass for the multimodal LLM call function.

        :param inputs: list of input variables to the multimodal LLM call. One is an image and the second one is text
        :type inputs: List[Variable]
        :param response_role_description: role description for the response variable
        :type response_role_description: str, optional

        >>> from textgrad import Variable, get_engine
        >>> from textgrad.autograd import MultimodalLLMCall
        >>> target_image = "A byte representation of the image"
        >>> question_variable = Variable("What do you see here?", role_description="question to answer", requires_grad=False)
        >>> response = MultimodalLLMCall("gpt-4o")([target_image, question_variable])
        """
        # First ensure that all keys are present in the fields

        # Assert that all variables are either strings or bytes
        for variable in inputs:
            if not isinstance(variable.get_value(), (str, bytes)):
                raise ValueError(f"MultimodalLLMCall only accepts str or bytes, got {type(variable.get_value())}")
        
        system_prompt_value = self.system_prompt.value if self.system_prompt else None
        input_content = [inp.value for inp in inputs]
        # Make the LLM Call
        response_text = self.engine(input_content, system_prompt=system_prompt_value)

        # Create the response variable
        response = Variable(
            value=response_text,
            predecessors=[self.system_prompt, *inputs] if self.system_prompt else [*inputs],
            role_description=response_role_description
        )
        
        logger.info(f"MultimodalLLMCall function forward", extra={"text": f"System:{system_prompt_value}\n{inputs}"})
        
        # Populate the gradient function, using a container to store the backward function and the context
        response.set_grad_fn(BackwardContext(backward_fn=self.backward, 
                                             response=response, 
                                             input_content=input_content, 
                                             system_prompt=system_prompt_value))
        
        return response

        Forward pass for the multimodal LLM call function.

        :param inputs: list of input variables to the multimodal LLM call. One is an image and the second one is text
        :type inputs: List[Variable]
        :param response_role_description: role description for the response variable
        :type response_role_description: str, optional

        >>> from textgrad import Variable, get_engine
        >>> from textgrad.autograd import MultimodalLLMCall
        >>> target_image = "A byte representation of the image"
        >>> question_variable = Variable("What do you see here?", role_description="question to answer", requires_grad=False)
        >>> response = MultimodalLLMCall("gpt-4o")([target_image, question_variable])
        
textgrad/autograd/multimodal_ops.py