llm("Tell me a joke.")
        """
        if self.streaming:
            combined_output = ""
            async for chunk in self._astream(
                prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
            ):
                combined_output += chunk.text
            text = combined_output
        else:
            text = (
                self.pipeline(
                    sequences=prompt, generation_config=self.generation_config
                )
                .generations[0]
                .text
            )
        if stop is not None:
            text = enforce_stop_tokens(text, stop)
        return text
    def _stream(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Iterator[GenerationChunk]:
        """Yields results objects as they are generated in real time.
        It also calls the callback manager's on_llm_new_token event with
        similar parameters to the OpenAI LLM class method of the same name.
        Args:
            prompt: The prompt to pass into the model.
            stop: Optional list of stop words to use when generating.
        Returns:
            A generator representing the stream of tokens being generated.
        Yields:
            A dictionary like object containing a string token.
        Example:
            .. code-block:: python
                from langchain.llms import DeepSparse
                llm = DeepSparse(
                    model="zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base_quant-none",
                    streaming=True
                )
                for chunk in llm.stream("Tell me a joke",
                        stop=["'","\n"]):