class_docstr: Wrapper around the BigDL-LLM Transformer-INT4 model in Transformer.pipeline()
  
  Example:
      .. code-block:: python
  
          from langchain.llms import TransformersPipelineLLM
          llm = TransformersPipelineLLM.from_model_id(model_id="decapoda-research/llama-7b-hf")
class_docstr_tok: ["Wrapper","around","the","BigDL-LLM","Transformer-INT4","model","in","Transformer.pipeline","(",")","Example",":","..","code-block",":",":","python","from","langchain.llms","import","TransformersPipelineLLM","llm","=","TransformersPipelineLLM.from_model_id","(","model_id=","''","decapoda-research\/llama-7b-hf","''",")"]
code: def _call(
      self,
      prompt: str,
      stop: Optional[List[str]] = None,
      run_manager: Optional[CallbackManagerForLLMRun] = None,
      **kwargs: Any,
  ) -> str:
      response = self.pipeline(prompt)
      if self.pipeline.task == "text-generation":
          # Text generation return includes the starter text.
          text = response[0]["generated_text"][len(prompt) :]
      elif self.pipeline.task == "text2text-generation":
          text = response[0]["generated_text"]
      elif self.pipeline.task == "summarization":
          text = response[0]["summary_text"]
      else:
          raise ValueError(
              f"Got invalid task {self.pipeline.task}, "
              f"currently only {VALID_TASKS} are supported"
          )
      if stop is not None:
          # This is a bit hacky, but I can't figure out a better way to enforce
          # stop tokens when making calls to huggingface_hub.
          text = enforce_stop_tokens(text, stop)
      return text
  
file_path: BigDL/python/llm/src/bigdl/llm/langchain/llms/transformerspipelinellm.py
imports: import importlib.util
  import logging
  from typing import Any, List, Mapping, Optional
  from pydantic import Extra
  from langchain.callbacks.manager import CallbackManagerForLLMRun
  from langchain.llms.base import LLM
  from langchain.llms.utils import enforce_stop_tokens
label_desc: Use image node_id 4 for calling the TransformersPipelineLLM obj's underlying member method code with example usage: obj._call(prompt, stop, run_manager) and returns: text