class_docstr: Wrapper around the BigDL-LLM
  
  Example:
      .. code-block:: python
  
          from langchain.llms import LlamaLLM
          llm = LlamaLLM(model_path="/path/to/llama/model")
docstr: Call the Llama model and return the output.
  
  Args:
      prompt: The prompt to use for generation.
      stop: A list of strings to stop generation when encountered.
  
  Returns:
      The generated text.
  
  Example:
      .. code-block:: python
  
          from langchain.llms import LlamaLLM
          llm = LlamaLLM(model_path="/path/to/local/llama/model.bin")
          llm("This is a prompt.")
docstr_tok: ["Call","the","Llama","model","and","return","the","output",".","Args",":","prompt",":","The","prompt","to","use","for","generation",".","stop",":","A","list","of","strings","to","stop","generation","when","encountered",".","Returns",":","The","generated","text",".","Example",":","..","code-block",":",":","python","from","langchain.llms","import","LlamaLLM","llm","=","LlamaLLM","(","model_path=","''","\/path\/to\/local\/llama\/model.bin","''",")","llm","(","``","This","is","a","prompt",".","''",")"]
code: def _call(
      self,
      prompt: str,
      stop: Optional[List[str]] = None,
      run_manager: Optional[CallbackManagerForLLMRun] = None,
      **kwargs,
  ) -> str:
      """Call the Llama model and return the output.
  
      Args:
          prompt: The prompt to use for generation.
          stop: A list of strings to stop generation when encountered.
  
      Returns:
          The generated text.
  
      Example:
          .. code-block:: python
  
              from langchain.llms import LlamaLLM
              llm = LlamaLLM(model_path="/path/to/local/llama/model.bin")
              llm("This is a prompt.")
      """
      if self.native:
          if self.streaming:
              # If streaming is enabled, we use the stream
              # method that yields as they are generated
              # and return the combined strings from the first choices's text:
              combined_text_output = ""
              for token in self.stream(
                  prompt=prompt, stop=stop, run_manager=run_manager
              ):
                  combined_text_output += token["choices"][0]["text"]
              return combined_text_output
          else:
              params = self._get_parameters(stop)
              result = self.client(prompt=prompt, **params)
              return result["choices"][0]["text"]
      else:
          return self.client._call(prompt, stop, run_manager, **kwargs)
  
imports: import logging
  import importlib
  from typing import Any, Dict, Generator, List, Optional
  from pydantic import Field, root_validator
  from langchain.callbacks.manager import CallbackManagerForLLMRun
  from langchain.llms.base import LLM
  from .transformersllm import TransformersLLM
label_desc: Use image node_id 6 for calling the _BaseCausalLM obj's underlying member method code with example usage: obj._call(prompt, stop, run_manager) and returns: self, combined_text_output, result