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: Get the number of tokens that present in the text.
  
  Useful for checking if an input will fit in a model's context window.
  
  Args:
      text: The string input to tokenize.
  
  Returns:
      The number of tokens in the text.
docstr_tok: ["Get","the","number","of","tokens","that","present","in","the","text",".","Useful","for","checking","if","an","input","will","fit","in","a","model","'s","context","window",".","Args",":","text",":","The","string","input","to","tokenize",".","Returns",":","The","number","of","tokens","in","the","text","."]
code: def get_num_tokens(self, text: str) -> int:
      """Get the number of tokens that present in the text.
  
      Useful for checking if an input will fit in a model's context window.
  
      Args:
          text: The string input to tokenize.
  
      Returns:
          The number of tokens in the text.
      """
      tokenized_text = self.client.tokenize(text.encode("utf-8"))
      return len(tokenized_text)
  
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 8 for calling the _BaseCausalLM obj's underlying member method code with example usage: obj.get_num_tokens(text) and returns: len