code: def load_tool(
      task_or_repo_id,
      model_repo_id=None,
      remote=False,
      token=None,
      **kwargs,
  ):
      """
      Main function to quickly load a tool, be it on the Hub or in the Transformers library.
  
      Args:
          task_or_repo_id (`str`):
              The task for which to load the tool or a repo ID of a tool on the Hub. Tasks implemented in Transformers
              are:
  
              - `"document-question-answering"`
              - `"image-captioning"`
              - `"image-question-answering"`
              - `"image-segmentation"`
              - `"speech-to-text"`
              - `"summarization"`
              - `"text-classification"`
              - `"text-question-answering"`
              - `"text-to-speech"`
              - `"translation"`
  
          model_repo_id (`str`, *optional*):
              Use this argument to use a different model than the default one for the tool you selected.
          remote (`bool`, *optional*, defaults to `False`):
              Whether to use your tool by downloading the model or (if it is available) with an inference endpoint.
          token (`str`, *optional*):
              The token to identify you on hf.co. If unset, will use the token generated when running `huggingface-cli
              login` (stored in `~/.huggingface`).
          kwargs (additional keyword arguments, *optional*):
              Additional keyword arguments that will be split in two: all arguments relevant to the Hub (such as
              `cache_dir`, `revision`, `subfolder`) will be used when downloading the files for your tool, and the others
              will be passed along to its init.
      """
      if task_or_repo_id in TASK_MAPPING:
          tool_class_name = TASK_MAPPING[task_or_repo_id]
          main_module = importlib.import_module("transformers")
          tools_module = main_module.tools
          tool_class = getattr(tools_module, tool_class_name)
  
          if remote:
              if model_repo_id is None:
                  endpoints = get_default_endpoints()
                  if task_or_repo_id not in endpoints:
                      raise ValueError(
                          f"Could not infer a default endpoint for {task_or_repo_id}, you need to pass one using the "
                          "`model_repo_id` argument."
                      )
                  model_repo_id = endpoints[task_or_repo_id]
              return RemoteTool(
                  model_repo_id, token=token, tool_class=tool_class
              )
          else:
              return tool_class(model_repo_id, token=token, **kwargs)
      else:
          return Tool.from_hub(
              task_or_repo_id,
              model_repo_id=model_repo_id,
              token=token,
              remote=remote,
              **kwargs,
          )
  
imports: import base64
  import importlib
  import inspect
  import io
  import json
  import os
  import tempfile
  from typing import Any, Dict, List, Optional, Union
  from huggingface_hub import create_repo, hf_hub_download, metadata_update, upload_folder
  from huggingface_hub.utils import RepositoryNotFoundError, build_hf_headers, get_session
  from ..dynamic_module_utils import custom_object_save, get_class_from_dynamic_module, get_imports
  from ..image_utils import is_pil_image
  from ..models.auto import AutoProcessor
  from ..utils import CONFIG_NAME, cached_file, is_accelerate_available, is_torch_available, is_vision_available, logging
  from .agent_types import handle_agent_inputs, handle_agent_outputs
label_desc: Use image node_id 6 for calling a global function with example usage: load_tool(task_or_repo_id, model_repo_id, remote, token) and returns: Tool, RemoteTool, tool_class