code: def get_task(model: str, use_auth_token: Optional[str] = None) -> str:
      tmp = io.BytesIO()
      headers = {}
      if use_auth_token:
          headers["Authorization"] = f"Bearer {use_auth_token}"
  
      try:
          http_get(
              f"https://huggingface.co/api/models/{model}",
              tmp,
              headers=headers,
          )
          tmp.seek(0)
          body = tmp.read()
          data = json.loads(body)
      except Exception as e:
          raise RuntimeError(
              f"Instantiating a pipeline without a task set raised an error: {e}"
          )
      if "pipeline_tag" not in data:
          raise RuntimeError(
              f"The model {model} does not seem to have a correct `pipeline_tag` set to infer the task automatically"
          )
      if data.get("library_name", "transformers") != "transformers":
          raise RuntimeError(
              f"This model is meant to be used with {data['library_name']} not with transformers"
          )
      task = data["pipeline_tag"]
      return task
  
imports: import io
  import json
  import os
  import warnings
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
  from numpy import isin
  from huggingface_hub.file_download import http_get
  from ..configuration_utils import PretrainedConfig
  from ..dynamic_module_utils import get_class_from_dynamic_module
  from ..feature_extraction_utils import PreTrainedFeatureExtractor
  from ..models.auto.configuration_auto import AutoConfig
  from ..models.auto.feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
  from ..models.auto.modeling_auto import AutoModelForDepthEstimation
  from ..models.auto.tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
  from ..tokenization_utils import PreTrainedTokenizer
  from ..tokenization_utils_fast import PreTrainedTokenizerFast
  from ..utils import HUGGINGFACE_CO_RESOLVE_ENDPOINT, is_kenlm_available, is_pyctcdecode_available, is_tf_available, is_torch_available, logging
  from .audio_classification import AudioClassificationPipeline
  from .automatic_speech_recognition import AutomaticSpeechRecognitionPipeline
  from .base import ArgumentHandler, CsvPipelineDataFormat, JsonPipelineDataFormat, PipedPipelineDataFormat, Pipeline, PipelineDataFormat, PipelineException, PipelineRegistry, get_default_model_and_revision, infer_framework_load_model
  from .conversational import Conversation, ConversationalPipeline
  from .depth_estimation import DepthEstimationPipeline
  from .document_question_answering import DocumentQuestionAnsweringPipeline
  from .feature_extraction import FeatureExtractionPipeline
  from .fill_mask import FillMaskPipeline
  from .image_classification import ImageClassificationPipeline
  from .image_segmentation import ImageSegmentationPipeline
  from .image_to_text import ImageToTextPipeline
  from .object_detection import ObjectDetectionPipeline
  from .question_answering import QuestionAnsweringArgumentHandler, QuestionAnsweringPipeline
  from .table_question_answering import TableQuestionAnsweringArgumentHandler, TableQuestionAnsweringPipeline
  from .text2text_generation import SummarizationPipeline, Text2TextGenerationPipeline, TranslationPipeline
  from .text_classification import TextClassificationPipeline
  from .text_generation import TextGenerationPipeline
  from .token_classification import AggregationStrategy, NerPipeline, TokenClassificationArgumentHandler, TokenClassificationPipeline
  from .visual_question_answering import VisualQuestionAnsweringPipeline
  from .zero_shot_classification import ZeroShotClassificationArgumentHandler, ZeroShotClassificationPipeline
  from .zero_shot_image_classification import ZeroShotImageClassificationPipeline
  from .zero_shot_object_detection import ZeroShotObjectDetectionPipeline
label_desc: Use image node_id 2 for calling a global function with example usage: get_task(model, use_auth_token) and returns: task