class_docstr: Structured Model Card class. Store model card as well as methods for loading/downloading/saving model cards.
  
  Please read the following paper for details and explanation on the sections: "Model Cards for Model Reporting" by
  Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer,
  Inioluwa Deborah Raji and Timnit Gebru for the proposal behind model cards. Link: https://arxiv.org/abs/1810.03993
  
  Note: A model card can be loaded and saved to disk.
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code: def __init__(self, **kwargs):
      warnings.warn(
          "The class `ModelCard` is deprecated and will be removed in version 5 of Transformers",
          FutureWarning,
      )
      # Recommended attributes from https://arxiv.org/abs/1810.03993 (see papers)
      self.model_details = kwargs.pop("model_details", {})
      self.intended_use = kwargs.pop("intended_use", {})
      self.factors = kwargs.pop("factors", {})
      self.metrics = kwargs.pop("metrics", {})
      self.evaluation_data = kwargs.pop("evaluation_data", {})
      self.training_data = kwargs.pop("training_data", {})
      self.quantitative_analyses = kwargs.pop(
          "quantitative_analyses", {}
      )
      self.ethical_considerations = kwargs.pop(
          "ethical_considerations", {}
      )
      self.caveats_and_recommendations = kwargs.pop(
          "caveats_and_recommendations", {}
      )
  
      # Open additional attributes
      for key, value in kwargs.items():
          try:
              setattr(self, key, value)
          except AttributeError as err:
              logger.error(
                  f"Can't set {key} with value {value} for {self}"
              )
              raise err
  
imports: import copy
  import json
  import os
  import warnings
  from dataclasses import dataclass
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Union
  import requests
  import yaml
  from huggingface_hub import model_info
  from huggingface_hub.utils import HFValidationError
  from .None import __version__
  from .models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES, MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, MODEL_FOR_CTC_MAPPING_NAMES, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES, MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES, MODEL_FOR_MASKED_LM_MAPPING_NAMES, MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES, MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
  from .training_args import ParallelMode
  from .utils import MODEL_CARD_NAME, cached_file, is_datasets_available, is_offline_mode, is_tf_available, is_tokenizers_available, is_torch_available, logging
label_desc: Use image node_id 1 to create a new ModelCard object  with example: obj = ModelCard()