class_docstr: This provides the basic information about the model.
  
  # Parameters
  
  description : `str`
      A high-level overview of the model.
      Eg. The model implements a reading comprehension model patterned
          after the proposed model in [Devlin et al, 2018]
          (https://api.semanticscholar.org/CorpusID:52967399), with improvements
          borrowed from the SQuAD model in the transformers project.
          It predicts start tokens and end tokens with a linear layer on top of
          word piece embeddings.
  
  short_description : `str`
      A one-line description of the model.
      Eg. A reading comprehension model patterned after RoBERTa,
          with improvements borrowed from the SQuAD model in the transformers project.
  
  developed_by : `str`
      Person/organization that developed the model. This can be used by all
      stakeholders to infer details pertaining to model development and
      potential conflicts of interest.
  
  contributed_by : `str`
      Person that contributed the model to the repository.
  
  date : `str`
      The date on which the model was contributed. This is useful for all
      stakeholders to become further informed on what techniques and
      data sources were likely to be available during model development.
      Format example: 2020-09-23
  
  version : `str`
      The version of the model, and how it differs from previous versions.
      This is useful for all stakeholders to track whether the model is the
      latest version, associate known bugs to the correct model versions,
      and aid in model comparisons.
  
  model_type : `str`
      The type of the model; the basic architecture. This is likely to be
      particularly relevant for software and model developers, as well as
      individuals knowledgeable about machine learning, to highlight what
      kinds of assumptions are encoded in the system.
      Eg. Naive Bayes Classifier.
  
  paper : `Union[str, Dict, Paper]`
      The paper on which the model is based.
      Format example:
      {
          "title": "Model Cards for Model Reporting (Mitchell et al, 2019)",
          "url": "https://api.semanticscholar.org/CorpusID:52946140",
          "citation": "<BibTex>",
      }
  
  license : `str`
      License information for the model.
  
  contact : `str`
      The email address to reach out to the relevant developers/contributors
      for questions/feedback about the model.
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code: def __init__(
      self,
      description: Optional[str] = None,
      short_description: Optional[str] = None,
      developed_by: Optional[str] = None,
      contributed_by: Optional[str] = None,
      date: Optional[str] = None,
      version: Optional[str] = None,
      model_type: Optional[str] = None,
      paper: Optional[Union[str, Dict, Paper]] = None,
      license: Optional[str] = None,
      contact: Optional[str] = None,
  ):
      self.description = description
      self.short_description = short_description
      self.developed_by = developed_by
      self.contributed_by = contributed_by
      self.date = date
      self.version = version
      self.model_type = model_type
      if isinstance(paper, Paper):
          self.paper = paper
      elif isinstance(paper, Dict):
          self.paper = Paper(**paper)
      else:
          self.paper = Paper(title=paper)
      self.license = license
      self.contact = contact
  
imports: import os
  import logging
  from dataclasses import dataclass
  from typing import Optional, Union, Dict, Any, Callable
  from allennlp.common.from_params import FromParams
  from allennlp.models import Model
  from allennlp.common.checks import ConfigurationError
label_desc: Use image node_id 1 to create a new ModelDetails object from inherited base classes: ModelCardInfo with example: obj = ModelDetails(description, short_description, developed_by, contributed_by, date, version, model_type, paper, license, contact)