class_docstr: This provides information about the training data. If the model was initialized
  from pretrained weights, a link to the pretrained model's model card/training
  data can additionally be provided, if available. Any relevant definitions should
  also be included.
  
  # Parameters
  
  dataset : `Union[str, Dict, Dataset]`
      The name(s) (and link(s), if available) of the dataset(s) used to train
      the model. Optionally, provide a link to the relevant datasheet(s) as well.
      Eg. * Proprietary data from Perspective API; includes comments from online
            forums such as Wikipedia and New York Times, with crowdsourced labels of
            whether the comment is "toxic".
          * "Toxic" is defined as "a rude, disrespectful, or unreasonable comment
            that is likely to make you leave a discussion."
  motivation : `str`
      The reasons for selecting the dataset(s).
      Eg. For the BERT model, document-level corpora were used rather than a
          shuffled sentence-level corpus in order to extract long contiguous sequences.
  preprocessing : `str`
      Eg. Only the text passages were extracted from English Wikipedia;  lists, tables,
          and headers were ignored.
class_docstr_tok: ["This","provides","information","about","the","training","data",".","If","the","model","was","initialized","from","pretrained","weights",",","a","link","to","the","pretrained","model","'s","model","card\/training","data","can","additionally","be","provided",",","if","available",".","Any","relevant","definitions","should","also","be","included",".","#","Parameters","dataset",":","`","Union","[","str",",","Dict",",","Dataset","]","`","The","name","(","s",")","(","and","link","(","s",")",",","if","available",")","of","the","dataset","(","s",")","used","to","train","the","model",".","Optionally",",","provide","a","link","to","the","relevant","datasheet","(","s",")","as","well",".","Eg",".","*","Proprietary","data","from","Perspective","API",";","includes","comments","from","online","forums","such","as","Wikipedia","and","New","York","Times",",","with","crowdsourced","labels","of","whether","the","comment","is","``","toxic","''",".","*","``","Toxic","''","is","defined","as","``","a","rude",",","disrespectful",",","or","unreasonable","comment","that","is","likely","to","make","you","leave","a","discussion",".","''","motivation",":","`","str","`","The","reasons","for","selecting","the","dataset","(","s",")",".","Eg",".","For","the","BERT","model",",","document-level","corpora","were","used","rather","than","a","shuffled","sentence-level","corpus","in","order","to","extract","long","contiguous","sequences",".","preprocessing",":","`","str","`","Eg",".","Only","the","text","passages","were","extracted","from","English","Wikipedia",";","lists",",","tables",",","and","headers","were","ignored","."]
code: def to_dict(self):
      info = {}
      for key, val in self.__dict__.items():
          if val:
              info["training_" + key] = val
      return info
  
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 2 for calling the TrainingData obj's underlying member method code with example usage: obj.to_dict() and returns: info