class_docstr: A single training/test sample. This should contain the input and the label. Is initialized with
  the human readable clear_text. Over the course of data preprocessing, this object is populated
  with tokenized and featurized versions of the data.
docstr: :param id: The unique id of the sample
  :type id: str
  :param clear_text: A dictionary containing various human readable fields (e.g. text, label).
  :type clear_text: dict
  :param tokenized: A dictionary containing the tokenized version of clear text plus helpful meta data: offsets (start position of each token in the original text) and start_of_word (boolean if a token is the first one of a word).
  :type tokenized: dict
  :param features: A dictionary containing features in a vectorized format needed by the model to process this sample.
  :type features: dict
docstr_tok: [":","param","id",":","The","unique","id","of","the","sample",":","type","id",":","str",":","param","clear_text",":","A","dictionary","containing","various","human","readable","fields","(","e.g",".","text",",","label",")",".",":","type","clear_text",":","dict",":","param","tokenized",":","A","dictionary","containing","the","tokenized","version","of","clear","text","plus","helpful","meta","data",":","offsets","(","start","position","of","each","token","in","the","original","text",")","and","start_of_word","(","boolean","if","a","token","is","the","first","one","of","a","word",")",".",":","type","tokenized",":","dict",":","param","features",":","A","dictionary","containing","features","in","a","vectorized","format","needed","by","the","model","to","process","this","sample",".",":","type","features",":","dict"]
code: def __init__(self, id, clear_text, tokenized=None, features=None):
      """
      :param id: The unique id of the sample
      :type id: str
      :param clear_text: A dictionary containing various human readable fields (e.g. text, label).
      :type clear_text: dict
      :param tokenized: A dictionary containing the tokenized version of clear text plus helpful meta data: offsets (start position of each token in the original text) and start_of_word (boolean if a token is the first one of a word).
      :type tokenized: dict
      :param features: A dictionary containing features in a vectorized format needed by the model to process this sample.
      :type features: dict
  
      """
      self.id = id
      self.clear_text = clear_text
      self.features = features
      self.tokenized = tokenized
  
file_path: FARM/farm/data_handler/samples.py
imports: from transformers.models.bert.tokenization_bert import whitespace_tokenize
  from farm.visual.ascii.images import SAMPLE
  import numpy
  import logging
label_desc: Use image node_id 1 to create a new Sample object from inherited base classes: object with example: obj = Sample(id, clear_text, tokenized, features)