class_docstr: Base class for data converters for sequence classification data sets.
class_docstr_tok: ["Base","class","for","data","converters","for","sequence","classification","data","sets","."]
docstr: Gets a collection of `InputExample`s for the train set.
docstr_tok: ["Gets","a","collection","of","`","InputExample","`","s","for","the","train","set","."]
code: def get_train_examples(self, filename, size=-1):
      """Gets a collection of `InputExample`s for the train set."""
      raise NotImplementedError()
  
imports: import pandas
  import os
  import torch
  from pathlib import Path
  import pickle
  from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
  from torch.utils.data.distributed import DistributedSampler
  from transformers import WEIGHTS_NAME, BertConfig, BertForSequenceClassification, BertTokenizer, XLMConfig, XLMForSequenceClassification, XLMTokenizer, XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer
label_desc: Use image node_id 1 for calling the DataProcessor obj's underlying member method code with example usage: obj.get_train_examples(filename, size) without return types