class_docstr: Base class for Data Processing for all data sources
  
  Data Processor is designed to be Model-independent (but Data-dependent) so that
      - Encourages experimentation with a variety of models             (BERT-style; GPT-style; T5-style),             which have different tokenization/preprocessing requirements
      - Facilitates experiments with a variety of data sources, 
         as data is processed into a common format
      
  Roles 
      1. Processes raw files into Dialogue Input Examples. 
      2. Keeps all possibly relevant information from the raw files, which 
          the Dataset class can then determine which labels to use
docstr: Gets a collection of `InputExample`s for the test set.
code: def get_test_examples(self):
      """Gets a collection of `InputExample`s for the test set."""
      raise NotImplementedError()
  
file_path: NeMo/nemo/collections/nlp/data/dialogue/data_processor/data_processor.py
imports: import random
  from nemo.collections.nlp.data.data_utils.data_preprocessing import DataProcessor
label_desc: Use image node_id 4 for calling the DialogueDataProcessor obj's underlying member method code with example usage: obj.get_test_examples() without return types