docstr: Constructor
  Designed for free form generation tasks such as Dialogue Response Generation 
  
  Args:
      dataset_split: dataset split
      dialogues_processor: dialogues processor
      tokenizer: tokenizer
      cfg: cfg container for dataset
docstr_tok: ["Constructor","Designed","for","free","form","generation","tasks","such","as","Dialogue","Response","Generation","Args",":","dataset_split",":","dataset","split","dialogues_processor",":","dialogues","processor","tokenizer",":","tokenizer","cfg",":","cfg","container","for","dataset"]
code: def __init__(
      self,
      dataset_split: str,
      dialogues_processor: object,
      tokenizer,
      cfg,
  ):
      """Constructor
      Designed for free form generation tasks such as Dialogue Response Generation
  
      Args:
          dataset_split: dataset split
          dialogues_processor: dialogues processor
          tokenizer: tokenizer
          cfg: cfg container for dataset
      """
      self.cfg = cfg
      self.input_label_type = self.cfg.input_field
      self.output_label_type = self.cfg.output_field
      self.tokenizer = tokenizer
      self.tokenizer.tokenizer.padding_side = "right"
      if not isinstance(dataset_split, str):
          dataset_split = dataset_split[0]
  
      self.features = dialogues_processor.get_dialog_examples(
          dataset_split
      )
      self.features = self.remove_invalid_samples(self.features)
  
      if self.cfg.debug_mode:
          self.features = self.features[:16]
  
file_path: NeMo/nemo/collections/nlp/data/dialogue/dataset/dialogue_gpt_generation_dataset.py
imports: import copy
  import torch
  from nemo.collections.nlp.data.dialogue.dataset.dialogue_dataset import DialogueDataset
label_desc: Use image node_id 1 to create a new DialogueGPTGenerationDataset object from inherited base classes: DialogueDataset with example: obj = DialogueGPTGenerationDataset(dataset_split, dialogues_processor, tokenizer, cfg)