code: def get_dataset(self, type_path) -> Seq2SeqDataset:
      n_obs = self.n_obs[type_path]
      max_target_length = self.target_lens[type_path]
      dataset = Seq2SeqDataset(
          self.tokenizer,
          type_path=type_path,
          n_obs=n_obs,
          max_target_length=max_target_length,
          **self.dataset_kwargs,
      )
      return dataset
  
imports: import argparse
  import copy
  import json
  import logging
  import multiprocessing
  import os
  import random
  import shutil
  import sys
  import time
  from collections import defaultdict
  from pathlib import Path
  from typing import Any, Dict, List, Tuple
  import numpy
  import pytorch_lightning
  import torch
  import torch.distributed
  from datasets import concatenate_datasets, load_from_disk
  from torch.utils.data import DataLoader
  from transformers import AutoConfig, AutoTokenizer, BartForConditionalGeneration, BatchEncoding, DPRConfig, DPRContextEncoder, DPRContextEncoderTokenizerFast, RagConfig, RagSequenceForGeneration, RagTokenForGeneration, RagTokenizer, T5ForConditionalGeneration
  from transformers import logging
  from transformers.integrations import is_ray_available
  from glob import glob
  from callbacks_rag import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
  from kb_encode_utils import add_index, embed_update
  from lightning_base import BaseTransformer, add_generic_args, generic_train
  from pynvml import nvmlDeviceGetCount, nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo, nvmlInit
  from utils_rag import Seq2SeqDataset, calculate_exact_match, get_git_info, is_rag_model, lmap, pickle_save, save_git_info, save_json, set_extra_model_params
label_desc: Use image node_id 14 for calling the GenerativeQAModule obj's underlying member method code with example usage: obj.get_dataset(type_path) and returns: dataset