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 logging
  import os
  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
  import torch.distributed
  from pytorch_lightning.plugins.training_type import DDPPlugin
  from torch.utils.data import DataLoader
  from transformers import AutoConfig, AutoTokenizer, BartForConditionalGeneration, BatchEncoding, RagConfig, RagSequenceForGeneration, RagTokenForGeneration, RagTokenizer, T5ForConditionalGeneration
  from transformers import logging
  from transformers.integrations import is_ray_available
  from callbacks_rag import get_checkpoint_callback, get_early_stopping_callback, Seq2SeqLoggingCallback
  from distributed_pytorch_retriever import RagPyTorchDistributedRetriever
  from utils_rag import calculate_exact_match, flatten_list, get_git_info, is_rag_model, lmap, pickle_save, save_git_info, save_json, set_extra_model_params, Seq2SeqDataset
  from lightning_base import BaseTransformer, add_generic_args, generic_train
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