code: def get_raw_dataset(dataset_name, output_path, seed, local_rank):
      if "Dahoas/rm-static" in dataset_name:
          return raw_datasets.DahoasRmstaticDataset(
              output_path, seed, local_rank, dataset_name
          )
      elif "Dahoas/full-hh-rlhf" in dataset_name:
          return raw_datasets.DahoasFullhhrlhfDataset(
              output_path, seed, local_rank, dataset_name
          )
      elif "Dahoas/synthetic-instruct-gptj-pairwise" in dataset_name:
          return (
              raw_datasets.DahoasSyntheticinstructgptjpairwiseDataset(
                  output_path, seed, local_rank, dataset_name
              )
          )
      elif "yitingxie/rlhf-reward-datasets" in dataset_name:
          return raw_datasets.YitingxieRlhfrewarddatasetsDataset(
              output_path, seed, local_rank, dataset_name
          )
      elif "openai/webgpt_comparisons" in dataset_name:
          return raw_datasets.OpenaiWebgptcomparisonsDataset(
              output_path, seed, local_rank, dataset_name
          )
      elif "stanfordnlp/SHP" in dataset_name:
          return raw_datasets.StanfordnlpSHPDataset(
              output_path, seed, local_rank, dataset_name
          )
      elif "pvduy/sharegpt_alpaca_oa_vicuna_format" in dataset_name:
          return raw_datasets.PvduySharegptalpacaoavicunaformatDataset(
              output_path, seed, local_rank, dataset_name
          )
      elif "wangrui6/Zhihu-KOL" in dataset_name:
          return raw_datasets.Wangrui6ZhihuKOLDataset(
              output_path, seed, local_rank, dataset_name
          )
      elif "Cohere/miracl-zh-queries-22-12" in dataset_name:
          return raw_datasets.CohereMiraclzhqueries2212Dataset(
              output_path, seed, local_rank, dataset_name
          )
      elif "Hello-SimpleAI/HC3-Chinese" in dataset_name:
          return raw_datasets.HelloSimpleAIHC3ChineseDataset(
              output_path, seed, local_rank, dataset_name
          )
      elif "mkqa-Chinese" in dataset_name:
          return raw_datasets.MkqaChineseDataset(
              output_path, seed, local_rank, "mkqa"
          )
      elif "mkqa-Japanese" in dataset_name:
          return raw_datasets.MkqaJapaneseDataset(
              output_path, seed, local_rank, "mkqa"
          )
      elif "Cohere/miracl-ja-queries-22-12" in dataset_name:
          return raw_datasets.CohereMiracljaqueries2212Dataset(
              output_path, seed, local_rank, dataset_name
          )
      elif "lmqg/qg_jaquad" in dataset_name:
          return raw_datasets.LmqgQgjaquadDataset(
              output_path, seed, local_rank, dataset_name
          )
      elif "lmqg/qag_jaquad" in dataset_name:
          return raw_datasets.LmqgQagjaquadDataset(
              output_path, seed, local_rank, dataset_name
          )
      elif "local/jsonfile" in dataset_name:
          chat_path = os.path.abspath(
              os.path.join(
                  os.path.dirname(__file__),
                  os.path.pardir,
                  os.path.pardir,
                  os.path.pardir,
              )
          )
          if not (
              os.path.isfile(chat_path + "/data/train.json")
              and os.path.isfile(chat_path + "/data/eval.json")
          ):
              raise RuntimeError(
                  f"Please check both the train.json and eval.json files in your applications/DeepSpeed-Chat/data directory."
              )
          return raw_datasets.LocalJsonFileDataset(
              output_path, seed, local_rank, dataset_name, chat_path
          )
      else:
          raise RuntimeError(
              f"We do not have configs for dataset {dataset_name}, but you can add it by yourself in raw_datasets.py."
          )
  
imports: import torch
  from torch.utils.data import Dataset, Subset, ConcatDataset
  from torch.nn.utils.rnn import pad_sequence
  import torch.nn.functional
  from datasets import load_dataset
  import numpy
  import os
  import hashlib
  from itertools import chain
  from dschat.utils.data import raw_datasets
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 1 for calling a global function with example usage: get_raw_dataset(dataset_name, output_path, seed, local_rank) and returns: raw_datasets, raw_datasets, raw_datasets, raw_datasets, raw_datasets, raw_datasets, raw_datasets, raw_datasets, raw_datasets, raw_datasets, raw_datasets, raw_datasets, raw_datasets, raw_datasets, raw_datasets, raw_datasets