code: def __init__(self, length, batch_size, seq_len, vocab_size):
      self.length = length
      self.batch_size = batch_size
      self.seq_len = seq_len
      self.vocab_size = vocab_size
  
imports: import math
  import os
  import time
  from itertools import chain
  import datasets
  import torch
  import torch.distributed
  import transformers.utils.logging
  from accelerate.utils import set_seed
  from context import barrier_context
  from datasets import load_dataset
  from packaging import version
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  from transformers import CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoTokenizer, GPT2Tokenizer, OPTForCausalLM, SchedulerType, default_data_collator, get_scheduler
  from transformers.utils.versions import require_version
  import colossalai
  from colossalai.legacy.context import ParallelMode
  from colossalai.legacy.core import global_context
  from colossalai.legacy.tensor import ProcessGroup
  from colossalai.legacy.utils import get_dataloader
  from colossalai.logging import disable_existing_loggers, get_dist_logger
  from colossalai.nn.optimizer import HybridAdam
  from colossalai.utils import get_current_device
  from colossalai.zero import GeminiOptimizer
label_desc: Use image node_id 1 to create a new DummyDataloader object  with example: obj = DummyDataloader(length, batch_size, seq_len, vocab_size)