code: def __init__(
      self,
      num_samples: int = 100,
      max_length: int = 512,
      vocab_size: int = 32000,
  ):
      self.num_samples = num_samples
      self.max_length = max_length
      set_seed(42)
      self.input_ids = torch.randint(
          0,
          vocab_size,
          (num_samples, max_length),
          device=get_current_device(),
      )
      self.attention_mask = torch.ones_like(self.input_ids)
  
imports: import copy
  from contextlib import nullcontext
  from typing import Optional
  import torch
  import torch.distributed
  from torch.testing import assert_close
  from torch.utils.data import Dataset
  import colossalai
  from colossalai.booster import Booster
  from colossalai.booster.plugin import HybridParallelPlugin
  from colossalai.fx import is_compatible_with_meta
  from colossalai.lazy.lazy_init import LazyInitContext
  from colossalai.nn.optimizer import HybridAdam
  from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
  from colossalai.utils import get_current_device, set_seed
  from tests.kit.model_zoo import model_zoo
label_desc: Use image node_id 1 to create a new RandomDataset object from inherited base classes: Dataset with example: obj = RandomDataset(num_samples, max_length, vocab_size)