code: def __init__(
      self,
      dataset_length: int,
      sequence_length: int,
      vocab_size: int,
      n_class: int,
  ):
      self._sequence_length = sequence_length
      self._vocab_size = vocab_size
      self._n_class = n_class
      self._dataset_length = dataset_length
      self._datas = torch.randint(
          low=0,
          high=self._vocab_size,
          size=(
              self._dataset_length,
              self._sequence_length,
          ),
          dtype=torch.long,
      )
      self._labels = torch.randint(
          low=0,
          high=self._n_class,
          size=(self._dataset_length, 1),
          dtype=torch.long,
      )
  
file_path: ColossalAI/examples/language/bert/benchmark.py
imports: import argparse
  import torch
  from benchmark_utils import benchmark
  from torch.utils.data import DataLoader, Dataset
  from transformers import AlbertConfig, AlbertForSequenceClassification, BertConfig, BertForSequenceClassification, get_linear_schedule_with_warmup
  import colossalai
  from colossalai.booster import Booster
  from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin
  from colossalai.cluster import DistCoordinator
  from colossalai.nn.optimizer import HybridAdam
label_desc: Use image node_id 1 to create a new RandintDataset object from inherited base classes: Dataset with example: obj = RandintDataset(dataset_length, sequence_length, vocab_size, n_class)