code: def __init__(self, batch_size: int):
      datasets = load_data_lm()
      self.data = datasets
  
      train_df = pd.DataFrame(
          data={
              "x": integerify(datasets["train"]),
              "y_target": integerify(datasets["train"]),
          }
      )
      val_df = pd.DataFrame(
          data={
              "x": integerify(datasets["valid"]),
              "y_target": integerify(datasets["valid"]),
          }
      )
      test_df = pd.DataFrame(
          data={
              "x": integerify(datasets["test"]),
              "y_target": integerify(datasets["test"]),
          }
      )
  
      super().__init__(
          train_set=DataFrameDataset(train_df),
          train_batch_size=batch_size,
          val_set=DataFrameDataset(val_df),
          val_batch_size=batch_size,
          test_set=DataFrameDataset(test_df),
          test_batch_size=batch_size,
      )
  
file_path: transfer-nlp/experiments/transformers/dataset.py
imports: import random
  import numpy
  import pandas
  import torch
  from pytorch_pretrained_bert import cached_path, BertTokenizer
  from transfer_nlp.loaders.loaders import DatasetSplits, DataFrameDataset
  from transfer_nlp.plugins.config import register_plugin
label_desc: Use image node_id 1 to create a new BertLMTuningDataset object from inherited base classes: DatasetSplits with example: obj = BertLMTuningDataset(batch_size)