class_docstr: Transformation controller which applies multi transformations
  to each data sample.
class_docstr_tok: ["Transformation","controller","which","applies","multi","transformations","to","each","data","sample","."]
docstr: Check dataset
  
  :param textflint.Dataset dataset: the input dataset
docstr_tok: ["Check","dataset",":","param","textflint.Dataset","dataset",":","the","input","dataset"]
code: def prepare(self, dataset):
      r"""
      Check dataset
  
      :param textflint.Dataset dataset: the input dataset
  
      """
      assert isinstance(dataset, Dataset)
      self._check_dataset(dataset)
      self._check_fields()
      set_seed(self.random_seed)
  
imports: from tqdm import tqdm
  from abc import ABC
  from itertools import product
  from ...input.dataset import Dataset
  from ...common.utils.seed import set_seed
  from ...common.utils.logger import logger
  from ...common.preprocess import EnProcessor
  from ...common.utils.load import pkg_class_load, load_module_from_file
  from ..transformation import Transformation, Pipeline
  from ..subpopulation import SubPopulation
  from ...common.settings import TASK_TRANSFORMATION_PATH, ALLOWED_TRANSFORMATIONS, TASK_SUBPOPULATION_PATH, ALLOWED_SUBPOPULATIONS
label_desc: Use image node_id 2 for calling the Generator obj's underlying member method code with example usage: obj.prepare(dataset) without return types