class_docstr: Checks for duplicate samples in the dataset.
  
  Parameters
  ----------
  columns : Union[Hashable, List[Hashable]] , default: None
      List of columns to check, if none given checks
      all columns Except ignored ones.
  ignore_columns : Union[Hashable, List[Hashable]] , default: None
      List of columns to ignore, if none given checks
      based on columns variable.
  n_to_show : int , default: 5
      number of most common duplicated samples to show.
  n_samples : int , default: 10_000_000
      number of samples to use for this check.
  random_state : int, default: 42
      random seed for all check internals.
code: def __init__(
      self,
      columns: Union[Hashable, List[Hashable], None] = None,
      ignore_columns: Union[Hashable, List[Hashable], None] = None,
      n_to_show: int = 5,
      n_samples: int = 10_000_000,
      random_state: int = 42,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.columns = columns
      self.ignore_columns = ignore_columns
      self.n_to_show = n_to_show
      self.n_samples = n_samples
      self.random_state = random_state
  
file_path: deepchecks/deepchecks/tabular/checks/data_integrity/data_duplicates.py
imports: from typing import List, Union
  import numpy
  from deepchecks.core import CheckResult
  from deepchecks.core.errors import DatasetValidationError
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.utils.abstracts.data_duplicates import DataDuplicatesAbstract
  from deepchecks.utils.dataframes import select_from_dataframe
  from deepchecks.utils.strings import format_list, format_percent
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 1 to create a new DataDuplicates object from inherited base classes: SingleDatasetCheck, DataDuplicatesAbstract with example: obj = DataDuplicates(columns, ignore_columns, n_to_show, n_samples, random_state)