class_docstr: Detect samples in the test data that appear also in training data.
  
  Parameters
  ----------
  n_samples : int , default: 10_000_000
      number of samples to use for this check.
  n_to_show : int , default: 10
      number of samples that appear in test and training data to show.
  random_state : int, default: 42
      random seed for all check internals.
class_docstr_tok: ["Detect","samples","in","the","test","data","that","appear","also","in","training","data",".","Parameters","--","--","--","--","--","n_samples",":","int",",","default",":","10_000_000","number","of","samples","to","use","for","this","check",".","n_to_show",":","int",",","default",":","10","number","of","samples","that","appear","in","test","and","training","data","to","show",".","random_state",":","int",",","default",":","42","random","seed","for","all","check","internals","."]
code: def __init__(
      self,
      n_samples: int = 10_000_000,
      n_to_show: int = 10,
      random_state: int = 42,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.n_samples = n_samples
      self.n_to_show = n_to_show
      self.random_state = random_state
  
file_path: deepchecks/deepchecks/tabular/checks/train_test_validation/train_test_samples_mix.py
imports: from typing import Any, List
  import pandas
  from pandas.core.dtypes.common import is_integer_dtype
  from deepchecks.core import CheckResult
  from deepchecks.tabular import Context, TrainTestCheck
  from deepchecks.utils.abstracts.train_test_samples_mix import TrainTestSamplesMixAbstract
  from deepchecks.utils.strings import format_percent
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 1 to create a new TrainTestSamplesMix object from inherited base classes: TrainTestCheck, TrainTestSamplesMixAbstract with example: obj = TrainTestSamplesMix(n_samples, n_to_show, random_state)