class_docstr: Detect columns which contain a mix of numerical and string values.
  
  Parameters
  ----------
  columns : Union[Hashable, List[Hashable]] , default: None
      Columns to check, if none are given checks all columns
      except ignored ones.
  ignore_columns : Union[Hashable, List[Hashable]] , default: None
      Columns to ignore, if none given checks based on columns
      variable.
  n_top_columns : int , optional
      amount of columns to show ordered by feature importance (date, index, label are first)
  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_top_columns: int = 10,
      n_samples: int = 10_000_000,
      random_state: int = 42,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.columns = columns
      self.ignore_columns = ignore_columns
      self.n_top_columns = n_top_columns
      self.n_samples = n_samples
      self.random_state = random_state
  
file_path: deepchecks/deepchecks/tabular/checks/data_integrity/mixed_data_types.py
imports: from typing import List, Tuple, Union
  import numpy
  import pandas
  from deepchecks.core import CheckResult, ConditionCategory, ConditionResult
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.tabular.utils.feature_importance import N_TOP_MESSAGE, column_importance_sorter_df
  from deepchecks.utils.dataframes import select_from_dataframe
  from deepchecks.utils.strings import format_list, format_number, format_percent, is_string_column, truncate_string
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 1 to create a new MixedDataTypes object from inherited base classes: SingleDatasetCheck with example: obj = MixedDataTypes(columns, ignore_columns, n_top_columns, n_samples, random_state)