class_docstr: Search for various types of null values, including string representations of null.
  
  Parameters
  ----------
  null_string_list : Iterable[str] , default: None
      List of strings to be considered alternative null representations
  check_nan : bool , default: True
      Whether to add to null list to check also NaN values
  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)
  aggregation_method: t.Optional[str], default: 'max'
      {feature_aggregation_method_argument:2*indent}
  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.
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code: def __init__(
      self,
      null_string_list: Iterable[str] = None,
      check_nan: bool = True,
      columns: Union[Hashable, List[Hashable], None] = None,
      ignore_columns: Union[Hashable, List[Hashable], None] = None,
      n_top_columns: int = 10,
      aggregation_method: Optional[str] = "max",
      n_samples: int = 10_000_000,
      random_state: int = 42,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.null_string_list = null_string_list
      self.check_nan = check_nan
      self.columns = columns
      self.ignore_columns = ignore_columns
      self.n_top_columns = n_top_columns
      self.aggregation_method = aggregation_method
      self.n_samples = n_samples
      self.random_state = random_state
  
imports: import math
  from typing import Dict, Iterable, List, Optional, Union
  import numpy
  import pandas
  from pandas.api.types import is_categorical_dtype
  from deepchecks.core import CheckResult, ConditionCategory, ConditionResult
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.core.reduce_classes import ReduceFeatureMixin
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.tabular._shared_docs import docstrings
  from deepchecks.tabular.utils.feature_importance import N_TOP_MESSAGE
  from deepchecks.tabular.utils.messages import get_condition_passed_message
  from deepchecks.utils.dataframes import select_from_dataframe
  from deepchecks.utils.strings import format_percent, string_baseform
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 1 to create a new MixedNulls object from inherited base classes: SingleDatasetCheck, ReduceFeatureMixin with example: obj = MixedNulls(null_string_list, check_nan, columns, ignore_columns, n_top_columns, aggregation_method, n_samples, random_state)