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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docstr: Validate the object given is a list of strings. If null is given return default list of null values.
  
  Parameters
  ----------
  nsl
      Object to validate
  
  Returns
  -------
  set
      Returns list of null values as set object
docstr_tok: ["Validate","the","object","given","is","a","list","of","strings",".","If","null","is","given","return","default","list","of","null","values",".","Parameters","--","--","--","--","--","nsl","Object","to","validate","Returns","--","--","--","-","set","Returns","list","of","null","values","as","set","object"]
code: def _validate_null_string_list(self, nsl) -> set:
      """Validate the object given is a list of strings. If null is given return default list of null values.
  
      Parameters
      ----------
      nsl
          Object to validate
  
      Returns
      -------
      set
          Returns list of null values as set object
      """
      result: set
      if nsl:
          if not isinstance(nsl, Iterable):
              raise DeepchecksValueError(
                  "null_string_list must be an iterable"
              )
          if len(nsl) == 0:
              raise DeepchecksValueError(
                  "null_string_list can't be empty list"
              )
          if any((not isinstance(string, str) for string in nsl)):
              raise DeepchecksValueError(
                  "null_string_list must contain only items of type 'str'"
              )
          result = set(nsl)
      else:
          # Default values
          result = set(DEFAULT_NULL_VALUES)
  
      return result
  
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 4 for calling the MixedNulls obj's underlying member method code with example usage: obj._validate_null_string_list(nsl) and returns: result