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: Return an aggregated drift score based on aggregation method defined.
docstr_tok: ["Return","an","aggregated","drift","score","based","on","aggregation","method","defined","."]
code: def reduce_output(
      self, check_result: CheckResult
  ) -> Dict[str, float]:
      """Return an aggregated drift score based on aggregation method defined."""
      feature_importance = check_result.value["feature_importance"]
  
      if check_result.value["columns"]:
          total_mixed_nulls = {
              column: [
                  check_result.value["columns"][column][null_value][
                      "count"
                  ]
                  for null_value in check_result.value["columns"][
                      column
                  ]
              ]
              for column in check_result.value["columns"]
          }
          # If there is only one kind of null value in the column, then the total_mixed_nulls will be 0 for that
          # column
          total_mixed_nulls = {
              column: sum(total_mixed_nulls[column])
              if len(total_mixed_nulls[column]) > 1
              else 0
              for column in total_mixed_nulls
          }
      else:
          total_mixed_nulls = {
              column: 0 for column in check_result.value["columns"]
          }
  
      percent_mismatched = pd.Series(
          {
              column: total_mixed_nulls[column]
              / check_result.value["n_samples"]
              for column in check_result.value["columns"]
          }
      )
      return self.feature_reduce(
          self.aggregation_method,
          percent_mismatched,
          feature_importance,
          "Percent Mixed Nulls",
      )
  
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 3 for calling the MixedNulls obj's underlying member method code with example usage: obj.reduce_output(check_result) and returns: self