class_docstr: Aggregation function for a single metric.
class_docstr_tok: ["Aggregation","function","for","a","single","metric","."]
docstr: Initialize the aggregation function.
  
  :param aggfunc: The aggregation function.
  :type aggfunc: callable
docstr_tok: ["Initialize","the","aggregation","function",".",":","param","aggfunc",":","The","aggregation","function",".",":","type","aggfunc",":","callable"]
code: def __init__(self, aggfunc):
      """Initialize the aggregation function.
  
      :param aggfunc: The aggregation function.
      :type aggfunc: callable
      """
      super(_AggFunc, self).__init__(aggfunc)
  
imports: import logging
  import math
  import warnings
  from abc import ABC, abstractmethod
  import numpy
  import pandas
  from sklearn.metrics import multilabel_confusion_matrix
  from erroranalysis._internal.cohort_filter import filter_from_cohort
  from erroranalysis._internal.constants import DIFF, PRED_Y, ROW_INDEX, TRUE_Y, MatrixParams, MetricKeys, Metrics, ModelTask, metric_to_display_name
  from erroranalysis._internal.metrics import get_ordered_classes, is_multi_agg_metric, metric_to_func
  from raiutils.exceptions import UserConfigValidationException
label_desc: Use image node_id 1 to create a new _AggFunc object from inherited base classes: _BaseAggFunc with example: obj = _AggFunc(aggfunc)