class_docstr: Defines the ErrorAnalysisManager for discovering errors in a model.
class_docstr_tok: ["Defines","the","ErrorAnalysisManager","for","discovering","errors","in","a","model","."]
docstr: Add an error analyzer to be computed later.
  
  :param max_depth: The maximum depth of the tree.
  :type max_depth: Optional[int]
  :param num_leaves: The number of leaves in the tree.
  :type num_leaves: Optional[int]
  :param min_child_samples: The minimal number of data required to
      create one leaf.
  :type min_child_samples: Optional[int]
  :param filter_features: One or two features to use for the
      matrix filter.
  :type filter_features: Optional[list]
docstr_tok: ["Add","an","error","analyzer","to","be","computed","later",".",":","param","max_depth",":","The","maximum","depth","of","the","tree",".",":","type","max_depth",":","Optional","[","int","]",":","param","num_leaves",":","The","number","of","leaves","in","the","tree",".",":","type","num_leaves",":","Optional","[","int","]",":","param","min_child_samples",":","The","minimal","number","of","data","required","to","create","one","leaf",".",":","type","min_child_samples",":","Optional","[","int","]",":","param","filter_features",":","One","or","two","features","to","use","for","the","matrix","filter",".",":","type","filter_features",":","Optional","[","list","]"]
code: def add(
      self,
      max_depth: int = 3,
      num_leaves: int = 31,
      min_child_samples: int = 20,
      filter_features: Optional[List] = None,
  ):
      """Add an error analyzer to be computed later.
  
      :param max_depth: The maximum depth of the tree.
      :type max_depth: Optional[int]
      :param num_leaves: The number of leaves in the tree.
      :type num_leaves: Optional[int]
      :param min_child_samples: The minimal number of data required to
          create one leaf.
      :type min_child_samples: Optional[int]
      :param filter_features: One or two features to use for the
          matrix filter.
      :type filter_features: Optional[list]
      """
      model_wrapper_without_metadata = self._analyzer.model
      if (
          model_wrapper_without_metadata is None
          or model_wrapper_without_metadata.model is None
      ):
          raise UserConfigValidationException(
              "Model is required for error analysis"
          )
  
      ea_config = ErrorAnalysisConfig(
          max_depth=max_depth,
          num_leaves=num_leaves,
          min_child_samples=min_child_samples,
          filter_features=filter_features,
      )
      is_duplicate = ea_config.is_duplicate(self._ea_config_list)
  
      if is_duplicate:
          raise DuplicateManagerConfigException(
              "Duplicate config specified for error analysis,"
              "config already added"
          )
      else:
          self._ea_config_list.append(ea_config)
  
imports: import json
  import warnings
  from pathlib import Path
  from typing import Any, List, Optional
  import jsonschema
  import pandas
  from erroranalysis._internal.constants import metric_to_display_name
  from erroranalysis._internal.error_analyzer import ModelAnalyzer
  from erroranalysis._internal.error_report import as_error_report
  from erroranalysis._internal.error_report import json_converter
  from raiutils.exceptions import UserConfigValidationException
  from raiutils.models import ModelTask
  from responsibleai._config.base_config import BaseConfig
  from responsibleai._interfaces import ErrorAnalysisData
  from responsibleai._internal.constants import ErrorAnalysisManagerKeys
  from responsibleai._internal.constants import FileFormats, ListProperties, ManagerNames
  from responsibleai._tools.shared.state_directory_management import DirectoryManager
  from responsibleai.exceptions import ConfigAndResultMismatchException, DuplicateManagerConfigException
  from responsibleai.managers.base_manager import BaseManager
  from responsibleai.utils import _find_features_having_missing_values, _measure_time
label_desc: Use image node_id 2 for calling the ErrorAnalysisManager obj's underlying member method code with example usage: obj.add(max_depth, num_leaves, min_child_samples, filter_features) without return types