code: def compute_matrix(
      analyzer,
      features,
      filters,
      composite_filters,
      quantile_binning=False,
      num_bins=BIN_THRESHOLD,
  ):
      """Compute a matrix of metrics for a given set of feature names.
  
      The filters and composite filters are used to filter the data
      prior to computing the matrix.
  
      :param analyzer: The error analyzer.
      :type analyzer: BaseAnalyzer
      :param features: A list of one or two feature names to compute metrics for.
      :type features: list
      :param filters: A list of filters to apply to the data.
      :type filters: list
      :param composite_filters: A list of composite filters to apply to the data.
      :type composite_filters: list
      :param quantile_binning: Whether to use quantile binning.
      :type quantile_binning: bool
      :param num_bins: The number of bins to use for quantile binning.
      :type num_bins: int
      :return: A dictionary representation of the computed matrix which can be
          saved to JSON.
      :rtype: dict
  
      :Example:
  
      An example of running compute_matrix with a filter and a composite
      filter:
  
      >>> from erroranalysis._internal.error_analyzer import ModelAnalyzer
      >>> from erroranalysis._internal.matrix_filter import (
      ...     compute_matrix)
      >>> from erroranalysis._internal.constants import ModelTask
      >>> from sklearn.datasets import load_breast_cancer
      >>> from sklearn.model_selection import train_test_split
      >>> from sklearn import svm
      >>> breast_cancer_data = load_breast_cancer()
      >>> feature_names = breast_cancer_data.feature_names
      >>> X_train, X_test, y_train, y_test = train_test_split(
      ...     breast_cancer_data.data, breast_cancer_data.target,
      ...     test_size=0.5, random_state=0)
      >>> categorical_features = []
      >>> clf = svm.SVC(gamma=0.001, C=100., probability=True,
      ...               random_state=777)
      >>> model = clf.fit(X_train, y_train)
      >>> model_task = ModelTask.CLASSIFICATION
      >>> analyzer = ModelAnalyzer(model, X_test, y_test, feature_names,
      ...                          categorical_features, model_task=model_task)
      >>> filters = [{'arg': [23.85], 'column': 'mean radius',
      ...             'method': 'less and equal'}]
      >>> composite_filters = [{'compositeFilters':
      ...                      [{'compositeFilters':
      ...                       [{'arg': [13.45, 22.27],
      ...                         'column': 'mean radius',
      ...                         'method': 'in the range of'},
      ...                        {'arg': [10.88, 24.46],
      ...                         'column': 'mean texture',
      ...                         'method': 'in the range of'}],
      ...                        'operation': 'and'}],
      ...                      'operation': 'or'}]
      >>> matrix = compute_matrix(analyzer, ['mean radius', 'mean texture'],
      ...                         filters, composite_filters)
      """
      filtered_df = filter_from_cohort(
          analyzer, filters, composite_filters
      )
      return compute_matrix_on_dataset(
          analyzer, features, filtered_df, quantile_binning, num_bins
      )
  
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 3 for calling a global function with example usage: compute_matrix(analyzer, features, filters, composite_filters, quantile_binning, num_bins) and returns: compute_matrix_on_dataset