code: def validate_matrix(
      matrix,
      exp_total_count,
      exp_total_error,
      features,
      metric=Metrics.ERROR_RATE,
  ):
      assert MATRIX in matrix
      assert CATEGORY1 in matrix
      num_cat1 = len(matrix[CATEGORY1][VALUES])
      if len(features) == 2:
          assert len(matrix[MATRIX]) == num_cat1
          assert CATEGORY2 in matrix
          num_cat2 = len(matrix[CATEGORY2][VALUES])
          assert len(matrix[MATRIX][0]) == num_cat2
          validate_matrix_category(matrix[CATEGORY1])
          validate_matrix_category(
              matrix[CATEGORY2], reverse_order=False
          )
          validate_matrix_metric(
              matrix,
              exp_total_count,
              exp_total_error,
              metric,
              num_cat1,
              num_cat2,
          )
      else:
          assert len(matrix[MATRIX][0]) == num_cat1
          assert len(matrix[MATRIX]) == 1
          validate_matrix_category(matrix[CATEGORY1])
          validate_matrix_metric(
              matrix,
              exp_total_count,
              exp_total_error,
              metric,
              1,
              num_cat1,
          )
  
imports: import numpy
  import pandas
  import pytest
  from erroranalysis._internal.cohort_filter import filter_from_cohort
  from erroranalysis._internal.constants import ARG, COLUMN, METHOD, ROW_INDEX, TRUE_Y, MatrixParams, Metrics, ModelTask, f1_metrics, metric_to_display_name, precision_metrics, recall_metrics
  from erroranalysis._internal.error_analyzer import ModelAnalyzer
  from erroranalysis._internal.matrix_filter import CATEGORY1, CATEGORY2, COUNT, FALSE_COUNT, FN, FP, INTERVAL_MAX, INTERVAL_MIN, MATRIX, METRIC_NAME, METRIC_VALUE, TN, TP, VALUES, bin_data
  from erroranalysis._internal.metrics import get_ordered_classes, is_multi_agg_metric, metric_to_func
  from rai_test_utils.datasets.tabular import create_adult_census_data, create_binary_classification_dataset, create_cancer_data, create_diabetes_data, create_housing_data, create_iris_data, create_simple_titanic_data, create_wine_data
  from rai_test_utils.models.model_utils import create_models_classification, create_models_regression
  from rai_test_utils.models.sklearn import create_kneighbors_classifier, create_titanic_pipeline
  from raiutils.exceptions import UserConfigValidationException
label_desc: Use image node_id 4 for calling a global function with example usage: validate_matrix(matrix, exp_total_count, exp_total_error, features, metric) without return types