code: def test_error_analysis_many_rows(self):
      X, y = make_classification(n_samples=110000)
  
      # Split data into train and test
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.2, random_state=0
      )
      classes = np.unique(y_train).tolist()
      feature_names = [
          "col" + str(i) for i in list(range(X_train.shape[1]))
      ]
      X_train = pd.DataFrame(X_train, columns=feature_names)
      X_test = pd.DataFrame(X_test, columns=feature_names)
      knn = sklearn.neighbors.KNeighborsClassifier()
      knn.fit(X_train, y_train)
      ErrorAnalysisDashboard(
          model=knn, dataset=X_test, true_y=y_test, classes=classes
      )
  
imports: import numpy
  import pandas
  import shap
  import sklearn
  from interpret.ext.blackbox import MimicExplainer
  from interpret.ext.glassbox import LGBMExplainableModel
  from interpret_community.common.constants import ModelTask
  from sklearn.datasets import load_iris, make_classification
  from sklearn.model_selection import train_test_split
  from erroranalysis._internal.constants import Metrics, metric_to_display_name
  from erroranalysis._internal.surrogate_error_tree import DEFAULT_MAX_DEPTH, DEFAULT_MIN_CHILD_SAMPLES, DEFAULT_NUM_LEAVES
  from raiwidgets import ErrorAnalysisDashboard
  from raiwidgets.explanation_constants import WidgetRequestResponseConstants
label_desc: Use image node_id 2 for calling the TestErrorAnalysisDashboard obj's underlying member method code with example usage: obj.test_error_analysis_many_rows() without return types