code: def test_explanation_dashboard_many_columns(self):
      X, y = make_classification(n_features=2000)
  
      # 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)
  
      model_task = ModelTask.Classification
      explainer = MimicExplainer(
          knn, X_train, LGBMExplainableModel, model_task=model_task
      )
      global_explanation = explainer.explain_global(X_test)
  
      ExplanationDashboard(
          explanation=global_explanation,
          model=knn,
          dataset=X_test,
          true_y=y_test,
          classes=classes,
      )
  
file_path: responsible-ai-toolbox/raiwidgets/tests/test_explanation_dashboard.py
imports: import numpy
  import pandas
  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 make_classification
  from sklearn.model_selection import train_test_split
  from raiwidgets import ExplanationDashboard
label_desc: Use image node_id 1 for calling the TestExplanationDashboardDashboard obj's underlying member method code with example usage: obj.test_explanation_dashboard_many_columns() without return types