code: def test_plot():
      # loading dataset
      data = pycaret.datasets.get_data("juice")
      assert isinstance(data, pd.DataFrame)
  
      # init setup
      pycaret.classification.setup(
          data,
          target="Purchase",
          log_experiment=True,
          log_plots=True,
          html=False,
          session_id=123,
          fold=2,
          n_jobs=1,
      )
  
      model = pycaret.classification.create_model(
          "rf", max_depth=2, n_estimators=5
      )
  
      exp = pycaret.classification.ClassificationExperiment()
      available_plots = exp._available_plots
  
      for plot in available_plots:
          pycaret.classification.plot_model(model, plot=plot)
  
      models = [
          pycaret.classification.create_model("et"),
          pycaret.classification.create_model("xgboost"),
      ]
  
      # no pfi due to dependency hell
      available_shap = [
          "summary",
          "correlation",
          "reason",
          "pdp",
          "msa",
      ]
  
      for model in models:
          for plot in available_shap:
              pycaret.classification.interpret_model(model, plot=plot)
              pycaret.classification.interpret_model(
                  model,
                  plot=plot,
                  X_new_sample=data.drop("Purchase", axis=1).iloc[:10],
              )
  
      assert 1 == 1
  
imports: import pandas
  import pytest
  import pycaret.classification
  import pycaret.datasets
label_desc: Use image node_id 1 for calling a global function with example usage: test_plot() without return types