code: def telco_churn(quantile=0.5):
      """Returns dataset in format x, [y1, y2]. This dataset
      is useful for demonstrating multi-output model or for
      experimenting with reduction strategy creation.
  
      The data is from hyperparameter optimization experiment with
      Kaggle telco churn dataset.
  
      x: features
      y1: val_loss
      y2: val_f1score
  
      quantile is for transforming the otherwise continuous y variables into
      labels so that higher value is stronger. If set to 0 then original
      continuous will be returned."""
  
      import wrangle
      import pandas as pd
  
      df = pd.read_csv(
          "https://raw.githubusercontent.com/autonomio/examples/master/telco_churn/telco_churn_for_sensitivity.csv"
      )
  
      df = df.drop(
          ["val_acc", "loss", "f1score", "acc", "round_epochs"], 1
      )
  
      for col in df.iloc[:, 2:].columns:
          df = wrangle.col_to_multilabel(df, col)
  
      df = wrangle.df_rename_cols(df)
  
      if quantile > 0:
          y1 = (df.C0 < df.C0.quantile(quantile)).astype(int).values
          y2 = (df.C1 > df.C1.quantile(quantile)).astype(int).values
      else:
          y1 = df.C0.values
          y2 = df.C1.values
  
      x = df.drop(["C0", "C1"], 1).values
  
      return x, [y1, y2]
  
label_desc: Use image node_id 1 for calling a global function with example usage: telco_churn(quantile) and returns: x,  list