input: Write a python function to split data into training and testing sets, fit a model, make predictions, and calculate the confusion matrix.
  assert type(confusion_matrix_func(X, y, model))==tuple
  assert len(confusion_matrix_func(X, y, model))==4
output: from sklearn.model_selection import train_test_split
  from sklearn.metrics import confusion_matrix
  
  def confusion_matrix_func(X, y, model):
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Fit the model
      model.fit(X_train, y_train)
      
      # Make predictions
      y_pred = model.predict(X_test)
      
      # Calculate the confusion matrix
      tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()
      
      return tn, fp, fn, tp