input: Write a python function to predict a new customer's churn status using the trained XGBoost model.
  assert predict_customer_churn('C123456', X_train, labelencoder_X_2, onehotencoder, classifier) == 'The customer with ID C123456 will not churn.'
  assert predict_customer_churn('C654321', X_train, labelencoder_X_2, onehotencoder, classifier) == 'The customer with ID C654321 will churn.'
output: def predict_customer_churn(customer_id, X_train, labelencoder_X_2, onehotencoder, classifier):
      import numpy as np
      
      # Find the index of the customer in the dataset
      customer_index = np.where(X_train[:, 0] == customer_id)[0][0]
      
      # Extract the customer's data
      customer_data = X_train[customer_index, 1:]
      
      # Encode and transform the customer's data
      customer_data[1] = labelencoder_X_2.transform([customer_data[1]])[0]
      customer_data = onehotencoder.transform([customer_data]).toarray()
      
      # Make a prediction
      prediction = classifier.predict(customer_data)
      
      if prediction == 0:
          return f'The customer with ID {customer_id} will not churn.'
      else:
          return f'The customer with ID {customer_id} will churn.'