input: Write a python function to train an XGBoost classifier on the breast cancer dataset and return the accuracy on the test set.
  assert train_xgboost_classifier() > 0.9
output: def train_xgboost_classifier():
      import xgboost as xgb
      from sklearn.datasets import load_breast_cancer
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      # Load the breast cancer dataset
      data = load_breast_cancer()
      X, y = data.data, data.target
  
      # Split the data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Initialize the XGBoost classifier
      model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss')
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = model.predict(X_test)
  
      # Calculate the accuracy
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy