input: Write a python function to train a decision tree classifier on a given dataset and return the trained model.
  assert isinstance(train_decision_tree(pd.read_csv('customer_data.csv')), DecisionTreeClassifier)
  assert hasattr(train_decision_tree(pd.read_csv('customer_data.csv')), 'predict')
output: def train_decision_tree(data):
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.model_selection import train_test_split
  
      X = data.drop('target', axis=1)
      y = data['target']
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = DecisionTreeClassifier()
      model.fit(X_train, y_train)
  
      return model