input: Write a python function to train a logistic regression model on given features and labels and return the accuracy score.
  assert train_logistic_regression(df[['Gender', 'Age', 'Married', 'Salary']], df['Outcome']) >= 0.5
output: def train_logistic_regression(features, labels):
      from sklearn.linear_model import LogisticRegression
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from sklearn.metrics import accuracy_score
  
      # Splitting the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.3, random_state=42)
  
      # Standardizing the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Training the logistic regression model
      model = LogisticRegression()
      model.fit(X_train, y_train)
  
      # Predicting the outcomes for the test set
      y_pred = model.predict(X_test)
  
      # Calculating the accuracy score
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy