input: Write a python function to load features and labels from numpy files, split the data into training and testing sets, train a logistic regression model, and return the accuracy. 
  assert round(train_logistic_regression("features.npy", "labels.npy"), 2) == 0.85
  assert round(train_logistic_regression("features.npy", "labels.npy"), 2) == 0.90
output: def train_logistic_regression(features_path, labels_path):
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
  
      # Load features and labels from numpy files
      features = np.load(features_path)
      labels = np.load(labels_path)
      
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)
      
      # Train a logistic regression model
      model = LogisticRegression()
      model.fit(X_train, y_train)
      
      # Make predictions and calculate accuracy
      predictions = model.predict(X_test)
      accuracy = accuracy_score(y_test, predictions)
      
      return accuracy