input: Write a function to initialize, fit, and evaluate a logistic regression model using scikit-learn.
  assert logistic_regression_model(X=[[1, 2], [3, 4]], y=[0, 1]) >= 0 and logistic_regression_model(X=[[1, 2], [3, 4]], y=[0, 1]) <= 1
  assert logistic_regression_model(X=[[0.5, 1], [1.5, 2.5]], y=[0, 1]) >= 0 and logistic_regression_model(X=[[0.5, 1], [1.5, 2.5]], y=[0, 1]) <= 1
output: def logistic_regression_model(X, y):
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
  
      # Initialize the logistic regression model
      model = LogisticRegression()
  
      # Fit the model with the provided data
      model.fit(X, y)
  
      # Predict the labels for the provided data
      y_pred = model.predict(X)
  
      # Evaluate the model's accuracy
      accuracy = accuracy_score(y, y_pred)
  
      return accuracy