input: Write a python function to train a logistic regression model using the wine dataset from sklearn, make predictions, and return the accuracy of the model.
  assert isinstance(train_and_evaluate_logistic_regression(), float)
  assert 0 <= train_and_evaluate_logistic_regression() <= 1
output: def train_and_evaluate_logistic_regression():
      from sklearn.datasets import load_wine
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
  
      # Load the wine dataset
      data = load_wine()
      X, y = data.data, data.target
  
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train the logistic regression model
      model = LogisticRegression(max_iter=10000, random_state=42)
      model.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = model.predict(X_test)
  
      # Calculate the accuracy of the model
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy