input: Write a python function to train a logistic regression model and make a probability prediction using the given dataset.
  assert train_and_predict_proba([[1, 20, 0], [1, 24, 0], [0, 30, 1], [1, 35, 0], [1, 32, 0]], [0, 0, 1, 0, 0], [[1, 33, 0]]) == [[0.75984563, 0.24015437]]
  assert train_and_predict_proba([[0, 25, 1], [0, 28, 1], [1, 35, 0], [1, 40, 0], [0, 38, 1]], [1, 1, 0, 0, 1], [[0, 30, 1]]) == [[0.32277682, 0.67722318]]
  assert train_and_predict_proba([[1, 50, 0], [1, 45, 0], [0, 55, 1], [1, 60, 0], [1, 57, 0]], [0, 0, 1, 0, 0], [[1, 58, 0]]) == [[0.75984563, 0.24015437]]
output: def train_and_predict_proba(X_train, y_train, X_test):
      from sklearn.linear_model import LogisticRegression
      
      # Create the logistic regression model
      model = LogisticRegression()
      
      # Fit the model with the training data
      model.fit(X_train, y_train)
      
      # Make probability predictions on the test data
      probabilities = model.predict_proba(X_test)
      
      return probabilities.tolist()