input: Write a python function to build, compile, fit, and evaluate a neural network model using TensorFlow and Keras.
  assert build_and_evaluate_model(X_train, y_train, X_test, y_test)[0] < 1.0
  assert build_and_evaluate_model(X_train, y_train, X_test, y_test)[1] > 0.5
output: def build_and_evaluate_model(X_train, y_train, X_test, y_test):
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      
      # Build the model
      model = Sequential([
          Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
          Dense(64, activation='relu'),
          Dense(1, activation='sigmoid')
      ])
      
      # Compile the model
      model.compile(optimizer='adam', 
                    loss='binary_crossentropy', 
                    metrics=['accuracy'])
      
      # Fit the model
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
      
      # Evaluate the model
      loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
      
      return loss, accuracy