input: Write a python function to train a neural network model using TensorFlow for XOR operation.
  assert neural_network_XOR()[1] == 1.0
output: import tensorflow as tf
  import numpy as np
  
  def neural_network_XOR():
      # XOR input and output
      X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
      y = np.array([[0], [1], [1], [0]])
  
      # Define the model
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(2, input_dim=2, activation='relu'),
          tf.keras.layers.Dense(1, activation='sigmoid')
      ])
  
      # Compile the model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
      # Train the model
      model.fit(X, y, epochs=1000, verbose=0)
  
      # Evaluate the model
      loss, accuracy = model.evaluate(X, y, verbose=0)
  
      return model, accuracy