input: Write a function to create a Python neural network model that takes in a given trajectory of stock prices and predicts the stock price for the next day. The function should return the model and print the predicted stock price for the next day based on the provided input.
  
  Create a neural network model using TensorFlow that accepts a sequence of stock prices and predicts the price for the next day. The input should be a list of recent stock prices and the output should be the predicted price for the subsequent day.
  
  Function Header:
  def create_model(input_data):
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(7, )),
          tf.keras.layers.Dense(1)
      ])
  
      model.compile(loss='mean_squared_error', 
                    optimizer='adam',
                    metrics=['accuracy'])
  
      model.fit(np.array([input_data]), np.array([input_data[-1]]), epochs=10)
  
      return model
  
  assert create_model([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
  assert create_model([15, 15.4, 15.5, 15.6, 15.7, 15.9, 16])
  assert create_model([20, 20.4, 20.5, 20.6, 20.7, 20.9, 21])
output: 
  def create_model(input_data):
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(7, )),
          tf.keras.layers.Dense(1)
      ])
  
      model.compile(loss='mean_squared_error', 
                    optimizer='adam',
                    metrics=['accuracy'])
  
      model.fit(np.array([input_data]), np.array([input_data[-1]]), epochs=10)
  
      predicted_price = model.predict(np.array([input_data]))
      print(predicted_price[0][0])
  
      return model
  