input: Write a function to create a neural network model that predicts the stock price for the next day based on a given trajectory of stock prices. The function should compile the model and the main block should include an example of predicting the stock price using the given trajectory [10, 10.4, 10.5, 10.6, 10.7, 10.9, 11].
  
  def create_model():
      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'])
  
      return model
      
  if __name__ == "__main__":
      model = create_model()
  
      input_data = np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]])
      predictions = model.predict(input_data)
  
      print(f"Predicted stock price for the next day is: {predictions[0][0]}")
output: def create_model():
      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'])
  
      return model