input: Write a function to create a Python neural network model that takes in a given trajectory of stock prices (an array of 7 consecutive prices) and predicts the stock price for the next day. The model should be created using TensorFlow and should return the predicted stock price as a single value. If the input is an array of 7 stock prices, the function should print the predicted stock price for the next day.
  
  
  import numpy as np
  import tensorflow as tf
  
  def predict_next_day_stock_price(stock_prices):
      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
      
      model = create_model()
  
      input_data = np.array([stock_prices])
      predictions = model.predict(input_data)
  
      print(f"Predicted stock price for the next day is: {predictions[0][0]}")
  
  # Test cases
  predict_next_day_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
  
output: def predict_next_day_stock_price(stock_prices):
      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
      
      model = create_model()
  
      input_data = np.array([stock_prices])
      predictions = model.predict(input_data)
  
      print(f"Predicted stock price for the next day is: {predictions[0][0]}")