input: Write a function to create a Python neural network model using TensorFlow 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 provide an example input and output prediction.
  
  
  import numpy as np
  import tensorflow as tf
  
  def create_stock_prediction_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
  
  assert create_stock_prediction_model() is not None
  
  stock_prices = [10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]
  model = create_stock_prediction_model()
  prediction = model.predict(np.array([stock_prices]))
  print(f"Predicted stock price for the next day is: {prediction[0][0]}")
  # The output should be a float value representing the predicted stock price for the next day.
  
output: def create_stock_prediction_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