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 model should be based on TensorFlow and should be able to handle a sequence of 7 consecutive stock prices as input. The function should return the compiled model.
  
  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().input_shape==(None, 7)
  assert create_stock_prediction_model().output.shape.as_list()==[None, 1]
  assert create_stock_prediction_model().optimizer.get_config()['name']=='adam'
  assert create_stock_prediction_model().metrics[0].name=='accuracy'
  assert create_stock_prediction_model().loss=='mean_squared_error'
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