input: Write a function that creates a Python neural network model to predict the next day's stock price from a given trajectory of stock prices. The function should return the compiled model. The input trajectory is a list of 7 stock prices. Assert the model's prediction against a known output.
  
  
  import numpy as np
  import tensorflow as tf
  
  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
      
  assert np.isclose(create_model().predict(np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]]))[0][0], 11.1)
  assert np.isclose(create_model().predict(np.array([[9, 9.5, 10, 10.3, 10.6, 11, 11.3]]))[0][0], 11.5)
  assert np.isclose(create_model().predict(np.array([[10, 10.2, 10.4, 10.5, 10.7, 10.8, 11]]))[0][0], 11.2)
  assert np.isclose(create_model().predict(np.array([[8, 8.5, 8.7, 9, 9.2, 9.4, 9.6]]))[0][0], 9.8)
  assert np.isclose(create_model().predict(np.array([[7, 7.1, 7.2, 7.3, 7.4, 7.5, 7.6]]))[0][0], 7.7)
  
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