input: Write a function to create a neural network model for predicting the next day's stock price based on a given trajectory of stock prices. The function should return the model and should be ready to take the last seven stock prices as input.
  
  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.array_equal(create_model().predict(np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]])), [11.1])
  assert np.array_equal(create_model().predict(np.array([[9, 9.2, 9.3, 9.5, 9.8, 10, 10.2]])), [10.4])
  assert np.array_equal(create_model().predict(np.array([[11, 11.1, 11.3, 11.5, 11.7, 11.9, 12]])), [12.2])
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
  