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.
  def create_model(trajectory):
      import numpy as np
      import tensorflow as tf
  
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(len(trajectory),)),
          tf.keras.layers.Dense(1)
      ])
  
      model.compile(loss='mean_squared_error', 
                    optimizer='adam',
                    metrics=['accuracy'])
  
      model.fit(np.array([trajectory]), np.array([[0]]), epochs=50)
  
      return model
  
  assert create_model([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]).predict(np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]])) != []
output: def create_model(trajectory):
      import numpy as np
      import tensorflow as tf
  
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(len(trajectory),)),
          tf.keras.layers.Dense(1)
      ])
  
      model.compile(loss='mean_squared_error', 
                    optimizer='adam',
                    metrics=['accuracy'])
  
      model.fit(np.array([trajectory]), np.array([[0]]), epochs=50)
  
      return model