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. If the input is an empty list, the function should return None.
  assert create_stock_prediction_model([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]) is not None
  assert create_stock_prediction_model([]) == None
  assert create_stock_prediction_model([9.8, 10.2, 10.3, 10.4, 10.5, 10.7, 10.8]) is not None
output: def create_stock_prediction_model(trajectory):
      if not trajectory:
          return None
      import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
      X = np.array(trajectory[:-1]).reshape(-1, 1)
      y = np.array(trajectory[1:]).reshape(-1, 1)
      model = Sequential()
      model.add(Dense(10, input_dim=1, activation='relu'))
      model.add(Dense(1))
      model.compile(optimizer='adam', loss='mean_squared_error')
      model.fit(X, y, epochs=100, verbose=0)
      return model