input: Write a function to create a Deep Learning model using Python's Keras library to predict stock prices. The function should build an LSTM model, compile it, and train it on the provided dataset. The function should also return the trained model. If the input dataset is empty, the function should return None.
  
  assert create_stock_predictor_model(np.array([[1], [2], [3], [4], [5]])) is not None
  assert create_stock_predictor_model(np.array([])) is None
output: def create_stock_predictor_model(dataset):
      if len(dataset) == 0:
          return None
      
      import numpy as np
      from keras.models import Sequential
      from keras.layers import LSTM, Dense
      
      model = Sequential()
      model.add(LSTM(50, input_shape=(1, 1)))
      model.add(Dense(1))
      model.compile(optimizer='adam', loss='mean_squared_error')
      
      dataset = dataset.reshape((-1, 1, 1))
      model.fit(dataset, dataset, epochs=10, batch_size=1, verbose=0)
      
      return model