input: Write a function to create a Deep Learning model using Keras to predict stock prices. The function should define the model architecture using LSTM layers, compile the model with the Adam optimizer and mean squared error loss, and train the model using the provided data. The function should return the trained model. If the input data is empty, the function should return None.
  from keras.models import Sequential
  from keras.layers import Dense, LSTM, Dropout
  import numpy as np
  
  def predict_stock_price(dataset):
      if len(dataset) == 0:
          return None
  
      time_steps = 60
      batch_size = 32
  
      model = Sequential()
      model.add(LSTM(units=50, return_sequences=True, input_shape=(time_steps, 1)))
      model.add(Dropout(0.2))
      model.add(LSTM(units=50, return_sequences=True))
      model.add(Dropout(0.2))
      model.add(LSTM(units=50))
      model.add(Dropout(0.2))
      model.add(Dense(units=1))
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      data = dataset[:, 0:1]
      x_train = data[:int(len(data)*0.8)]
      x_test = data[int(len(data)*0.8):]
      x_train = np.reshape(x_train, (x_train.shape[0], time_steps, 1))
      x_test = np.reshape(x_test, (x_test.shape[0], time_steps, 1))
  
      model.fit(x_train, x_train, epochs=50, batch_size=batch_size, validation_data=(x_test, x_test))
      return model
  
  assert predict_stock_price(np.array([])) is None
  assert predict_stock_price(np.array([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]])) is not None
output: def predict_stock_price(dataset):
      if len(dataset) == 0:
          return None
  
      time_steps = 60
      batch_size = 32
  
      model = Sequential()
      model.add(LSTM(units=50, return_sequences=True, input_shape=(time_steps, 1)))
      model.add(Dropout(0.2))
      model.add(LSTM(units=50, return_sequences=True))
      model.add(Dropout(0.2))
      model.add(LSTM(units=50))
      model.add(Dropout(0.2))
      model.add(Dense(units=1))
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      data = dataset[:, 0:1]
      x_train = data[:int(len(data)*0.8)]
      x_test = data[int(len(data)*0.8):]
      x_train = np.reshape(x_train, (x_train.shape[0], time_steps, 1))
      x_test = np.reshape(x_test, (x_test.shape[0], time_steps, 1))
  
      model.fit(x_train, x_train, epochs=50, batch_size=batch_size, validation_data=(x_test, x_test))
      return model