input: Write a function to create a Deep Learning model using Keras in Python that predicts stock prices. The function should define the necessary layers, compile the model, and prepare the training and testing data. If successful, the function should return the compiled model.
  
  def predict_stock_price():
      from keras.models import Sequential
      from keras.layers import Dense, LSTM, Dropout
      import numpy as np
      
      # define constants
      time_steps = 60
      batch_size = 32
      
      # build the model
      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')
      
      # read the data 
      dataset = np.loadtxt('data.csv', delimiter=',')
      data = dataset[:, 0:1]
      
      # create train and test data
      x_train = data[:int(len(data)*0.8)]
      x_test = data[int(len(data)*0.8):]
      
      # reshape the data for input format
      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))
      
      # train the model
      model.fit(x_train, x_train, epochs=50, batch_size=batch_size, validation_data=(x_test, x_test))
      
      return model
  
  assert isinstance(predict_stock_price(), Sequential) == True
  assert predict_stock_price()._is_compiled == True
output: def predict_stock_price():
      from keras.models import Sequential
      from keras.layers import Dense, LSTM, Dropout
      import numpy as np
      
      # define constants
      time_steps = 60
      batch_size = 32
      
      # build the model
      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')
      
      # read the data 
      dataset = np.loadtxt('data.csv', delimiter=',')
      data = dataset[:, 0:1]
      
      # create train and test data
      x_train = data[:int(len(data)*0.8)]
      x_test = data[int(len(data)*0.8):]
      
      # reshape the data for input format
      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))
      
      # train the model
      model.fit(x_train, x_train, epochs=50, batch_size=batch_size, validation_data=(x_test, x_test))
      
      return model