input: Write a function to create a Deep Learning model using Keras to predict stock prices. The function should build an LSTM model with specified layers, compile it, and train it on provided stock price data. The function should return the trained 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 predict_stock_price().input_shape == [(None, 60, 1)]
  assert predict_stock_price().optimizer.get_config()['name'] == 'adam'
  assert predict_stock_price().loss == 'mean_squared_error'
  
output: 
  def predict_stock_price():
      from keras.models import Sequential
      from keras.layers import Dense, LSTM, Dropout
      import numpy as np
      
      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')
      
      dataset = np.loadtxt('data.csv', delimiter=',')
      
      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
  