input: Write a function to create a deep learning model using Keras to predict stock prices. The function should define a Sequential model with three LSTM layers, each followed by a Dropout layer, and a final Dense layer. The function should also compile the model with the Adam optimizer and mean squared error loss function, and train it using the given training and test data. The function should return the trained model.
  from keras.models import Sequential
  from keras.layers import Dense
  from keras.layers import LSTM
  from keras.layers import Dropout
  import numpy as np
  
  def predict_stock_price_model(data_file):
      # 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_file, 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_model('data.csv') is not None
  assert predict_stock_price_model('data.csv').summary() is not None
output: 
  def predict_stock_price_model(data_file):
      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_file, 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
  