input: Write a function to create a Deep Learning model using Keras to predict stock prices. The function should define the model architecture, read the data from a CSV file, split the data into training and testing sets, reshape the data for input, and train the model.
  
  def predict_stock_price():
      from keras.models import Sequential
      from keras.layers import Dense
      from keras.layers import LSTM
      from keras.layers import 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))
  
  assert predict_stock_price.__code__.co_name == 'predict_stock_price'
  assert predict_stock_price.__code__.co_varnames == ('Sequential', 'Dense', 'LSTM', 'Dropout', 'np', 'time_steps', 'batch_size', 'model', 'dataset', 'data', 'x_train', 'x_test')
  assert predict_stock_price.__code__.co_argcount == 0
  assert predict_stock_price.__globals__.get('__name__') == '__main__'
output: def predict_stock_price():
      from keras.models import Sequential
      from keras.layers import Dense
      from keras.layers import LSTM
      from keras.layers import 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))