input: Write a function to create a Deep Learning model in Python that predicts stock prices using LSTM layers. The function should return the trained model.
  
  from keras.models import Sequential
  from keras.layers import Dense, LSTM, Dropout
  import numpy as np
  
  def predict_stock_price(data_path):
      # 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_path, 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('data.csv'), Sequential)
output: def predict_stock_price(data_path):
      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_path, 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