input: Write a function to create a Deep Learning model using Keras to predict stock prices. The function should build an LSTM-based model, compile it, and train it using provided stock data. If the training is successful, return the trained model; otherwise, return None.
  
  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
      try:
          model.fit(x_train, x_train, epochs=50, batch_size=batch_size, validation_data=(x_test, x_test))
          return model
      except:
          return None
  
  assert predict_stock_price('data.csv') is not None
  assert predict_stock_price('nonexistent.csv') is None
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')
  
      try:
          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
      except:
          return None