input: Write a function to create a Deep Learning Program in Python that predicts stock prices using an LSTM model. The function should define the model architecture, compile the model, and train it using provided data. If the input data is empty, the function should return an error message.
  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(dataset):
      if len(dataset) == 0:
          return "Error: Input data is empty."
      
      # 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')
  
      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 trained successfully."
  
  # Test cases
  dataset1 = np.array([[1], [2], [3], [4], [5]])
  dataset2 = np.array([])
  
  assert predict_stock_price(dataset1) == "Model trained successfully."
  assert predict_stock_price(dataset2) == "Error: Input data is empty."
output: def predict_stock_price(dataset):
      if len(dataset) == 0:
          return "Error: Input data is empty."
      
      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')
  
      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 trained successfully."