input: Write a function to create a Deep Learning model in Python that predicts stock prices using LSTM layers. The function should read the stock price data from a CSV file, preprocess the data, and train the model. The function should return the trained model. The input data should be divided into training and testing sets, and the model should be trained for 50 epochs with a batch size of 32.
  
  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(file_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(file_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)
  assert predict_stock_price('data.csv').optimizer.get_config()['name'] == 'adam'
  assert predict_stock_price('data.csv').loss == 'mean_squared_error'
output: def predict_stock_price(file_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(file_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