input: Write a function to create a Deep Learning model in Python using Keras to predict stock prices. The model should include LSTM layers and Dropout layers with specified input parameters. The function should also load data from a CSV file and split it into training and testing sets. The data should be reshaped to fit the model's input shape and the model should be trained using the fit method.
  
  
  from keras.models import Sequential
  from keras.layers import Dense, LSTM, Dropout
  import numpy as np
  
  def predict_stock_price(time_steps=60, batch_size=32, epochs=50):
      # 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=epochs, batch_size=batch_size, validation_data=(x_test, x_test))
  
      return model
  
  
  Assert test cases:
  
  assert predict_stock_price() is not None
  
output: def predict_stock_price(time_steps=60, batch_size=32, epochs=50):
      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=epochs, batch_size=batch_size, validation_data=(x_test, x_test))
      return model