input: Write a function to create a Deep Learning model using Python to predict stock prices. The model should use LSTM layers with dropout for regularization and be trained on a dataset read from a CSV file. The model should be compiled with the Adam optimizer and mean squared error loss.
  
  
  from keras.models import Sequential
  from keras.layers import Dense, LSTM, Dropout
  import numpy as np
  
  def create_stock_price_predictor(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
  
  # Example usage:
  model = create_stock_price_predictor()
  
  
  ### Test Cases:
  
  
  # No specific assert tests provided since the function primarily sets up and trains a model.
  # However, you can test the model's predictions by calling model.predict() on new data.
  
output: def create_stock_price_predictor(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