input: from typing import Tuple
  
  def predict_stock_market_movements() -> Tuple[float, float]:
      """
      Create an AI-based system in Python for predicting stock market movements.
      The function should import necessary libraries, load data, split it into 
      training and testing sets, build a deep learning model, train the model, 
      and evaluate it. 
  
      The function returns a tuple containing the evaluation scores (mse) for 
      the test data.
  
      >>> predict_stock_market_movements()
      (0.0234, 0.0234)
      """
      # import libraries
      import pandas as pd
      import numpy as np
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      # import data
      data = pd.read_csv('data.csv')
  
      # split data into train and test data
      X_train = data.iloc[:, 0]  # Choosing the independent variable
      y_train = data.iloc[:, 1]  # Choosing the dependent variable
      X_test = data.iloc[:, 0]   # Choosing the independent variable
      y_test = data.iloc[:, 1]   # Choosing the dependent variable
  
      # build a deep learning model
      # create a model and add layers
      model = Sequential()
      model.add(Dense(64, input_shape=(1,), activation='relu'))
      model.add(Dense(128, activation='relu'))
      model.add(Dense(64, activation='relu'))
      model.add(Dense(1))
  
      # compile the model
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
  
      # train the model
      model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))
  
      # evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score
output:     # import libraries
      import pandas as pd
      import numpy as np
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      # import data
      data = pd.read_csv('data.csv')
  
      # split data into train and test data
      X_train = data.iloc[:, 0]  # Choosing the independent variable
      y_train = data.iloc[:, 1]  # Choosing the dependent variable
      X_test = data.iloc[:, 0]   # Choosing the independent variable
      y_test = data.iloc[:, 1]   # Choosing the dependent variable
  
      # build a deep learning model
      # create a model and add layers
      model = Sequential()
      model.add(Dense(64, input_shape=(1,), activation='relu'))
      model.add(Dense(128, activation='relu'))
      model.add(Dense(64, activation='relu'))
      model.add(Dense(1))
  
      # compile the model
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
  
      # train the model
      model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))
  
      # evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score