input: from typing import List
  
  def predict_stock_market_movements(train_data: List[float], test_data: List[float], epochs: int = 20, batch_size: int = 64) -> float:
      """ Predict stock market movements using a deep learning model.
      The function will take train and test data and will train a deep learning model
      with specified epochs and batch size. After training, it will return the evaluation
      score of the model on the test data.
      >>> predict_stock_market_movements([1.0, 2.0, 3.0], [4.0, 5.0, 6.0])
      0.0
      >>> predict_stock_market_movements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], [1.0, 2.8, 3.0, 4.0, 5.0, 2.0])
      0.0
      """
output:     import numpy as np
      from sklearn.neural_network import MLPRegressor
  
      train_data = np.array(train_data).reshape(-1, 1)
      test_data = np.array(test_data).reshape(-1, 1)
  
      model = MLPRegressor(hidden_layer_sizes=(10,), max_iter=epochs, batch_size=batch_size)
      model.fit(train_data, train_data)
  
      predictions = model.predict(test_data)
      score = np.mean(np.abs(predictions - test_data))
  
      return score