input: 
  def predict_stock_market(data: pd.DataFrame, batch_size: int, epochs: int) -> float:
      """
      Predicts stock market movements using a deep learning model. The function takes a DataFrame containing training data, a batch size for training, and the number of epochs to train for. It returns the mean squared error of the model on the test data.
      
      >>> data = pd.read_csv('data.csv')
      >>> predict_stock_market(data, 64, 20)
      0.012
      """
      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
  
      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))
  
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
  
      model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_test, y_test))
  
      score = model.evaluate(X_test, y_test, verbose=0)
      return score[1]
  
output:     X_train = X_train.values.reshape(-1, 1)
      y_train = y_train.values.reshape(-1, 1)
      X_test = X_test.values.reshape(-1, 1)
      y_test = y_test.values.reshape(-1, 1)