input: Write a python function to normalize a DataFrame by subtracting the mean and dividing by the standard deviation.
  assert normalize(pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})).round(4).equals(pd.DataFrame({'A': [-1.2247, 0.0, 1.2247], 'B': [-1.2247, 0.0, 1.2247]}).round(4))
  assert normalize(pd.DataFrame({'A': [10, 20, 30], 'B': [40, 50, 60]})).round(4).equals(pd.DataFrame({'A': [-1.2247, 0.0, 1.2247], 'B': [-1.2247, 0.0, 1.2247]}).round(4))
  assert normalize(pd.DataFrame({'A': [0, 0, 0], 'B': [1, 2, 3]})).round(4).equals(pd.DataFrame({'A': [0.0, 0.0, 0.0], 'B': [-1.2247, 0.0, 1.2247]}).round(4))
output: import pandas as pd
  
  def normalize(df):
      return (df - df.mean()) / df.std()