code: def smd(feature, treatment):
      """Calculate the standard mean difference (SMD) of a feature between the
      treatment and control groups.
  
      The definition is available at
      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3144483/#s11title
  
      Args:
          feature (pandas.Series): a column of a feature to calculate SMD for
          treatment (pandas.Series): a column that indicate whether a row is in
                                     the treatment group or not
  
      Returns:
          (float): The SMD of the feature
      """
      t = feature[treatment == 1]
      c = feature[treatment == 0]
      return (t.mean() - c.mean()) / np.sqrt(0.5 * (t.var() + c.var()))
  
imports: import argparse
  import logging
  import sys
  import numpy
  import pandas
  from sklearn.neighbors import NearestNeighbors
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils import check_random_state
label_desc: Use image node_id 1 for calling a global function with example usage: smd(feature, treatment) and returns: unknown