code: def create_table_one(data, treatment_col, features):
      """Report balance in input features between the treatment and control groups.
  
      References:
          R's tableone at CRAN: https://github.com/kaz-yos/tableone
          Python's tableone at PyPi: https://github.com/tompollard/tableone
  
      Args:
          data (pandas.DataFrame): total or matched sample data
          treatment_col (str): the column name for the treatment
          features (list of str): the column names of features
  
      Returns:
          (pandas.DataFrame): A table with the means and standard deviations in
              the treatment and control groups, and the SMD between two groups
              for the features.
      """
      t1 = pd.pivot_table(
          data[features + [treatment_col]],
          columns=treatment_col,
          aggfunc=[
              lambda x: "{:.2f} ({:.2f})".format(x.mean(), x.std())
          ],
      )
      t1.columns = t1.columns.droplevel(level=0)
      t1["SMD"] = (
          data[features]
          .apply(lambda x: smd(x, data[treatment_col]))
          .round(4)
      )
  
      n_row = pd.pivot_table(
          data[[features[0], treatment_col]],
          columns=treatment_col,
          aggfunc=["count"],
      )
      n_row.columns = n_row.columns.droplevel(level=0)
      n_row["SMD"] = ""
      n_row.index = ["n"]
  
      t1 = pd.concat([n_row, t1], axis=0)
      t1.columns.name = ""
      t1.columns = ["Control", "Treatment", "SMD"]
      t1.index.name = "Variable"
  
      return t1
  
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 2 for calling a global function with example usage: create_table_one(data, treatment_col, features) and returns: t1