code: def get_std_diffs(
      X, W, weight=None, weighted=False, numeric_threshold=5
  ):
      """Calculate the inverse probability of treatment weighted standardized
      differences in covariate means between the treatment and the control.
      If weighting is set to 'False', calculate unweighted standardized
      differences. Accepts only continuous and binary numerical variables.
      """
      cont_cols, prop_cols = _get_numeric_vars(
          X, threshold=numeric_threshold
      )
      cols = cont_cols + prop_cols
  
      if len(cols) == 0:
          raise ValueError(
              "No variable passed the test for continuous or binary variables."
          )
  
      treat = W == 1
      contr = W == 0
  
      X_1 = X.loc[treat, cols]
      X_0 = X.loc[contr, cols]
  
      cont_index = np.array([col in cont_cols for col in cols])
      prop_index = np.array([col in prop_cols for col in cols])
  
      std_diffs_cont = np.empty(sum(cont_index))
      std_diffs_prop = np.empty(sum(prop_index))
  
      if weighted:
          assert (
              weight is not None
          ), 'weight should be provided when weighting is set to "True"'
  
          weight_1 = weight[treat]
          weight_0 = weight[contr]
  
          X_1_mean, X_1_var = np.apply_along_axis(
              lambda x: _get_wmean_wvar(x, weight_1), 0, X_1
          )
          X_0_mean, X_0_var = np.apply_along_axis(
              lambda x: _get_wmean_wvar(x, weight_0), 0, X_0
          )
  
      elif not weighted:
          X_1_mean, X_1_var = np.apply_along_axis(
              lambda x: _get_mean_var(x), 0, X_1
          )
          X_0_mean, X_0_var = np.apply_along_axis(
              lambda x: _get_mean_var(x), 0, X_0
          )
  
      X_1_mean_cont, X_1_var_cont = (
          X_1_mean[cont_index],
          X_1_var[cont_index],
      )
      X_0_mean_cont, X_0_var_cont = (
          X_0_mean[cont_index],
          X_0_var[cont_index],
      )
  
      std_diffs_cont = (X_1_mean_cont - X_0_mean_cont) / np.sqrt(
          (X_1_var_cont + X_0_var_cont) / 2
      )
  
      X_1_mean_prop = X_1_mean[prop_index]
      X_0_mean_prop = X_0_mean[prop_index]
  
      std_diffs_prop = (X_1_mean_prop - X_0_mean_prop) / np.sqrt(
          (
              (X_1_mean_prop * (1 - X_1_mean_prop))
              + (X_0_mean_prop * (1 - X_0_mean_prop))
          )
          / 2
      )
  
      std_diffs = np.concatenate(
          [std_diffs_cont, std_diffs_prop], axis=0
      )
      std_diffs_df = pd.DataFrame(std_diffs, index=cols)
  
      return std_diffs_df
  
imports: from matplotlib import pyplot
  import logging
  import numpy
  import pandas
  import seaborn
  from lightgbm import LGBMRegressor
  from ..inference.meta.tmle import TMLELearner
label_desc: Use image node_id 17 for calling a global function with example usage: get_std_diffs(X, W, weight, weighted, numeric_threshold) and returns: std_diffs_df