code: def _plot_std_diffs(
      diffs_pre, num_unbal_pre, diffs_post, num_unbal_post, bal_tol=0.1
  ):
      fig, ax1 = plt.subplots()
  
      color = "#EA2566"
  
      sds_pre = pd.DataFrame(
          {
              "std_diff": diffs_pre[0],
              "covariate": diffs_pre.index,
              "prepost": "pre",
          }
      )
      sds_post = pd.DataFrame(
          {
              "std_diff": diffs_post[0],
              "covariate": diffs_post.index,
              "prepost": "post",
          }
      )
  
      sds = pd.concat([sds_pre, sds_post], ignore_index=True)
  
      sns.stripplot(
          data=sds, x="std_diff", y="covariate", hue="prepost", ax=ax1
      )
  
      ax1.set_xlabel(
          "Pre/Post Number of unbalanced covariates: {num_unbal_pre}/{num_unbal_post}".format(
              num_unbal_pre=num_unbal_pre, num_unbal_post=num_unbal_post
          ),
          fontsize=14,
      )
      ax1.axvline(
          x=-bal_tol, ymin=0, ymax=1, color=color, linestyle="--", lw=2
      )
      ax1.axvline(
          x=bal_tol, ymin=0, ymax=1, color=color, linestyle="--", lw=2
      )
  
      fig.suptitle("Standardized differences in means", fontsize=16)
  
      return fig
  
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 15 for calling a global function with example usage: _plot_std_diffs(diffs_pre, num_unbal_pre, diffs_post, num_unbal_post, bal_tol) and returns: fig