code: def _plot_leverage_resid2(
      results, influence, alpha=0.05, ax=None, **kwargs
  ):
      from scipy.stats import norm, zscore
  
      fig, ax = utils.create_mpl_ax(ax)
  
      infl = influence
      leverage = infl.hat_matrix_diag
      resid = zscore(infl.resid)
      ax.plot(resid**2, leverage, "o", **kwargs)
      ax.set_xlabel("Normalized residuals**2")
      ax.set_ylabel("Leverage")
      ax.set_title("Leverage vs. Normalized residuals squared")
  
      large_leverage = leverage > _high_leverage(results)
      # norm or t here if standardized?
      cutoff = norm.ppf(1.0 - alpha / 2)
      large_resid = np.abs(resid) > cutoff
      labels = results.model.data.row_labels
      if labels is None:
          labels = lrange(int(results.nobs))
      index = np.where(np.logical_or(large_leverage, large_resid))[0]
      ax = utils.annotate_axes(
          index,
          labels,
          lzip(resid**2, leverage),
          [(0, 5)] * int(results.nobs),
          "large",
          ax=ax,
          ha="center",
          va="bottom",
      )
      ax.margins(0.075, 0.075)
      return fig
  
imports: from statsmodels.compat.pandas import Appender
  from statsmodels.compat.python import lrange, lzip
  import numpy
  import pandas
  from patsy import dmatrix
  from statsmodels.genmod.generalized_estimating_equations import GEE
  from statsmodels.genmod.generalized_linear_model import GLM
  from statsmodels.graphics import utils
  from statsmodels.nonparametric.smoothers_lowess import lowess
  from statsmodels.regression.linear_model import GLS, OLS, WLS
  from statsmodels.sandbox.regression.predstd import wls_prediction_std
  from statsmodels.tools.tools import maybe_unwrap_results
  from ._regressionplots_doc import _plot_added_variable_doc, _plot_ceres_residuals_doc, _plot_influence_doc, _plot_leverage_resid2_doc, _plot_partial_residuals_doc
label_desc: Use image node_id 13 for calling a global function with example usage: _plot_leverage_resid2(results, influence, alpha, ax) and returns: fig