code: def influence_plot(
      results,
      external=True,
      alpha=0.05,
      criterion="cooks",
      size=48,
      plot_alpha=0.75,
      ax=None,
      **kwargs,
  ):
      infl = results.get_influence()
      res = _influence_plot(
          results,
          infl,
          external=external,
          alpha=alpha,
          criterion=criterion,
          size=size,
          plot_alpha=plot_alpha,
          ax=ax,
          **kwargs,
      )
      return res
  
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 12 for calling a global function with example usage: influence_plot(results, external, alpha, criterion, size, plot_alpha, ax) and returns: res