code: def run_solvers(
      model,
      true_params,
      alpha,
      get_l1_slsqp_results,
      get_l1_cvxopt_results,
      print_summaries,
  ):
      """
      Runs the solvers using the specified settings and returns a result string.
      Works the same for any l1 penalized likelihood model.
      """
      results = {}
      #### Train the models
      # Get ML results
      results["results_ML"] = model.fit(method="newton")
      # Get l1 results
      start_params = results["results_ML"].params.ravel(order="F")
      if get_l1_slsqp_results:
          results["results_l1_slsqp"] = model.fit_regularized(
              method="l1",
              alpha=alpha,
              maxiter=1000,
              start_params=start_params,
              retall=True,
          )
      if get_l1_cvxopt_results:
          results["results_l1_cvxopt_cp"] = model.fit_regularized(
              method="l1_cvxopt_cp",
              alpha=alpha,
              maxiter=50,
              start_params=start_params,
              retall=True,
              feastol=1e-5,
          )
  
      return results
  
file_path: statsmodels/statsmodels/examples/l1_demo/demo.py
imports: from optparse import OptionParser
  import statsmodels.api
  import scipy
  from scipy import linalg
  from scipy import stats
label_desc: Use image node_id 3 for calling a global function with example usage: run_solvers(model, true_params, alpha, get_l1_slsqp_results, get_l1_cvxopt_results, print_summaries) and returns: results