code: def _calculate_threshold(estimator, importances, threshold):
      """Interpret the threshold value"""
  
      if threshold is None:
          # determine default from estimator
          est_name = estimator.__class__.__name__
          is_l1_penalized = (
              hasattr(estimator, "penalty")
              and estimator.penalty == "l1"
          )
          is_lasso = "Lasso" in est_name
          is_elasticnet_l1_penalized = "ElasticNet" in est_name and (
              (
                  hasattr(estimator, "l1_ratio_")
                  and np.isclose(estimator.l1_ratio_, 1.0)
              )
              or (
                  hasattr(estimator, "l1_ratio")
                  and np.isclose(estimator.l1_ratio, 1.0)
              )
          )
          if is_l1_penalized or is_lasso or is_elasticnet_l1_penalized:
              # the natural default threshold is 0 when l1 penalty was used
              threshold = 1e-5
          else:
              threshold = "mean"
  
      if isinstance(threshold, str):
          if "*" in threshold:
              scale, reference = threshold.split("*")
              scale = float(scale.strip())
              reference = reference.strip()
  
              if reference == "median":
                  reference = np.median(importances)
              elif reference == "mean":
                  reference = np.mean(importances)
              else:
                  raise ValueError("Unknown reference: " + reference)
  
              threshold = scale * reference
  
          elif threshold == "median":
              threshold = np.median(importances)
  
          elif threshold == "mean":
              threshold = np.mean(importances)
  
          else:
              raise ValueError(
                  "Expected threshold='mean' or threshold='median' got %s"
                  % threshold
              )
  
      else:
          threshold = float(threshold)
  
      return threshold
  
imports: from copy import deepcopy
  from numbers import Integral, Real
  import numpy
  from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone
  from ..exceptions import NotFittedError
  from ..utils._param_validation import HasMethods, Interval, Options
  from ..utils._tags import _safe_tags
  from ..utils.metadata_routing import MetadataRouter, MethodMapping, _routing_enabled, process_routing
  from ..utils.metaestimators import available_if
  from ..utils.validation import _num_features, check_is_fitted, check_scalar
  from ._base import SelectorMixin, _get_feature_importances
label_desc: Use image node_id 1 for calling a global function with example usage: _calculate_threshold(estimator, importances, threshold) and returns: threshold