code: def _describe_rules(self, estimator, X, estimator_name=""):
      rules_by_sample = estimator.predict_rules(X)
      rules_active = estimator.M.w
      active_rules_by_sample = rules_by_sample[
          :, rules_active.astype(bool)
      ]
      sample_coverage = active_rules_by_sample.mean(axis=0).tolist()
      rule_list = []
      for r in zip(estimator.rules(as_str=False), sample_coverage):
          rule_list.append({"rule": r[0], "coverage": r[1]})
  
      # For DNF rules, a sample is covered if *any* rule applies
      total_coverage = active_rules_by_sample.max(axis=1).mean()
  
      if estimator_name == "Overlap":
          return_str = (
              f"{estimator_name} Rules: Found {len(rule_list)} rule(s), "
              f"covering {total_coverage:.1%} of samples in the Support set\n"
          )
      else:
          return_str = f"{estimator_name} Rules: Found {len(rule_list)} rule(s), covering {total_coverage:.1%} of samples\n"
      for idx, r in enumerate(rule_list):
          if idx == 0:
              prefix = "   "
          else:
              prefix = "OR "
  
          return_str += f"\t {prefix}Rule #{idx}: "
          return_str += self._print_rule(r["rule"])
          return_str += (
              f"\t\t [Covers {r['coverage']:.1%} of samples]\n"
          )
  
      return return_str
  
imports: from dataclasses import asdict, dataclass
  from typing import Dict, List, Optional, Union
  import numpy
  import pandas
  from sklearn.base import BaseEstimator
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import GridSearchCV, cross_val_predict
  from dowhy.causal_refuters.overrule.ruleset import BCSRulesetEstimator
  from dowhy.causal_refuters.overrule.utils import fatom
label_desc: Use image node_id 10 for calling the OverruleAnalyzer obj's underlying member method code with example usage: obj._describe_rules(estimator, X, estimator_name) and returns: return_str