code: def aggregation_result(
      self, aggregation_result: Dict[str, Any]
  ) -> Dict[str, Any]:
      if self.id in aggregation_result:
          aggregation_result = aggregation_result[self.id]
  
      aggregation_result = unflatten_dict(aggregation_result)
      results = {
          "covered_records": aggregation_result.pop("covered_records"),
          "annotated_covered_records": aggregation_result.pop(
              "annotated_covered_records"
          ),
      }
  
      all_correct = []
      all_incorrect = []
      all_precision = []
      for label, metrics in aggregation_result.items():
          correct = metrics.get("correct_records", 0)
          incorrect = metrics.get("incorrect_records", 0)
          annotated = correct + incorrect
          metrics["annotated"] = annotated
          if annotated > 0:
              precision = correct / annotated
              metrics["precision"] = precision
              all_precision.append(precision)
  
          all_correct.append(correct)
          all_incorrect.append(incorrect)
          results[self._decode_label_name(label)] = metrics
  
      results["correct_records"] = sum(all_correct)
      results["incorrect_records"] = sum(all_incorrect)
      if len(all_precision) > 0:
          results["precision"] = sum(all_precision) / len(all_precision)
  
      return results
  
file_path: argilla/src/argilla/server/daos/backend/metrics/text_classification.py
imports: import dataclasses
  from typing import Any, Dict, List, Optional
  from argilla.server.daos.backend.metrics.base import ElasticsearchMetric, TermsAggregation
  from argilla.server.daos.backend.query_helpers import aggregations, filters
  from argilla.server.helpers import unflatten_dict
label_desc: Use image node_id 4 for calling the LabelingRulesMetric obj's underlying member method code with example usage: obj.aggregation_result(aggregation_result) and returns: results