code: def evaluate(self, predictions: List[Dict]):
      evaluated_ids = set()
  
      recall_tracker = RecallTracker(self.topk)
  
      for pred in predictions:
          cur_id = f"{pred['image_id']}_{pred['sentence_id']}"
          if cur_id in evaluated_ids:
              print(
                  "Warning, multiple predictions found for sentence"
                  f"{pred['sentence_id']} in image {pred['image_id']}"
              )
              continue
  
          # Skip the sentences with no valid phrase
          if cur_id not in self.all_ids:
              if len(pred["boxes"]) != 0:
                  print(
                      f"Warning, in image {pred['image_id']} we were not expecting predictions "
                      f"for sentence {pred['sentence_id']}. Ignoring them."
                  )
              continue
  
          evaluated_ids.add(cur_id)
  
          pred_boxes = pred["boxes"]
          if str(pred["image_id"]) not in self.imgid2sentences:
              raise RuntimeError(f"Unknown image id {pred['image_id']}")
          if (
              not 0
              <= int(pred["sentence_id"])
              < len(self.imgid2sentences[str(pred["image_id"])])
          ):
              raise RuntimeError(
                  f"Unknown sentence id {pred['sentence_id']}"
                  f" in image {pred['image_id']}"
              )
          target_sentence = self.imgid2sentences[str(pred["image_id"])][
              int(pred["sentence_id"])
          ]
  
          phrases = self.imgid2sentences[str(pred["image_id"])][
              int(pred["sentence_id"])
          ]
          if len(pred_boxes) != len(phrases):
              raise RuntimeError(
                  f"Error, got {len(pred_boxes)} predictions, expected {len(phrases)} "
                  f"for sentence {pred['sentence_id']} in image {pred['image_id']}"
              )
  
          for cur_boxes, phrase in zip(pred_boxes, phrases):
              target_boxes = self.imgid2boxes[str(pred["image_id"])][
                  phrase["phrase_id"]
              ]
  
              ious = box_iou(
                  np.asarray(cur_boxes), np.asarray(target_boxes)
              )
              for k in self.topk:
                  maxi = 0
                  if k == -1:
                      maxi = ious.max()
                  else:
                      assert k > 0
                      maxi = ious[:k].max()
                  if maxi >= self.iou_thresh:
                      recall_tracker.add_positive(k, "all")
                      for phrase_type in phrase["phrase_type"]:
                          recall_tracker.add_positive(k, phrase_type)
                  else:
                      recall_tracker.add_negative(k, "all")
                      for phrase_type in phrase["phrase_type"]:
                          recall_tracker.add_negative(k, phrase_type)
  
      if len(evaluated_ids) != len(self.all_ids):
          print(
              "ERROR, the number of evaluated sentence doesn't match. Missing predictions:"
          )
          un_processed = set(self.all_ids) - evaluated_ids
          for missing in un_processed:
              img_id, sent_id = missing.split("_")
              print(f"\t sentence {sent_id} in image {img_id}")
          raise RuntimeError("Missing predictions")
  
      return recall_tracker.report()
  
imports: import xml.etree.ElementTree
  from collections import defaultdict
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  from prettytable import PrettyTable
  from tqdm import tqdm
  import json
  import os
  import re
label_desc: Use image node_id 2 for calling the Flickr30kEntitiesRecallEvaluator obj's underlying member method code with example usage: obj.evaluate(predictions) and returns: recall_tracker