code: def __init__(
      self,
      flickr_path: str,
      subset: str = "test",
      topk: Sequence[int] = (1, 5, 10, -1),
      iou_thresh: float = 0.5,
      merge_boxes: bool = False,
      verbose: bool = True,
  ):
      assert subset in [
          "train",
          "test",
          "val",
      ], f"Wrong flickr subset {subset}"
  
      self.topk = topk
      self.iou_thresh = iou_thresh
  
      flickr_path = Path(flickr_path)
  
      # We load the image ids corresponding to the current subset
      with open(flickr_path / f"{subset}.txt") as file_d:
          self.img_ids = [line.strip() for line in file_d]
  
      if verbose:
          print(f"Flickr subset contains {len(self.img_ids)} images")
  
      # Read the box annotations for all the images
      self.imgid2boxes: Dict[str, Dict[str, List[List[int]]]] = {}
  
      if verbose:
          print("Loading annotations...")
  
      for img_id in self.img_ids:
          anno_info = get_annotations(
              flickr_path / "Annotations" / f"{img_id}.xml"
          )["boxes"]
          if merge_boxes:
              merged = {}
              for phrase_id, boxes in anno_info.items():
                  merged[phrase_id] = _merge_boxes(boxes)
              anno_info = merged
          self.imgid2boxes[img_id] = anno_info
  
      # Read the sentences annotations
      self.imgid2sentences: Dict[str, List[List[Optional[Dict]]]] = {}
  
      if verbose:
          print("Loading annotations...")
  
      self.all_ids: List[str] = []
      tot_phrases = 0
      for img_id in self.img_ids:
          sentence_info = get_sentence_data(
              flickr_path / "Sentences" / f"{img_id}.txt"
          )
          self.imgid2sentences[img_id] = [
              None for _ in range(len(sentence_info))
          ]
  
          # Some phrases don't have boxes, we filter them.
          for sent_id, sentence in enumerate(sentence_info):
              phrases = [
                  phrase
                  for phrase in sentence["phrases"]
                  if phrase["phrase_id"] in self.imgid2boxes[img_id]
              ]
              if len(phrases) > 0:
                  self.imgid2sentences[img_id][sent_id] = phrases
              tot_phrases += len(phrases)
  
          self.all_ids += [
              f"{img_id}_{k}"
              for k in range(len(sentence_info))
              if self.imgid2sentences[img_id][k] is not None
          ]
  
      if verbose:
          print(
              f"There are {tot_phrases} phrases in {len(self.all_ids)} sentences to evaluate"
          )
  
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 1 to create a new Flickr30kEntitiesRecallEvaluator object  with example: obj = Flickr30kEntitiesRecallEvaluator(flickr_path, subset, topk, iou_thresh, merge_boxes, verbose)