class_docstr: mAP evaluation metrics for the VIS task.
  
  Args:
      metric (str | list[str]): Metrics to be evaluated.
          Default value is `youtube_vis_ap`.
      metric_items (List[str], optional): Metric result names to be
          recorded in the evaluation result. Defaults to None.
      outfile_prefix (str | None): The prefix of json files. It includes
          the file path and the prefix of filename, e.g., "a/b/prefix".
          If not specified, a temp file will be created. Defaults to None.
      collect_device (str): Device name used for collecting results from
          different ranks during distributed training. Must be 'cpu' or
          'gpu'. Defaults to 'cpu'.
      prefix (str, optional): The prefix that will be added in the metric
          names to disambiguate homonyms metrics of different evaluators.
          If prefix is not provided in the argument, self.default_prefix
          will be used instead. Default: None
      format_only (bool): If True, only formatting the results to the
          official format and not performing evaluation. Defaults to False.
docstr: Evaluate the model performance of the whole dataset after processing
  all batches.
  
  Args:
      size (int): Length of the entire validation dataset.
  
  Returns:
      dict: Evaluation metrics dict on the val dataset. The keys are the
      names of the metrics, and the values are corresponding results.
docstr_tok: ["Evaluate","the","model","performance","of","the","whole","dataset","after","processing","all","batches",".","Args",":","size","(","int",")",":","Length","of","the","entire","validation","dataset",".","Returns",":","dict",":","Evaluation","metrics","dict","on","the","val","dataset",".","The","keys","are","the","names","of","the","metrics",",","and","the","values","are","corresponding","results","."]
code: def evaluate(self, size: int) -> dict:
      """Evaluate the model performance of the whole dataset after processing
      all batches.
  
      Args:
          size (int): Length of the entire validation dataset.
  
      Returns:
          dict: Evaluation metrics dict on the val dataset. The keys are the
          names of the metrics, and the values are corresponding results.
      """
      # wait for all processes to complete prediction.
      barrier()
  
      if len(self.results) == 0:
          warnings.warn(
              f"{self.__class__.__name__} got empty `self.results`. Please "
              "ensure that the processed results are properly added into "
              "`self.results` in `process` method."
          )
  
      results = collect_tracking_results(
          self.results, self.collect_device
      )
  
      # gather seq_info
      gathered_seq_info = all_gather_object(
          self._vis_meta_info["videos"]
      )
      all_seq_info = []
      for _seq_info in gathered_seq_info:
          all_seq_info.extend(_seq_info)
      # update self._vis_meta_info
      self._vis_meta_info = dict(videos=all_seq_info)
  
      if is_main_process():
          _metrics = self.compute_metrics(results)  # type: ignore
          # Add prefix to metric names
          if self.prefix:
              _metrics = {
                  "/".join((self.prefix, k)): v
                  for k, v in _metrics.items()
              }
          metrics = [_metrics]
      else:
          metrics = [None]  # type: ignore
  
      broadcast_object_list(metrics)
  
      # reset the results list
      self.results.clear()
      # reset the vis_meta_info
      self._vis_meta_info.clear()
      return metrics[0]
  
imports: import os.path
  import tempfile
  import warnings
  import zipfile
  from collections import OrderedDict, defaultdict
  from typing import Dict, List, Optional, Sequence, Tuple, Union
  import mmengine
  import numpy
  from mmengine.dist import all_gather_object, barrier, broadcast_object_list, is_main_process
  from mmengine.logging import MMLogger
  from mmdet.registry import METRICS
  from mmdet.structures.mask import encode_mask_results
  from ..functional import YTVIS, YTVISeval
  from .base_video_metric import BaseVideoMetric, collect_tracking_results
label_desc: Use image node_id 9 for calling the YouTubeVISMetric obj's underlying member method code with example usage: obj.evaluate(size) and returns: metrics