class_docstr: Wrap fields of the data dictionary into lists for evaluation.
  
  This class can be used as a last step of a test or validation
  pipeline for single image evaluation or inference.
  
  Example:
      >>> test_pipeline = [
      >>>    dict(type='LoadImageFromFile'),
      >>>    dict(type='Normalize',
                  mean=[123.675, 116.28, 103.53],
                  std=[58.395, 57.12, 57.375],
                  to_rgb=True),
      >>>    dict(type='Pad', size_divisor=32),
      >>>    dict(type='ImageToTensor', keys=['img']),
      >>>    dict(type='Collect', keys=['img']),
      >>>    dict(type='WrapFieldsToLists')
      >>> ]
code: def __repr__(self):
      return f"{self.__class__.__name__}()"
  
imports: from typing import Optional, Sequence
  import numpy
  from mmcv.transforms import to_tensor
  from mmcv.transforms.base import BaseTransform
  from mmengine.structures import InstanceData, PixelData
  from mmdet.registry import TRANSFORMS
  from mmdet.structures import DetDataSample, ReIDDataSample, TrackDataSample
  from mmdet.structures.bbox import BaseBoxes
label_desc: Use image node_id 2 for calling the WrapFieldsToLists obj's underlying member method code with example usage: obj.__repr__() and returns: self, str