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')
      >>> ]
class_docstr_tok: ["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 collections.abc import Sequence
  import mmcv
  import numpy
  import torch
  from mmcv.parallel import DataContainer
  from ..builder import PIPELINES
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