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')
      >>> ]
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docstr: Call function to wrap fields into lists.
  
  Args:
      results (dict): Result dict contains the data to wrap.
  
  Returns:
      dict: The result dict where value of ``self.keys`` are wrapped                 into list.
docstr_tok: ["Call","function","to","wrap","fields","into","lists",".","Args",":","results","(","dict",")",":","Result","dict","contains","the","data","to","wrap",".","Returns",":","dict",":","The","result","dict","where","value","of","``","self.keys","``","are","wrapped","into","list","."]
code: def __call__(self, results):
      """Call function to wrap fields into lists.
  
          Args:
              results (dict): Result dict contains the data to wrap.
  
          Returns:
              dict: The result dict where value of ``self.keys`` are wrapped \
                  into list.
          """
  
      # Wrap dict fields into lists
      for key, val in results.items():
          results[key] = [val]
      return results
  
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 1 for calling the WrapFieldsToLists obj's underlying member method code with example usage: obj.__call__(results) and returns: results