class_docstr: Transpose some results by given keys.
  
  Args:
      keys (Sequence[str]): Keys of results to be transposed.
      order (Sequence[int]): Order of transpose.
class_docstr_tok: ["Transpose","some","results","by","given","keys",".","Args",":","keys","(","Sequence","[","str","]",")",":","Keys","of","results","to","be","transposed",".","order","(","Sequence","[","int","]",")",":","Order","of","transpose","."]
docstr: Call function to transpose the channel order of data in results.
  
  Args:
      results (dict): Result dict contains the data to transpose.
  
  Returns:
      dict: The result dict contains the data transposed to                 ``self.order``.
docstr_tok: ["Call","function","to","transpose","the","channel","order","of","data","in","results",".","Args",":","results","(","dict",")",":","Result","dict","contains","the","data","to","transpose",".","Returns",":","dict",":","The","result","dict","contains","the","data","transposed","to","``","self.order","``","."]
code: def __call__(self, results):
      """Call function to transpose the channel order of data in results.
  
          Args:
              results (dict): Result dict contains the data to transpose.
  
          Returns:
              dict: The result dict contains the data transposed to \
                  ``self.order``.
          """
      for key in self.keys:
          results[key] = results[key].transpose(self.order)
      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 2 for calling the Transpose obj's underlying member method code with example usage: obj.__call__(results) and returns: results