class_docstr: Transpose some results by given keys.
  
  Args:
      keys (Sequence[str]): Keys of results to be transposed.
      order (Sequence[int]): Order of transpose.
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docstr: Call function to convert image in results to :obj:`torch.Tensor` and
  transpose the channel order.
  
  Args:
      results (dict): Result dict contains the image data to convert.
  
  Returns:
      dict: The result dict contains the image converted
          to :obj:`torch.Tensor` and transposed to (C, H, W) order.
docstr_tok: ["Call","function","to","convert","image","in","results","to",":","obj",":","`","torch.Tensor","`","and","transpose","the","channel","order",".","Args",":","results","(","dict",")",":","Result","dict","contains","the","image","data","to","convert",".","Returns",":","dict",":","The","result","dict","contains","the","image","converted","to",":","obj",":","`","torch.Tensor","`","and","transposed","to","(","C",",","H",",","W",")","order","."]
code: def __call__(self, results):
      """Call function to convert image in results to :obj:`torch.Tensor` and
      transpose the channel order.
  
      Args:
          results (dict): Result dict contains the image data to convert.
  
      Returns:
          dict: The result dict contains the image converted
              to :obj:`torch.Tensor` and transposed to (C, H, W) order.
      """
  
      for key in self.keys:
          results[key] = results[key].transpose(self.order)
      return results
  
imports: from collections.abc import Sequence
  import annotator.uniformer.mmcv
  import numpy
  import torch
  from annotator.uniformer.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