class_docstr: Convert image to :obj:`torch.Tensor` by given keys.
  
  The dimension order of input image is (H, W, C). The pipeline will convert
  it to (C, H, W). If only 2 dimension (H, W) is given, the output would be
  (1, H, W).
  
  Args:
      keys (Sequence[str]): Key of images to be converted to Tensor.
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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.
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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:
          img = results[key]
          if len(img.shape) < 3:
              img = np.expand_dims(img, -1)
          results[key] = to_tensor(img.transpose(2, 0, 1))
      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 ImageToTensor obj's underlying member method code with example usage: obj.__call__(results) and returns: results