class_docstr: Convert some results to :obj:`torch.Tensor` by given keys.
  
  Args:
      keys (Sequence[str]): Keys that need to be converted to Tensor.
class_docstr_tok: ["Convert","some","results","to",":","obj",":","`","torch.Tensor","`","by","given","keys",".","Args",":","keys","(","Sequence","[","str","]",")",":","Keys","that","need","to","be","converted","to","Tensor","."]
docstr: Call function to convert data in results to :obj:`torch.Tensor`.
  
  Args:
      results (dict): Result dict contains the data to convert.
  
  Returns:
      dict: The result dict contains the data converted
          to :obj:`torch.Tensor`.
docstr_tok: ["Call","function","to","convert","data","in","results","to",":","obj",":","`","torch.Tensor","`",".","Args",":","results","(","dict",")",":","Result","dict","contains","the","data","to","convert",".","Returns",":","dict",":","The","result","dict","contains","the","data","converted","to",":","obj",":","`","torch.Tensor","`","."]
code: def __call__(self, results):
      """Call function to convert data in results to :obj:`torch.Tensor`.
  
      Args:
          results (dict): Result dict contains the data to convert.
  
      Returns:
          dict: The result dict contains the data converted
              to :obj:`torch.Tensor`.
      """
  
      for key in self.keys:
          results[key] = to_tensor(results[key])
      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 ToTensor obj's underlying member method code with example usage: obj.__call__(results) and returns: results