class_docstr: Convert diffusion-weighted images to tensor images
  
  
  Example
  -------
  
  >>> import nipype.interfaces.mrtrix3 as mrt
  >>> tsr = mrt.FitTensor()
  >>> tsr.inputs.in_file = 'dwi.mif'
  >>> tsr.inputs.in_mask = 'mask.nii.gz'
  >>> tsr.inputs.grad_fsl = ('bvecs', 'bvals')
  >>> tsr.cmdline                               # doctest: +ELLIPSIS
  'dwi2tensor -fslgrad bvecs bvals -mask mask.nii.gz dwi.mif dti.mif'
  >>> tsr.run()                                 # doctest: +SKIP
class_docstr_tok: ["Convert","diffusion-weighted","images","to","tensor","images","Example","--","--","--","-",">",">",">","import","nipype.interfaces.mrtrix3","as","mrt",">",">",">","tsr","=","mrt.FitTensor","(",")",">",">",">","tsr.inputs.in_file","=","'dwi.mif'",">",">",">","tsr.inputs.in_mask","=","'mask.nii.gz'",">",">",">","tsr.inputs.grad_fsl","=","(","'bvecs","'",",","'bvals","'",")",">",">",">","tsr.cmdline","#","doctest",":","+ELLIPSIS","'dwi2tensor","-fslgrad","bvecs","bvals","-mask","mask.nii.gz","dwi.mif","dti.mif'",">",">",">","tsr.run","(",")","#","doctest",":","+SKIP"]
code: def _list_outputs(self):
      outputs = self.output_spec().get()
      outputs["out_file"] = op.abspath(self.inputs.out_file)
      if isdefined(self.inputs.predicted_signal):
          outputs["predicted_signal"] = op.abspath(
              self.inputs.predicted_signal
          )
      return outputs
  
imports: import os.path
  from ..base import traits, TraitedSpec, File, InputMultiObject, isdefined
  from .base import MRTrix3BaseInputSpec, MRTrix3Base
label_desc: Use image node_id 1 for calling the FitTensor obj's underlying member method code with example usage: obj._list_outputs() and returns: outputs