class_docstr: Creates a map of the mode of the diffusion tensors given a set of
  diffusion-weighted images, as well as their associated b-values and
  b-vectors [1]_. Fits the diffusion tensors and calculates tensor mode
  with Dipy.
  
  Example
  -------
  >>> import nipype.interfaces.dipy as dipy
  >>> mode = dipy.TensorMode()
  >>> mode.inputs.in_file = 'diffusion.nii'
  >>> mode.inputs.in_bvec = 'bvecs'
  >>> mode.inputs.in_bval = 'bvals'
  >>> mode.run()                                   # doctest: +SKIP
  
  References
  ----------
  .. [1] Daniel B. Ennis and G. Kindlmann, "Orthogonal Tensor
      Invariants and the Analysis of Diffusion Tensor Magnetic Resonance
      Images", Magnetic Resonance in Medicine, vol. 55, no. 1, pp. 136-146,
      2006.
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code: def _list_outputs(self):
      outputs = self._outputs().get()
      outputs["out_file"] = self._gen_filename("mode")
      return outputs
  
imports: import numpy
  import nibabel
  from ...None import logging
  from ..base import TraitedSpec, File, isdefined
  from .base import DipyDiffusionInterface, DipyBaseInterfaceInputSpec
label_desc: Use image node_id 2 for calling the TensorMode obj's underlying member method code with example usage: obj._list_outputs() and returns: outputs