class_docstr: Temporally predictive modeling with the support vector machine
  SVM Train Only
  For complete details, see the `3dsvm Documentation.
  <https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dsvm.html>`_
  
  Examples
  ========
  
  >>> from nipype.interfaces import afni as afni
  >>> svmTrain = afni.SVMTrain()
  >>> svmTrain.inputs.in_file = 'run1+orig'
  >>> svmTrain.inputs.trainlabels = 'run1_categories.1D'
  >>> svmTrain.inputs.ttype = 'regression'
  >>> svmTrain.inputs.mask = 'mask.nii'
  >>> svmTrain.inputs.model = 'model_run1'
  >>> svmTrain.inputs.alphas = 'alphas_run1'
  >>> res = svmTrain.run() # doctest: +SKIP
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code: def _format_arg(self, name, trait_spec, value):
      return super()._format_arg(name, trait_spec, value)
  
file_path: nipype/nipype/interfaces/afni/svm.py
imports: from ..base import TraitedSpec, traits, File
  from .base import AFNICommand, AFNICommandInputSpec, AFNICommandOutputSpec
label_desc: Use image node_id 1 for calling the SVMTrain obj's underlying member method code with example usage: obj._format_arg(name, trait_spec, value) and returns: super