code: def encodeBBox(self, prior, bbox):
      self.variances = [0.1, 0.1, 0.2, 0.2]
  
      xmin = (
          0.5
          * (bbox[2] + bbox[0] - prior[2] - prior[0])
          / (prior[2] - prior[0])
          / self.variances[0]
      )
      ymin = (
          0.5
          * (bbox[3] + bbox[1] - prior[3] - prior[1])
          / (prior[3] - prior[1])
          / self.variances[1]
      )
      xmax = (
          np.log((bbox[2] - bbox[0]) / (prior[2] - prior[0]))
          / self.variances[2]
      )
      ymax = (
          np.log((bbox[3] - bbox[1]) / (prior[3] - prior[1]))
          / self.variances[3]
      )
      return [xmin, ymin, xmax, ymax]
  
imports: from neon import NervanaObject
  import numpy
  from util import util
  from neon.transforms import Softmax
label_desc: Use image node_id 6 for calling the MBoxLoss obj's underlying member method code with example usage: obj.encodeBBox(prior, bbox) and returns: list