class_docstr: build SiamRPNTracker.predicted tracking result.and according to
  tracking result, product window penalty and smooth bbox
  
  Parameters
  ----------
      PENALTY_K : float
          Scale penalty
      WINDOW_INFLUENCE :float
          Window influence
      LR : float
          Interpolation learning rate
      EXEMPLAR_SIZE : int
          EXEMPLAR_SIZE
      INSTANCE_SIZE : int
          INSTANCE_SIZE
      BASE_SIZE : int
          Base size
      CONTEXT_AMOUNT : float
          Context amount
      STRIDE : int
          Anchor stride
      RATIOS : tuple
          Anchor ratios
      SCALES : tuple
          Anchor scales
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docstr: from cls to score
  
  Parameters
  ----------
      score : ndarray
          network output
  
  Returns
  -------
      get feature map score though softmax
docstr_tok: ["from","cls","to","score","Parameters","--","--","--","--","--","score",":","ndarray","network","output","Returns","--","--","--","-","get","feature","map","score","though","softmax"]
code: def _convert_score(self, score):
      """from cls to score
  
      Parameters
      ----------
          score : ndarray
              network output
  
      Returns
      -------
          get feature map score though softmax
      """
      score = nd.transpose(score, axes=(1, 2, 3, 0))
      score = nd.reshape(score, shape=(2, -1))
      score = nd.transpose(score, axes=(1, 0))
      score = nd.softmax(score, axis=1)
      score = nd.slice_axis(score, axis=1, begin=1, end=2)
      score = nd.squeeze(score, axis=1)
      return score.asnumpy()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import math
  from collections import namedtuple
  import numpy
  import mxnet
  from mxnet import nd
  from gluoncv.utils.filesystem import try_import_cv2
label_desc: Use image node_id 4 for calling the SiamRPNTracker obj's underlying member method code with example usage: obj._convert_score(score) and returns: score