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
class_docstr_tok: ["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"]
docstr: from loc to predict postion
  
  Parameters
  ----------
      delta : ndarray or np.ndarray
          network output
      anchor : np.ndarray
          generate anchor location
  
  Returns
  -------
      rejust predict postion though Anchor
docstr_tok: ["from","loc","to","predict","postion","Parameters","--","--","--","--","--","delta",":","ndarray","or","np.ndarray","network","output","anchor",":","np.ndarray","generate","anchor","location","Returns","--","--","--","-","rejust","predict","postion","though","Anchor"]
code: def _convert_bbox(self, delta, anchor):
      """from loc to predict postion
  
      Parameters
      ----------
          delta : ndarray or np.ndarray
              network output
          anchor : np.ndarray
              generate anchor location
  
      Returns
      -------
          rejust predict postion though Anchor
      """
      delta = nd.transpose(delta, axes=(1, 2, 3, 0))
      delta = nd.reshape(delta, shape=(4, -1))
      delta = delta.asnumpy()
      delta[0, :] = delta[0, :] * anchor[:, 2] + anchor[:, 0]
      delta[1, :] = delta[1, :] * anchor[:, 3] + anchor[:, 1]
      delta[2, :] = np.exp(delta[2, :]) * anchor[:, 2]
      delta[3, :] = np.exp(delta[3, :]) * anchor[:, 3]
      return delta
  
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 3 for calling the SiamRPNTracker obj's underlying member method code with example usage: obj._convert_bbox(delta, anchor) and returns: delta