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: generate score map anchors based on predefined configuration
  
  Parameters
  ----------
      score_size : int
          score map size
  
  Returns
  ----------
      score map anchor
docstr_tok: ["generate","score","map","anchors","based","on","predefined","configuration","Parameters","--","--","--","--","--","score_size",":","int","score","map","size","Returns","--","--","--","--","--","score","map","anchor"]
code: def generate_anchor(self, score_size):
      """
      generate score map anchors based on predefined configuration
  
      Parameters
      ----------
          score_size : int
              score map size
  
      Returns
      ----------
          score map anchor
      """
      anchors = Anchors(self.STRIDE, self.RATIOS, self.SCALES)
      anchor = anchors.anchors
      x_min, y_min, x_max, y_max = (
          anchor[:, 0],
          anchor[:, 1],
          anchor[:, 2],
          anchor[:, 3],
      )
      anchor = np.stack(
          [
              (x_min + x_max) * 0.5,
              (y_min + y_max) * 0.5,
              x_max - x_min,
              y_max - y_min,
          ],
          1,
      )
      total_stride = anchors.stride
      anchor_num = anchor.shape[0]
      anchor = np.tile(anchor, score_size * score_size).reshape((-1, 4))
      ori = -(score_size // 2) * total_stride
      x_x, y_y = np.meshgrid(
          [ori + total_stride * dx for dx in range(score_size)],
          [ori + total_stride * dy for dy in range(score_size)],
      )
      x_x, y_y = (
          np.tile(x_x.flatten(), (anchor_num, 1)).flatten(),
          np.tile(y_y.flatten(), (anchor_num, 1)).flatten(),
      )
      anchor[:, 0], anchor[:, 1] = x_x.astype(np.float32), y_y.astype(
          np.float32
      )
      return anchor
  
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 2 for calling the SiamRPNTracker obj's underlying member method code with example usage: obj.generate_anchor(score_size) and returns: anchor