class_docstr: Default training transform for simple pose.
  
  Parameters
  ----------
  num_joints : int
      Number of joints defined by dataset
  image_size : tuple of int
      Image size, as (height, width).
  heatmap_size : tuple of int
      Heatmap size, as (height, width).
  sigma : float
      Gaussian sigma for the heatmap generation.
class_docstr_tok: ["Default","training","transform","for","simple","pose",".","Parameters","--","--","--","--","--","num_joints",":","int","Number","of","joints","defined","by","dataset","image_size",":","tuple","of","int","Image","size",",","as","(","height",",","width",")",".","heatmap_size",":","tuple","of","int","Heatmap","size",",","as","(","height",",","width",")",".","sigma",":","float","Gaussian","sigma","for","the","heatmap","generation","."]
code: def _target_generator(self):
      if self._internal_target_generator is None:
          from ....model_zoo.simple_pose.pose_target import (
              SimplePoseGaussianTargetGenerator,
          )
  
          self._internal_target_generator = (
              SimplePoseGaussianTargetGenerator(
                  self._num_joints,
                  (self._width, self._height),
                  (self._heatmap_width, self._heatmap_height),
                  self._sigma,
              )
          )
          return self._internal_target_generator
      else:
          return self._internal_target_generator
  
file_path: gluon-cv/gluoncv/data/transforms/presets/simple_pose.py
imports: from __future__ import absolute_import
  import random
  import numpy
  import mxnet
  from ..image import random_flip
  from ..pose import flip_joints_3d, get_affine_transform, affine_transform
  from ....utils.filesystem import try_import_cv2
label_desc: Use image node_id 2 for calling the SimplePoseDefaultTrainTransform obj's underlying member method code with example usage: obj._target_generator() and returns: self, self