class_docstr: Default training transform for AlphaPose.
  
  Parameters
  ----------
  num_joints : int
      Number of joints defined by dataset
  joint_pairs: list
      Paired Joints for horizontal flipping
  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.
  random_flip: bool
      Data augmentation with horizaontal flipping.
  random_sample: bool
      Data augmentation with random sample.
  random_crop: bool
      Data augmentation with random crop.
  scale_factor: tuple
      Scale augmentation.
  rotation_factor: int
      Ratation augmentation.
code: def __init__(
      self,
      num_joints,
      joint_pairs,
      image_size=(256, 256),
      heatmap_size=(64, 64),
      sigma=1,
      random_flip=True,
      random_sample=False,
      random_crop=False,
      scale_factor=(0.2, 0.3),
      rotation_factor=30,
  ):
      self._sigma = sigma
      self._num_joints = num_joints
      self._joint_pairs = joint_pairs
      self._image_size = image_size
      self._heatmap_size = heatmap_size
      self._height = image_size[0]
      self._width = image_size[1]
      self._random_flip = random_flip
      self._random_sample = random_sample
      self._random_crop = random_crop
      self._scale_factor = scale_factor
      self._rotation_factor = rotation_factor
      self._aspect_ratio = float(self._width) / self._height
  
file_path: gluon-cv/gluoncv/data/transforms/presets/alpha_pose.py
imports: from __future__ import absolute_import
  import random
  import numpy
  import mxnet
  from ..pose import random_sample_bbox, count_visible, random_crop_bbox
  from ..pose import drawGaussian, transformBox, cv_cropBox, cv_rotate, detector_to_alpha_pose
label_desc: Use image node_id 1 to create a new AlphaPoseDefaultTrainTransform object from inherited base classes: object with example: obj = AlphaPoseDefaultTrainTransform(num_joints, joint_pairs, image_size, heatmap_size, sigma, random_flip, random_sample, random_crop, scale_factor, rotation_factor)