class_docstr: Combination of transforms for training.
  Follow the style of https://github.com/facebookresearch/SlowFast/
      (1) random short side scale jittering
      (2) random crop
      (3) random horizontal flip
      (4) to tensor
      (5) normalize
class_docstr_tok: ["Combination","of","transforms","for","training",".","Follow","the","style","of","https",":","\/\/github.com\/facebookresearch\/SlowFast\/","(","1",")","random","short","side","scale","jittering","(","2",")","random","crop","(","3",")","random","horizontal","flip","(","4",")","to","tensor","(","5",")","normalize"]
code: def __init__(
      self,
      crop_size,
      min_size,
      max_size,
      mean,
      std,
      prob=0.5,
      max_intensity=255.0,
  ):
      super(VideoGroupTrainTransformV3, self).__init__()
  
      from ...utils.filesystem import try_import_cv2
  
      self.cv2 = try_import_cv2()
      self.height = crop_size[0]
      self.width = crop_size[1]
      self.min_size = min_size
      self.max_size = max_size
      self.mean = np.asarray(mean).reshape((len(mean), 1, 1))
      self.std = np.asarray(std).reshape((len(std), 1, 1))
      self.prob = prob
      self.max_intensity = max_intensity
  
imports: from __future__ import division
  import random
  import math
  import numbers
  import numpy
  from mxnet.gluon import Block
label_desc: Use image node_id 1 to create a new VideoGroupTrainTransformV3 object from inherited base classes: Block with example: obj = VideoGroupTrainTransformV3(crop_size, min_size, max_size, mean, std, prob, max_intensity)