class_docstr: Combination of transforms for training.
  (1) multiscale crop
  (2) scale
  (3) random horizontal flip
  (4) to tensor
  (5) normalize
class_docstr_tok: ["Combination","of","transforms","for","training",".","(","1",")","multiscale","crop","(","2",")","scale","(","3",")","random","horizontal","flip","(","4",")","to","tensor","(","5",")","normalize"]
code: def __init__(
      self,
      size,
      scale_ratios,
      mean,
      std,
      fix_crop=True,
      more_fix_crop=True,
      max_distort=1,
      prob=0.5,
      max_intensity=255.0,
  ):
      super(VideoGroupTrainTransform, self).__init__()
  
      from ...utils.filesystem import try_import_cv2
  
      self.cv2 = try_import_cv2()
      self.height = size[0]
      self.width = size[1]
      self.multiScaleCrop = VideoMultiScaleCrop(
          size=size,
          scale_ratios=scale_ratios,
          fix_crop=fix_crop,
          more_fix_crop=more_fix_crop,
          max_distort=max_distort,
      )
      self.scale_ratios = scale_ratios
      self.fix_crop = fix_crop
      self.more_fix_crop = more_fix_crop
      self.max_distort = max_distort
      self.prob = prob
      self.max_intensity = max_intensity
      self.mean = np.asarray(mean).reshape((len(mean), 1, 1))
      self.std = np.asarray(std).reshape((len(std), 1, 1))
  
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 VideoGroupTrainTransform object from inherited base classes: Block with example: obj = VideoGroupTrainTransform(size, scale_ratios, mean, std, fix_crop, more_fix_crop, max_distort, prob, max_intensity)