class_docstr: Combination of transforms for training.
  Follow the style of https://github.com/open-mmlab/mmaction.
  This is only for tranining SlowFast family networks in mmaction.
      (1) random crop and resize
      (2) random horizontal flip
      (3) to tensor
      (4) normalize
class_docstr_tok: ["Combination","of","transforms","for","training",".","Follow","the","style","of","https",":","\/\/github.com\/open-mmlab\/mmaction",".","This","is","only","for","tranining","SlowFast","family","networks","in","mmaction",".","(","1",")","random","crop","and","resize","(","2",")","random","horizontal","flip","(","3",")","to","tensor","(","4",")","normalize"]
code: def __init__(
      self,
      size,
      mean,
      std,
      scale=(0.08, 1.0),
      ratio=(3.0 / 4.0, 4.0 / 3.0),
      prob=0.5,
      max_intensity=255.0,
  ):
      super(VideoGroupTrainTransformV4, self).__init__()
  
      from ...utils.filesystem import try_import_cv2
  
      self.cv2 = try_import_cv2()
      self.height = size[0]
      self.width = size[1]
      self.randomResizedCrop = RandomResizedCrop(
          size=size, scale=scale, ratio=ratio
      )
      self.scale = scale
      self.ratio = ratio
      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 VideoGroupTrainTransformV4 object from inherited base classes: Block with example: obj = VideoGroupTrainTransformV4(size, mean, std, scale, ratio, prob, max_intensity)