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 forward(self, clips):
      (
          h_off,
          w_off,
          crop_height,
          crop_width,
      ) = self.randomResizedCrop.get_params(
          clips[0], self.scale, self.ratio
      )
      is_flip = random.random() < self.prob
  
      new_clips = []
      for cur_img in clips:
          crop_img = cur_img[
              h_off : h_off + crop_height, w_off : w_off + crop_width, :
          ]
          resize_img = self.cv2.resize(
              crop_img, (self.width, self.height)
          )
          if is_flip:
              flip_img = np.flip(resize_img, axis=1)
          else:
              flip_img = resize_img
          tensor_img = (
              np.transpose(flip_img, axes=(2, 0, 1))
              / self.max_intensity
          )
          new_clips.append((tensor_img - self.mean) / self.std)
      return new_clips
  
imports: from __future__ import division
  import random
  import math
  import numbers
  import numpy
  from mxnet.gluon import Block
label_desc: Use image node_id 2 for calling the VideoGroupTrainTransformV4 obj's underlying member method code with example usage: obj.forward(clips) and returns: new_clips