class_docstr: Combination of transforms for validation.
  (1) center crop
  (2) to tensor
  (3) normalize
class_docstr_tok: ["Combination","of","transforms","for","validation",".","(","1",")","center","crop","(","2",")","to","tensor","(","3",")","normalize"]
code: def forward(self, clips):
      h, w, _ = clips[0].shape
      th, tw = self.size
      x1 = int(round((w - tw) / 2.0))
      y1 = int(round((h - th) / 2.0))
  
      new_clips = []
      for cur_img in clips:
          crop_img = cur_img[y1 : y1 + th, x1 : x1 + tw, :]
          tensor_img = (
              np.transpose(crop_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 VideoGroupValTransform obj's underlying member method code with example usage: obj.forward(clips) and returns: new_clips