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 __init__(self, size, mean, std, max_intensity=255.0):
      super(VideoGroupValTransform, self).__init__()
      if isinstance(size, numbers.Number):
          self.size = (int(size), int(size))
      else:
          self.size = size
      self.mean = np.asarray(mean).reshape((len(mean), 1, 1))
      self.std = np.asarray(std).reshape((len(std), 1, 1))
      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 VideoGroupValTransform object from inherited base classes: Block with example: obj = VideoGroupValTransform(size, mean, std, max_intensity)