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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def ConvBlock(kernel_size, filters, strides):
 """ResNet convolutional striding block."""

  ks = kernel_size
  filters1, filters2, filters3 = filters
  main = layers.Serial(
      layers.Conv(filters1, (1, 1), strides),
      layers.BatchNorm(),
      layers.Relu(),
      layers.Conv(filters2, (ks, ks), padding='SAME'),
      layers.BatchNorm(),
      layers.Relu(),
      layers.Conv(filters3, (1, 1)),
      layers.BatchNorm()
  )
  shortcut = layers.Serial(
      layers.Conv(filters3, (1, 1), strides),
      layers.BatchNorm()
  )
  return layers.Serial(
      layers.Branch(),
      layers.Parallel(main, shortcut),
      layers.SumBranches(),
      layers.Relu()
  )