class_docstr: Implements the Shake-Shake architecture.
  
  From <https://arxiv.org/pdf/1705.07485.pdf>
  This is intended to match the CIFAR-10 version, and correspond to
  "Shake-Shake-Batch" in Table 1.
code: def body(self, features):
      hparams = self._hparams
      is_training = hparams.mode == tf_estimator.ModeKeys.TRAIN
      inputs = features["inputs"]
      assert (hparams.num_hidden_layers - 2) % 6 == 0
      assert hparams.hidden_size % 16 == 0
      k = hparams.hidden_size // 16
      n = (hparams.num_hidden_layers - 2) // 6
      x = inputs
  
      x = tf.layers.conv2d(
          x, 16, (3, 3), padding="SAME", name="init_conv"
      )
      x = tf.layers.batch_normalization(
          x, training=is_training, name="init_bn"
      )
      with tf.variable_scope("L1"):
          x = shake_shake_layer(x, 16 * k, n, 1, hparams)
      with tf.variable_scope("L2"):
          x = shake_shake_layer(x, 32 * k, n, 2, hparams)
      with tf.variable_scope("L3"):
          x = shake_shake_layer(x, 64 * k, n, 2, hparams)
      x = tf.nn.relu(x)
  
      # Global avg on [1, 2] (we're nhwc) and dense to num_classes done by top.
      return x
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import hparam
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 1 for calling the ShakeShake obj's underlying member method code with example usage: obj.body(features) and returns: x