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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 shake_shake_branch(x, output_filters, stride, rand_forward, rand_backward, hparams):
 """Building a 2 branching convnet."""

  is_training = hparams.mode == tf.estimator.ModeKeys.TRAIN
  x = tf.nn.relu(x)
  x = tf.layers.conv2d(
      x,
      output_filters, (3, 3),
      strides=(stride, stride),
      padding="SAME",
      name="conv1")
  x = tf.layers.batch_normalization(x, training=is_training, name="bn1")
  x = tf.nn.relu(x)
  x = tf.layers.conv2d(x, output_filters, (3, 3), padding="SAME", name="conv2")
  x = tf.layers.batch_normalization(x, training=is_training, name="bn2")
  if is_training:
    x = x * rand_backward + tf.stop_gradient(x * rand_forward -
                                             x * rand_backward)
  else:
    x *= 1.0 / hparams.shake_shake_num_branches
  return x