code: def transform(
      input_,
      normalizer_fn=None,
      normalizer_params=None,
      reuse=False,
      trainable=True,
      is_training=True,
      alpha=1.0,
  ):
      """Maps content images to stylized images.
  
      Args:
        input_: Tensor. Batch of input images.
        normalizer_fn: normalization layer function for applying style
          normalization.
        normalizer_params: dict of parameters to pass to the style normalization op.
        reuse: bool. Whether to reuse model parameters. Defaults to False.
        trainable: bool. Should the parameters be marked as trainable?
        is_training: bool. Is it training phase or not?
        alpha: float. Width multiplier to reduce the number of filters used in the
          model and slim it down. Defaults to 1.0, which results
          in the hyper-parameters used in the published paper.
  
  
      Returns:
        Tensor. The output of the transformer network.
      """
      with tf.variable_scope("transformer", reuse=reuse):
          with slim.arg_scope(
              [slim.conv2d],
              activation_fn=tf.nn.relu,
              normalizer_fn=normalizer_fn,
              normalizer_params=normalizer_params,
              weights_initializer=tf.random_normal_initializer(
                  0.0, 0.01
              ),
              biases_initializer=tf.constant_initializer(0.0),
              trainable=trainable,
          ):
              with slim.arg_scope(
                  [slim.conv2d],
                  normalizer_fn=slim.batch_norm,
                  normalizer_params=None,
                  trainable=trainable,
              ):
                  with slim.arg_scope(
                      [slim.batch_norm],
                      is_training=is_training,
                      trainable=trainable,
                  ):
                      with tf.variable_scope("contract"):
                          h = model_util.conv2d(
                              input_, 9, 1, int(alpha * 32), "conv1"
                          )
                          h = model_util.conv2d(
                              h, 3, 2, int(alpha * 64), "conv2"
                          )
                          h = model_util.conv2d(
                              h, 3, 2, int(alpha * 128), "conv3"
                          )
              with tf.variable_scope("residual"):
                  h = model_util.residual_block(h, 3, "residual1")
                  h = model_util.residual_block(h, 3, "residual2")
                  h = model_util.residual_block(h, 3, "residual3")
                  h = model_util.residual_block(h, 3, "residual4")
                  h = model_util.residual_block(h, 3, "residual5")
              with tf.variable_scope("expand"):
                  h = model_util.upsampling(
                      h, 3, 2, int(alpha * 64), "conv1"
                  )
                  h = model_util.upsampling(
                      h, 3, 2, int(alpha * 32), "conv2"
                  )
                  return model_util.upsampling(
                      h, 9, 1, 3, "conv3", activation_fn=tf.nn.sigmoid
                  )
  
imports: from magenta.models.image_stylization import model
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 1 for calling a global function with example usage: transform(input_, normalizer_fn, normalizer_params, reuse, trainable, is_training, alpha) and returns: model_util