code: def transform(
      input_,
      alpha=1.0,
      normalizer_fn=ops.conditional_instance_norm,
      normalizer_params=None,
      reuse=False,
  ):
      """Maps content images to stylized images.
  
      Args:
        input_: Tensor. Batch of input images.
        alpha: Float. Width multiplier to reduce the number of filters in the model
            and slim it down. Defaults to 1.0, which results in the hyper-parameters
            used in the published paper.
        normalizer_fn: normalization layer function.  Defaults to
            ops.conditional_instance_norm.
        normalizer_params: dict of parameters to pass to the conditional instance
            normalization op.
        reuse: bool. Whether to reuse model parameters. Defaults to False.
  
      Returns:
        Tensor. The output of the transformer network.
      """
      if normalizer_params is None:
          normalizer_params = {"center": True, "scale": True}
      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),
          ):
              with tf.variable_scope("contract"):
                  h = conv2d(input_, 9, 1, int(alpha * 32), "conv1")
                  h = conv2d(h, 3, 2, int(alpha * 64), "conv2")
                  h = conv2d(h, 3, 2, int(alpha * 128), "conv3")
              with tf.variable_scope("residual"):
                  h = residual_block(h, 3, "residual1")
                  h = residual_block(h, 3, "residual2")
                  h = residual_block(h, 3, "residual3")
                  h = residual_block(h, 3, "residual4")
                  h = residual_block(h, 3, "residual5")
              with tf.variable_scope("expand"):
                  h = upsampling(h, 3, 2, int(alpha * 64), "conv1")
                  h = upsampling(h, 3, 2, int(alpha * 32), "conv2")
                  return upsampling(
                      h, 9, 1, 3, "conv3", activation_fn=tf.nn.sigmoid
                  )
  
imports: from magenta.models.image_stylization import ops
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 1 for calling a global function with example usage: transform(input_, alpha, normalizer_fn, normalizer_params, reuse) and returns: upsampling