class_docstr: Defines the SVGDecoder model.
code: def create_initial_input_for_decode(self, batch_size):
      # Create an initial output tensor. This will be passed
      # to the infer_step, which adds one timestep at every iteration.
      dim = self._problem_hparams.vocab_size["targets"]
      hdim = self._hparams.hidden_size
      initial_output = tf.zeros(
          (batch_size, 0, 1, hdim), dtype=tf.float32
      )
      zero_pad = tf.zeros((batch_size, 1, 1, dim), dtype=tf.float32)
      # Hack: foldl complains when the output shape is less specified than the
      # input shape, so we confuse it about the input shape.
      initial_output = tf.slice(
          initial_output,
          [0, 0, 0, 0],
          common_layers.shape_list(initial_output),
      )
      zero_pad = tf.slice(
          zero_pad, [0, 0, 0, 0], common_layers.shape_list(zero_pad)
      )
      return zero_pad, initial_output
  
imports: import copy
  from magenta.contrib import rnn
  from magenta.models.svg_vae import image_vae
  from magenta.models.svg_vae import svg_decoder_loss
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  from tensor2tensor.utils import trainer_lib
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 8 for calling the SVGDecoder obj's underlying member method code with example usage: obj.create_initial_input_for_decode(batch_size) and returns: zero_pad,  initial_output