class_docstr: Defines the SVGDecoder model.
code: def _greedy_infer(self, features, decode_length, use_tpu=False):
      # decode_length is set to 0, unused.
      del decode_length, use_tpu
      infer_features = copy.copy(features)
      if "targets" not in infer_features:
          infer_features["targets"] = infer_features["infer_targets"]
      logits, losses = self(
          infer_features
      )  # pylint: disable=not-callable
      return {
          "outputs": logits,
          "scores": None,
          "logits": logits,
          "losses": losses,
      }
  
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 9 for calling the SVGDecoder obj's underlying member method code with example usage: obj._greedy_infer(features, decode_length, use_tpu) and returns: dict