code: def decode_hparams(overrides=""):
      """Hparams for decoding."""
      hparams = decoding.decode_hparams()
      # Number of interpolations between [0.0, 1.0].
      hparams.add_hparam("num_interp", 11)
      # Which level(s) to interpolate.
      hparams.add_hparam("level_interp", [0, 1, 2])
      # "all" or "ranked", interpolate all channels or a "ranked".
      hparams.add_hparam("channel_interp", "all")
      # interpolate channels ranked according to squared L2 norm.
      hparams.add_hparam("rank_interp", 1)
      # Whether on not to save frames as summaries
      hparams.add_hparam("save_frames", True)
      hparams.parse(overrides)
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl import flags
  import numpy
  from six.moves import zip
  from tensor2tensor.bin import t2t_trainer
  from tensor2tensor.data_generators import image_utils
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.models.research import glow_ops
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import decoding
  from tensor2tensor.utils import trainer_lib
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 1 for calling a global function with example usage: decode_hparams(overrides) and returns: hparams