code: def next_frame_glow_hparams():
      """Hparams for next_frame_glow."""
      hparams = glow.glow_hparams()
      # Possible modes are conditional and unconditional
      hparams.add_hparam("gen_mode", "conditional")
      hparams.add_hparam("learn_top_scale", False)
      hparams.add_hparam("condition_all_levels", True)
      # For each video, substitutes "num_input_frames + num_output_frames" with a
      # randomly sampled patch of length "num_train_frames" during training.
      # -1 indicates that the entire video is used for training.
      hparams.add_hparam("num_train_frames", -1)
      # The following are hparams that model the latent transitions.
      # Encoder that maps the latents to a Gaussian distribution.
      # This function is used to model the prior over z_{t}. Can be,
      # Pointwise -> point-wise multiplication of z_{t-1}.
      # conv_net -> one-layer convolution over z_{t-1} .. z_{t - num_cond_latents}
      # conv3d_net or conv_lstm
      hparams.add_hparam("latent_dist_encoder", "conv_net")
      # Number of latents used in the encoder above.
      hparams.add_hparam("num_cond_latents", 1)
      hparams.add_hparam("latent_architecture", "glow_resnet")
      hparams.add_hparam("latent_apply_dilations", False)
      hparams.add_hparam("latent_dilation_rates", [1, 3])
      # Use latent skip connections
      hparams.add_hparam("model_input", False)
      hparams.add_hparam("cond_first_frame", False)
      hparams.add_hparam("latent_skip", True)
      hparams.add_hparam("latent_encoder_depth", 2)
      hparams.add_hparam("latent_encoder_width", 512)
      hparams.add_hparam("latent_dropout", 0.0)
      hparams.add_hparam("latent_pre_output_channels", 512)
      hparams.add_hparam("latent_activation", "relu")
      hparams.add_hparam("latent_noise", 0.0)
      # Pretrains the glow encoder for "pretrain_steps" number of steps.
      # By default, don't pretrain and learn end-to-end
      hparams.add_hparam("pretrain_steps", -1)
      hparams.bottom = {
          "inputs": modalities.video_raw_bottom,
          "targets": modalities.video_raw_targets_bottom,
      }
      hparams.loss = {
          "targets": modalities.video_l1_raw_loss,
      }
      hparams.top = {
          "targets": modalities.video_raw_top,
      }
      hparams.init_batch_size = 256
      hparams.batch_size = 32
      # Possible options: are prev_frame, single_conv and normal
      hparams.top_prior = "single_conv"
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from six.moves import range
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.layers import modalities
  from tensor2tensor.models.research import glow
  from tensor2tensor.models.research import glow_ops
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
  import tensorflow_probability
label_desc: Use image node_id 1 for calling a global function with example usage: next_frame_glow_hparams() and returns: hparams