code: def next_frame_glow_bair_quant():
      """Hparams to reproduce bits-per-pixel results on BAIR action-free dataset."""
      hparams = next_frame_glow_hparams()
      hparams.video_num_input_frames = 3
      hparams.video_num_target_frames = 10
      hparams.num_train_frames = 4
      hparams.num_cond_latents = 3
      hparams.depth = 24
      hparams.latent_dist_encoder = "conv3d_net"
      hparams.latent_encoder_width = 256
      hparams.latent_architecture = "glow_resnet"
      hparams.latent_encoder_depth = 5
      hparams.latent_apply_dilations = True
      hparams.latent_activation = "gatu"
      hparams.activation = "gatu"
      hparams.learning_rate_constant = 3e-4
      hparams.learning_rate_schedule = "constant*linear_warmup"
      hparams.learning_rate_warmup_steps = 10000
      hparams.init_batch_size = 128
      hparams.batch_size = 5
      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 2 for calling a global function with example usage: next_frame_glow_bair_quant() and returns: hparams