code: def fill_hparams(
      hparams,
      in_frames,
      out_frames,
      gen_mode="conditional",
      latent_dist_encoder="pointwise",
      pretrain_steps=-1,
      num_train_frames=-1,
      cond_first_frame=False,
      apply_dilations=False,
      activation="relu",
  ):
      """Set next_frame_glow hparams."""
      hparams.latent_activation = activation
      hparams.latent_apply_dilations = apply_dilations
      hparams.video_num_input_frames = in_frames
      hparams.video_num_target_frames = out_frames
      hparams.latent_dist_encoder = latent_dist_encoder
      hparams.gen_mode = gen_mode
      hparams.pretrain_steps = pretrain_steps
      hparams.num_train_frames = num_train_frames
      hparams.cond_first_frame = cond_first_frame
      if latent_dist_encoder in ["conv_net", "conv3d_net"]:
          hparams.num_cond_latents = in_frames
      else:
          hparams.num_cond_latents = 1
      problem = registry.problem("video_stochastic_shapes10k")
      p_hparams = problem.get_hparams(hparams)
      hparams.problem = problem
      hparams.problem_hparams = p_hparams
      hparams.tiny_mode = True
      hparams.reward_prediction = False
      hparams.latent_architecture = "glow_resnet"
      hparams.latent_encoder_depth = 2
      hparams.latent_pre_output_channels = 32
      if (
          hparams.gen_mode == "conditional"
          and hparams.latent_dist_encoder == "pointwise"
      ):
          hparams.batch_size = 16
          hparams.init_batch_size = 16
      else:
          hparams.batch_size = 16
          hparams.init_batch_size = 32
      hparams.affine_coupling_width = 32
      hparams.depth = 5
      hparams.n_levels = 2
      return hparams
  
file_path: tensor2tensor/tensor2tensor/models/video/nfg_test_utils.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import tempfile
  import numpy
  from tensor2tensor.data_generators import video_generated
  from tensor2tensor.models.video import next_frame_glow
  from tensor2tensor.utils import registry
  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: fill_hparams(hparams, in_frames, out_frames, gen_mode, latent_dist_encoder, pretrain_steps, num_train_frames, cond_first_frame, apply_dilations, activation) and returns: hparams