code: def next_frame_savp():
      """SAVP model hparams."""
      hparams = sv2p_params.next_frame_sv2p()
      hparams.add_hparam("z_dim", 8)
      hparams.add_hparam("num_discriminator_filters", 32)
      hparams.add_hparam("use_vae", True)
      hparams.add_hparam("use_gan", False)
      hparams.add_hparam("use_spectral_norm", True)
      hparams.add_hparam("gan_loss", "cross_entropy")
      hparams.add_hparam("gan_loss_multiplier", 0.01)
      hparams.add_hparam("gan_vae_loss_multiplier", 0.01)
      hparams.add_hparam("gan_optimization", "joint")
      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.latent_loss_multiplier_schedule = "linear"
      hparams.upsample_method = "bilinear_upsample_conv"
      hparams.internal_loss = False
      hparams.reward_prediction = False
      hparams.anneal_end = 100000
      hparams.num_iterations_1st_stage = 0
      hparams.num_iterations_2nd_stage = 50000
      return hparams
  
file_path: tensor2tensor/tensor2tensor/models/video/savp_params.py
imports: from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import modalities
  from tensor2tensor.models.video import sv2p_params
  from tensor2tensor.utils import registry
label_desc: Use image node_id 1 for calling a global function with example usage: next_frame_savp() and returns: hparams