code: def next_frame_sv2p():
      """SV2P model hparams."""
      hparams = basic_stochastic.next_frame_basic_stochastic()
      hparams.optimizer = "true_adam"
      hparams.learning_rate_schedule = "constant"
      hparams.learning_rate_constant = 1e-3
      hparams.video_num_input_frames = 1
      hparams.video_num_target_frames = 3
      hparams.batch_size = 16
      hparams.bottom = {
          "inputs": modalities.video_raw_bottom,
          "targets": modalities.video_raw_targets_bottom,
      }
      hparams.loss = {
          "targets": modalities.video_l2_raw_loss,
      }
      hparams.top = {
          "targets": modalities.video_raw_top,
      }
      hparams.video_modality_loss_cutoff = 0.0
      hparams.scheduled_sampling_mode = "count"
      hparams.scheduled_sampling_k = 900.0
      hparams.add_hparam("reward_prediction", True)
      hparams.add_hparam("reward_prediction_stop_gradient", False)
      hparams.add_hparam("reward_prediction_buffer_size", 0)
      hparams.add_hparam("model_options", "CDNA")
      hparams.add_hparam("num_masks", 10)
      hparams.add_hparam("multi_latent", False)
      hparams.add_hparam("relu_shift", 1e-12)
      hparams.add_hparam("dna_kernel_size", 5)
      hparams.add_hparam("upsample_method", "conv2d_transpose")
      hparams.add_hparam("reward_model", "basic")
      hparams.add_hparam("visualize_logits_histogram", True)
      hparams.add_hparam("action_normalize", False)
      return hparams
  
file_path: tensor2tensor/tensor2tensor/models/video/sv2p_params.py
imports: from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import modalities
  from tensor2tensor.models.video import basic_stochastic
  from tensor2tensor.utils import registry
label_desc: Use image node_id 1 for calling a global function with example usage: next_frame_sv2p() and returns: hparams