code: def next_frame_epva():
      """EPVA hparams."""
      hparams = (
          basic_deterministic_params.next_frame_basic_deterministic()
      )
      hparams.video_num_input_frames = 4
      hparams.video_num_target_frames = 4
      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.learning_rate_schedule = "constant"
      hparams.learning_rate_constant = 1e-05
      hparams.batch_size = 2
      hparams.clip_grad_norm = 0.01
      # TODO(msaffar): disentangle EPVA from SV2P
      hparams.add_hparam("reward_prediction", False)
      hparams.add_hparam("clip_pixel_values", True)
      hparams.add_hparam("context_frames", 5)
      hparams.add_hparam("enc_learning_rate", 1e-5)
      hparams.add_hparam("enc_pred_loss_scale", 0.1)
      hparams.add_hparam("enc_pred_loss_scale_delay", 6e5)
      hparams.add_hparam("enc_size", 64)
      hparams.add_hparam("enc_keep_prob", 0.65)
      hparams.add_hparam("enc_pred_use_l1_loss", False)
      hparams.add_hparam("enc_pred_use_l2norm", False)
      hparams.add_hparam("van_learning_rate", 3e-5)
      hparams.add_hparam("van_keep_prob", 0.9)
      hparams.add_hparam("sequence_length ", 64)
      hparams.add_hparam("skip_num", 2)
      hparams.add_hparam("pred_noise_std", 0)
      hparams.add_hparam("lstm_state_noise_stddev", 0)
      return hparams
  
imports: from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import modalities
  from tensor2tensor.models.video import basic_deterministic_params
  from tensor2tensor.utils import registry
label_desc: Use image node_id 1 for calling a global function with example usage: next_frame_epva() and returns: hparams