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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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", .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", .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