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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_emily():
 """Emily's model hparams."""

  hparams = sv2p_params.next_frame_sv2p()
  hparams.video_num_input_frames = 2
  hparams.video_num_target_frames = 10
  hparams.learning_rate_constant = 1e-4
  seq_length = hparams.video_num_input_frames + hparams.video_num_target_frames
  # The latent_loss_multiplier is divided by the number of frames because
  # the image sequence loss in t2t is averaged instead of added through
  # time as they do in the SVG-LP paper
  hparams.latent_loss_multiplier = 1e-4 / seq_length
  hparams.reward_prediction = False
  hparams.num_iterations_1st_stage = -1
  hparams.num_iterations_2nd_stage = -1
  hparams.optimizer_adam_beta1 = 0.9
  hparams.optimizer_adam_beta2 = 0.999
  hparams.optimizer_adam_epsilon = 1e-08
  hparams.anneal_end = -1
  hparams.clip_grad_norm = 5.0
  hparams.add_hparam("learned_prior", True)
  hparams.add_hparam("z_dim", 64)
  hparams.add_hparam("g_dim", 128)
  hparams.add_hparam("rnn_size", 256)
  hparams.add_hparam("prior_rnn_layers", 1)
  hparams.add_hparam("posterior_rnn_layers", 1)
  hparams.add_hparam("predictor_rnn_layers", 2)
  hparams.add_hparam("has_skips", True)
  hparams.add_hparam("has_batchnorm", True)
  return hparams