<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def next_frame_glow_hparams():
 """Hparams for next_frame_glow."""

  hparams = glow.glow_hparams()
  # Possible modes are conditional and unconditional
  hparams.add_hparam("gen_mode", "conditional")
  hparams.add_hparam("learn_top_scale", False)
  hparams.add_hparam("condition_all_levels", True)
  # For each video, substitutes "num_input_frames + num_output_frames" with a
  # randomly sampled patch of length "num_train_frames" during training.
  # -1 indicates that the entire video is used for training.
  hparams.add_hparam("num_train_frames", -1)
  # The following are hparams that model the latent transitions.
  # Encoder that maps the latents to a Gaussian distribution.
  # This function is used to model the prior over z_{t}. Can be,
  # Pointwise -> point-wise multiplication of z_{t-1}.
  # conv_net -> one-layer convolution over z_{t-1} .. z_{t - num_cond_latents}
  # conv3d_net or conv_lstm
  hparams.add_hparam("latent_dist_encoder", "conv_net")
  # Number of latents used in the encoder above.
  hparams.add_hparam("num_cond_latents", 1)
  hparams.add_hparam("latent_architecture", "glow_resnet")
  hparams.add_hparam("latent_apply_dilations", False)
  hparams.add_hparam("latent_dilation_rates", [1, 3])
  # Use latent skip connections
  hparams.add_hparam("model_input", False)
  hparams.add_hparam("cond_first_frame", False)
  hparams.add_hparam("latent_skip", True)
  hparams.add_hparam("latent_encoder_depth", 2)
  hparams.add_hparam("latent_encoder_width", 512)
  hparams.add_hparam("latent_dropout", 0.0)
  hparams.add_hparam("latent_pre_output_channels", 512)
  hparams.add_hparam("latent_activation", "relu")
  hparams.add_hparam("latent_noise", 0.0)
  # Pretrains the glow encoder for "pretrain_steps" number of steps.
  # By default, don't pretrain and learn end-to-end
  hparams.add_hparam("pretrain_steps", -1)
  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.init_batch_size = 256
  hparams.batch_size = 32
  # Possible options: are prev_frame, single_conv and normal
  hparams.top_prior = "single_conv"
  return hparams