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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_basic_deterministic():
 """Basic 2-frame conv model."""

  hparams = base.next_frame_base()
  hparams.video_num_input_frames = 4
  hparams.video_num_target_frames = 1
  hparams.hidden_size = 64
  hparams.batch_size = 4
  hparams.num_hidden_layers = 2
  hparams.optimizer = "Adafactor"
  hparams.learning_rate_constant = 1.5
  hparams.learning_rate_warmup_steps = 8000
  hparams.learning_rate_schedule = "linear_warmup * constant * rsqrt_decay"
  hparams.label_smoothing = 0.0
  hparams.initializer = "uniform_unit_scaling"
  hparams.initializer_gain = 1.3
  hparams.weight_decay = 0.0
  hparams.clip_grad_norm = 1.0
  hparams.dropout = 0.1
  hparams.add_hparam("residual_dropout", 0.5)
  hparams.add_hparam("num_compress_steps", 6)
  hparams.add_hparam("filter_double_steps", 2)
  hparams.add_hparam("pixel_sampling_temperature", 0.0)
  hparams.add_hparam("concat_internal_states", False)
  hparams.add_hparam("do_autoregressive_rnn", False)
  hparams.add_hparam("autoregressive_rnn_lookback", 8)
  hparams.add_hparam("autoregressive_rnn_warmup_steps", 8000)
  hparams.add_hparam("activation_fn", "relu")
  hparams.bottom["inputs"] = modalities.video_identity_bottom
  hparams.bottom["targets"] = modalities.video_identity_bottom
  return hparams