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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_base():
 """Common HParams for next_frame models."""

  hparams = common_hparams.basic_params1()
  # Loss cutoff.
  hparams.add_hparam("video_modality_loss_cutoff", 0.01)
  # Additional resizing the frames before feeding them to model.
  hparams.add_hparam("preprocess_resize_frames", None)
  # How many data points to suffle. Ideally should be part of problem not model!
  hparams.add_hparam("shuffle_buffer_size", 128)
  # Tiny mode. For faster tests.
  hparams.add_hparam("tiny_mode", False)
  # In case a model supports smaller/faster version.
  hparams.add_hparam("small_mode", False)
  # In case a model has stochastic version.
  hparams.add_hparam("stochastic_model", False)
  # Internal loss for recurrent models.
  hparams.add_hparam("internal_loss", True)
  # choose from: concat, multiplicative, multi_additive
  hparams.add_hparam("action_injection", "multi_additive")
  # Scheduled sampling method. Choose between
  # ground_truth_only, prediction_only, prob, count, prob_inverse_exp.
  hparams.add_hparam("scheduled_sampling_mode", "prediction_only")
  hparams.add_hparam("scheduled_sampling_decay_steps", 10000)
  hparams.add_hparam("scheduled_sampling_max_prob", 1.0)
  hparams.add_hparam("scheduled_sampling_k", 900.0)
  return hparams