code: 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
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import six
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.layers import discretization
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
label_desc: Use image node_id 3 for calling a global function with example usage: next_frame_base() and returns: hparams