code: def next_frame_basic_stochastic_discrete():
      """Basic 2-frame conv model with stochastic discrete latent."""
      hparams = basic_deterministic_params.next_frame_sampling()
      hparams.batch_size = 4
      hparams.video_num_target_frames = 6
      hparams.scheduled_sampling_mode = "prob_inverse_lin"
      hparams.scheduled_sampling_decay_steps = 40000
      hparams.scheduled_sampling_max_prob = 1.0
      hparams.dropout = 0.15
      hparams.filter_double_steps = 3
      hparams.hidden_size = 96
      hparams.learning_rate_constant = 0.002
      hparams.learning_rate_warmup_steps = 2000
      hparams.learning_rate_schedule = "linear_warmup * constant"
      hparams.concat_internal_states = True
      hparams.video_modality_loss_cutoff = 0.03
      hparams.add_hparam("bottleneck_bits", 128)
      hparams.add_hparam("bottleneck_noise", 0.1)
      hparams.add_hparam("discretize_warmup_steps", 40000)
      hparams.add_hparam("latent_rnn_warmup_steps", 40000)
      hparams.add_hparam("latent_rnn_max_sampling", 0.5)
      hparams.add_hparam("latent_use_max_probability", 0.8)
      hparams.add_hparam("full_latent_tower", False)
      hparams.add_hparam("latent_predictor_state_size", 128)
      hparams.add_hparam("latent_predictor_temperature", 1.0)
      hparams.add_hparam("complex_addn", True)
      hparams.add_hparam("recurrent_state_size", 64)
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.layers import discretization
  from tensor2tensor.models.video import base_vae
  from tensor2tensor.models.video import basic_deterministic
  from tensor2tensor.models.video import basic_deterministic_params
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 3 for calling a global function with example usage: next_frame_basic_stochastic_discrete() and returns: hparams