code: def next_frame_sampling_stochastic():
      """Basic 2-frame conv model with stochastic tower."""
      hparams = basic_deterministic_params.next_frame_sampling()
      hparams.stochastic_model = True
      hparams.add_hparam("latent_channels", 1)
      hparams.add_hparam("latent_std_min", -5.0)
      hparams.add_hparam("num_iterations_1st_stage", 15000)
      hparams.add_hparam("num_iterations_2nd_stage", 15000)
      hparams.add_hparam("latent_loss_multiplier", 1e-3)
      hparams.add_hparam("latent_loss_multiplier_dynamic", False)
      hparams.add_hparam("latent_loss_multiplier_alpha", 1e-5)
      hparams.add_hparam("latent_loss_multiplier_epsilon", 1.0)
      hparams.add_hparam("latent_loss_multiplier_schedule", "constant")
      hparams.add_hparam(
          "latent_num_frames", 0
      )  # 0 means use all frames.
      hparams.add_hparam("anneal_end", 40000)
      hparams.add_hparam("information_capacity", 0.0)
      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 2 for calling a global function with example usage: next_frame_sampling_stochastic() and returns: hparams