code: def compute_prior(
      name,
      z,
      latent,
      hparams,
      condition=False,
      state=None,
      temperature=1.0,
  ):
      """Distribution on z_t conditioned on z_{t-1} and latent.
  
      Args:
        name: variable scope.
        z: 4-D Tensor.
        latent: optional,
                if hparams.latent_dist_encoder == "pointwise", this is a list
                of 4-D Tensors of length hparams.num_cond_latents.
                else, this is just a 4-D Tensor
                The first-three dimensions of the latent should be the same as z.
        hparams: next_frame_glow_hparams.
        condition: Whether or not to condition the distribution on latent.
        state: tf.nn.rnn_cell.LSTMStateTuple.
               the current state of a LSTM used to model the distribution. Used
               only if hparams.latent_dist_encoder = "conv_lstm".
        temperature: float, temperature with which to sample from the Gaussian.
      Returns:
        prior_dist: instance of tfp.distributions.Normal
        state: Returns updated state.
      Raises:
        ValueError: If hparams.latent_dist_encoder is "pointwise" and if the shape
                    of latent is different from z.
      """
      with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
          if isinstance(condition, bool):
              condition = tf.constant(condition, dtype=tf.bool)
          prior_dist = single_conv_dist("level_prior", z)
          prior_mean, prior_scale = prior_dist.loc, prior_dist.scale
  
          if latent is None:
              mean, scale = prior_mean, prior_scale
          else:
              cond_mean, cond_scale, state = level_cond_prior(
                  prior_dist, z, latent, hparams, state
              )
              mean, scale = tf.cond(
                  condition,
                  lambda: (cond_mean, cond_scale),
                  lambda: (prior_mean, prior_scale),
              )
          dist = TemperedNormal(mean, scale, temperature)
          return dist, state
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import numpy
  import scipy
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.utils import contrib
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  import tensorflow_probability
label_desc: Use image node_id 33 for calling a global function with example usage: compute_prior(name, z, latent, hparams, condition, state, temperature) and returns: dist,  state