class_docstr: Construct a Lorenz System model with unknown scale parameters.
  
  This class models a Lorenz System, a three-dimensional nonlinear dynamical
  system used to model atmospheric convection. The differential equation model
  is identical to that of `LorenzSystem`, which assumes fixed parameters, but
  here the scale parameters of the innovation and observation distributions are
  additionally modeled as unknown random variables:
  
  ```
  innovation_scale ~ LogNormal(-1., 1.)
  observation_scale ~ LogNormal(-1., 1.)
  
  # IID noise processes.
  w_x(t) ~ Normal(0., innovation_scale)
  w_y(t) ~ Normal(0., innovation_scale)
  w_z(t) ~ Normal(0., innovation_scale)
  
  x(0) ~ Normal(0, 1)
  y(0) ~ Normal(0, 1)
  z(0) ~ Normal(0, 1)
  
  x'(t) = 10 * (y(t) - x(t)) + w_x(t)
  y'(t) = x(t) * (28 - z(t)) - y(t) + w_y(t)
  z'(t) = x(t) * y(t) - 8 / 3 * z(t) + w_z(t)
  
  # Noisy observations.
  obs_x(t) ~ Normal(x(t), observation_scale)
  obs_y(t) ~ Normal(y(t), observation_scale)
  obs_z(t) ~ Normal(z(t), observation_scale)
  ```
code: def _prior_distribution(self):
      return self._prior_dist
  
imports: import functools
  import numpy
  import tensorflow.compat.v2
  import tensorflow_probability
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import tensor_util
  from inference_gym.internal import data
  from inference_gym.targets import bayesian_model
  from inference_gym.targets import model
  from inference_gym.targets.ground_truth import convection_lorenz_bridge
  from inference_gym.targets.ground_truth import convection_lorenz_bridge_unknown_scales
label_desc: Use image node_id 2 for calling the LorenzSystemUnknownScales obj's underlying member method code with example usage: obj._prior_distribution() and returns: self