code: def __call__(self):
      # calls pyro.param so that params are exposed and constraints applied
      # should not create any new torch.Tensors after __init__
      self.initialize_params()
  
      N_state = self.config["sizes"]["state"]
  
      # initialize gamma to uniform
      gamma = Tensor(
          torch.zeros((N_state, N_state)),
          OrderedDict([("y_prev", Bint[N_state])]),
      )
  
      N_v = self.config["sizes"]["random"]
      N_c = self.config["sizes"]["group"]
      log_prob = []
  
      plate_g = Tensor(
          torch.zeros(N_c), OrderedDict([("g", Bint[N_c])])
      )
  
      # group-level random effects
      if self.config["group"]["random"] == "discrete":
          # group-level discrete effect
          e_g = Variable("e_g", Bint[N_v])
          e_g_dist = plate_g + dist.Categorical(**self.params["e_g"])(
              value=e_g
          )
  
          log_prob.append(e_g_dist)
  
          eps_g = (plate_g + self.params["eps_g"]["theta"])(e_g=e_g)
  
      elif self.config["group"]["random"] == "continuous":
          eps_g = Variable("eps_g", Reals[N_state])
          eps_g_dist = plate_g + dist.Normal(**self.params["eps_g"])(
              value=eps_g
          )
  
          log_prob.append(eps_g_dist)
      else:
          eps_g = to_funsor(0.0)
  
      N_s = self.config["sizes"]["individual"]
  
      plate_i = Tensor(
          torch.zeros(N_s), OrderedDict([("i", Bint[N_s])])
      )
      # individual-level random effects
      if self.config["individual"]["random"] == "discrete":
          # individual-level discrete effect
          e_i = Variable("e_i", Bint[N_v])
          e_i_dist = (
              plate_g
              + plate_i
              + dist.Categorical(**self.params["e_i"])(value=e_i)
              * self.raggedness_masks["individual"](t=0)
          )
  
          log_prob.append(e_i_dist)
  
          eps_i = (
              plate_i + plate_g + self.params["eps_i"]["theta"](e_i=e_i)
          )
  
      elif self.config["individual"]["random"] == "continuous":
          eps_i = Variable("eps_i", Reals[N_state])
          eps_i_dist = (
              plate_g
              + plate_i
              + dist.Normal(**self.params["eps_i"])(value=eps_i)
          )
  
          log_prob.append(eps_i_dist)
      else:
          eps_i = to_funsor(0.0)
  
      # add group-level and individual-level random effects to gamma
      gamma = gamma + eps_g + eps_i
  
      N_state = self.config["sizes"]["state"]
  
      # we've accounted for all effects, now actually compute gamma_y
      gamma_y = gamma(y_prev="y(t=1)")
  
      y = Variable("y", Bint[N_state])
      y_dist = (
          plate_g
          + plate_i
          + dist.Categorical(probs=gamma_y.exp() / gamma_y.exp().sum())(
              value=y
          )
      )
  
      # observation 1: step size
      step_dist = (
          plate_g
          + plate_i
          + dist.Gamma(
              **{k: v(y_curr=y) for k, v in self.params["step"].items()}
          )(value=self.observations["step"])
      )
  
      # step size zero-inflation
      if self.config["zeroinflation"]:
          step_zi = dist.Categorical(
              probs=self.params["zi_step"]["zi_param"](y_curr=y)
          )(value="zi_step")
          step_zi_dist = (
              plate_g
              + plate_i
              + dist.Delta(self.config["MISSING"], 0.0)(
                  value=self.observations["step"]
              )
          )
          step_dist = (
              step_zi + Stack("zi_step", (step_dist, step_zi_dist))
          ).reduce(ops.logaddexp, "zi_step")
  
      # observation 2: step angle
      angle_dist = (
          plate_g
          + plate_i
          + dist.VonMises(
              **{
                  k: v(y_curr=y)
                  for k, v in self.params["angle"].items()
              }
          )(value=self.observations["angle"])
      )
  
      # observation 3: dive activity
      omega_dist = (
          plate_g
          + plate_i
          + dist.Beta(
              **{
                  k: v(y_curr=y)
                  for k, v in self.params["omega"].items()
              }
          )(value=self.observations["omega"])
      )
  
      # dive activity zero-inflation
      if self.config["zeroinflation"]:
          omega_zi = dist.Categorical(
              probs=self.params["zi_omega"]["zi_param"](y_curr=y)
          )(value="zi_omega")
          omega_zi_dist = (
              plate_g
              + plate_i
              + dist.Delta(self.config["MISSING"], 0.0)(
                  value=self.observations["omega"]
              )
          )
          omega_dist = (
              omega_zi + Stack("zi_omega", (omega_dist, omega_zi_dist))
          ).reduce(ops.logaddexp, "zi_omega")
  
      # finally, construct the term for parallel scan reduction
      hmm_factor = step_dist + angle_dist + omega_dist
      hmm_factor = hmm_factor * self.raggedness_masks["individual"]
      hmm_factor = hmm_factor * self.raggedness_masks["timestep"]
      # copy masking behavior of pyro.infer.TraceEnum_ELBO._compute_model_factors
      hmm_factor = hmm_factor + y_dist
      log_prob.insert(0, hmm_factor)
  
      return log_prob
  
imports: from collections import OrderedDict
  import pyro
  import torch
  from torch.distributions import constraints
  import funsor.ops
  import funsor.torch.distributions
  from funsor.domains import Bint, Reals
  from funsor.tensor import Tensor
  from funsor.terms import Stack, Variable, to_funsor
label_desc: Use image node_id 5 for calling the Model obj's underlying member method code with example usage: obj.__call__() and returns: log_prob