class_docstr: generic mean-field guide for continuous random effects
class_docstr_tok: ["generic","mean-field","guide","for","continuous","random","effects"]
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_c = self.config["sizes"]["group"]
      N_s = self.config["sizes"]["individual"]
  
      log_prob = Tensor(torch.tensor(0.0), OrderedDict())
  
      plate_g = Tensor(
          torch.zeros(N_c), OrderedDict([("g", Bint[N_c])])
      )
      plate_i = Tensor(
          torch.zeros(N_s), OrderedDict([("i", Bint[N_s])])
      )
  
      if self.config["group"]["random"] == "continuous":
          eps_g_dist = plate_g + dist.Normal(**self.params["eps_g"])(
              value="eps_g"
          )
          log_prob += eps_g_dist
  
      # individual-level random effects
      if self.config["individual"]["random"] == "continuous":
          eps_i_dist = (
              plate_g
              + plate_i
              + dist.Normal(**self.params["eps_i"])(value="eps_i")
          )
          log_prob += eps_i_dist
  
      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 3 for calling the Guide obj's underlying member method code with example usage: obj.__call__() and returns: log_prob