code: def calculate_variational_targets(self):
      # calculate (some of the) variational parameters corresponding to exact posterior
  
      def calc_lambda_A(lA, lB, lC):
          return lA + lB + lC
  
      def calc_lambda_B(lA, lB):
          return (lA * lB) / (lA + lB)
  
      def calc_lambda_C(lA, lB, lC):
          return ((lA + lB) * lC) / (lA + lB + lC)
  
      self.target_lambdas = {"1": self.lambdas[0]}
      previous_names = ["1"]
      for n in range(2, self.N + 1):
          new_names = []
          for prev_name in previous_names:
              for LR in ["L", "R"]:
                  new_names.append(prev_name + LR)
                  self.target_lambdas[new_names[-1]] = self.lambdas[
                      n - 1
                  ]
          previous_names = new_names
  
      # recursion to compute the target precisions
      previous_names = ["1"]
      old_left_pivot_lambda = None
      for n in range(2, self.N + 1):
          new_names = []
          for prev_name in previous_names:
              BC_names = []
              for LR in ["L", "R"]:
                  new_names.append(prev_name + LR)
                  BC_names.append(new_names[-1])
              lambda_A0 = self.target_lambdas[prev_name]
              if n == self.N:
                  old_left_pivot_lambda = lambda_A0
              lambda_B0 = self.target_lambdas[BC_names[0]]
              lambda_C0 = self.target_lambdas[BC_names[1]]
              lambda_A = calc_lambda_A(lambda_A0, lambda_B0, lambda_C0)
              lambda_B = calc_lambda_B(lambda_A0, lambda_B0)
              lambda_C = calc_lambda_C(lambda_A0, lambda_B0, lambda_C0)
              self.target_lambdas[prev_name] = lambda_A
              self.target_lambdas[BC_names[0]] = lambda_B
              self.target_lambdas[BC_names[1]] = lambda_C
          previous_names = new_names
  
      for prev_name in previous_names:
          new_lambda = (
              self.N_data * self.lambdas[-1]
              + self.target_lambdas[prev_name]
          )
          self.target_lambdas[prev_name] = new_lambda
  
      leftmost_node_suffix = self.q_topo_sort[0][11:]
      leftmost_lambda = self.target_lambdas[leftmost_node_suffix]
      self.target_leftmost_constant = (
          self.data_sums[0] * self.lambdas[-1] / leftmost_lambda
      )
      self.target_leftmost_constant += (
          self.loc0
          * (leftmost_lambda - self.N_data * self.lambdas[-1])
          / leftmost_lambda
      )
  
      almost_leftmost_node_suffix = leftmost_node_suffix[:-1] + "R"
      almost_leftmost_lambda = self.target_lambdas[
          almost_leftmost_node_suffix
      ]
      result = self.lambdas[-1] * self.data_sums[1]
      result += (
          (almost_leftmost_lambda - self.N_data * self.lambdas[-1])
          * self.loc0
          * old_left_pivot_lambda
          / (old_left_pivot_lambda + self.lambdas[-2])
      )
      self.target_almost_leftmost_constant = (
          result / almost_leftmost_lambda
      )
  
imports: import logging
  import os
  import time
  from unittest import TestCase
  import numpy
  import pytest
  import torch
  import pyro
  import pyro.distributions
  import pyro.optim
  from pyro.distributions.testing import fakes
  from pyro.infer import SVI, TraceGraph_ELBO
  from pyro.poutine import Trace
  from tests.common import assert_equal
label_desc: Use image node_id 11 for calling the GaussianPyramidTests obj's underlying member method code with example usage: obj.calculate_variational_targets() and returns: unknown, unknown, unknown