code: def test_product_of_gaussians():
      """Test computing mu, sigma of product of gaussians."""
      size = 5
      mu = torch.ones(size)
      sigmas_squared = torch.ones(size)
      output = product_of_gaussians(mu, sigmas_squared)
      assert output[0] == 1
      assert output[1] == 1 / size
  
imports: import collections
  import numpy
  import pytest
  import torch
  from torch import tensor
  import torch.nn.functional
  from garage.envs import GymEnv, normalize
  from garage.experiment.deterministic import set_seed
  from garage.torch import as_torch_dict, compute_advantages, flatten_to_single_vector, global_device, pad_to_last, product_of_gaussians, set_gpu_mode, state_dict_to, torch_to_np
  import garage.torch._functions
  from garage.torch.policies import DeterministicMLPPolicy
  from tests.fixtures import TfGraphTestCase
label_desc: Use image node_id 5 for calling a global function with example usage: test_product_of_gaussians() without return types