code: def test_std_adaptive_network_output_values(
      input_dim, output_dim, hidden_sizes, std_hidden_sizes
  ):
      module = GaussianMLPIndependentStdModule(
          input_dim=input_dim,
          output_dim=output_dim,
          hidden_sizes=hidden_sizes,
          std_hidden_sizes=std_hidden_sizes,
          hidden_nonlinearity=None,
          hidden_w_init=nn.init.ones_,
          output_w_init=nn.init.ones_,
          std_hidden_nonlinearity=None,
          std_hidden_w_init=nn.init.ones_,
          std_output_w_init=nn.init.ones_,
      )
  
      dist = module(torch.ones(input_dim))
  
      exp_mean = torch.full(
          (output_dim,),
          input_dim * (torch.Tensor(hidden_sizes).prod().item()),
          dtype=torch.float,
      )
      exp_variance = (
          (input_dim * torch.Tensor(hidden_sizes).prod())
          .exp()
          .pow(2)
          .item()
      )
  
      assert dist.mean.equal(exp_mean)
      assert dist.variance.equal(
          torch.full((output_dim,), exp_variance, dtype=torch.float)
      )
      assert dist.rsample().shape == (output_dim,)
  
file_path: garage/tests/garage/torch/modules/test_gaussian_mlp_module.py
imports: import pytest
  import torch
  from torch import nn
  from garage.torch.modules.gaussian_mlp_module import GaussianMLPIndependentStdModule
  from garage.torch.modules.gaussian_mlp_module import GaussianMLPTwoHeadedModule
  from garage.torch.modules.gaussian_mlp_module import GaussianMLPModule
label_desc: Use image node_id 5 for calling a global function with example usage: test_std_adaptive_network_output_values(input_dim, output_dim, hidden_sizes, std_hidden_sizes) without return types