code: def test_softplus_min_std(
      input_dim, output_dim, hidden_sizes, learn_std
  ):
      min_value = 2.0
  
      module = GaussianMLPModule(
          input_dim=input_dim,
          output_dim=output_dim,
          hidden_sizes=hidden_sizes,
          init_std=1.0,
          min_std=min_value,
          hidden_nonlinearity=None,
          std_parameterization="softplus",
          hidden_w_init=nn.init.zeros_,
          output_w_init=nn.init.zeros_,
          learn_std=learn_std,
      )
  
      dist = module(torch.ones(input_dim))
  
      exp_variance = torch.Tensor([min_value]).exp().add(1.0).log() ** 2
  
      assert dist.variance.equal(
          torch.full((output_dim,), exp_variance[0], dtype=torch.float)
      )
  
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 9 for calling a global function with example usage: test_softplus_min_std(input_dim, output_dim, hidden_sizes, learn_std) without return types