class_docstr: An MLPHead that implements floating log stds for Gaussian distributions.
code: def _forward(self, inputs: torch.Tensor, **kwargs) -> torch.Tensor:
      # Compute the mean first, then append the log_std.
      mean = self.net(inputs)
  
      return torch.cat(
          [mean, self.log_std.unsqueeze(0).repeat([len(mean), 1])],
          axis=1,
      )
  
imports: import functools
  from typing import Optional
  import numpy
  from ray.rllib.core.models.base import Model
  from ray.rllib.core.models.configs import CNNTransposeHeadConfig, FreeLogStdMLPHeadConfig, MLPHeadConfig
  from ray.rllib.core.models.specs.checker import SpecCheckingError
  from ray.rllib.core.models.specs.specs_base import Spec
  from ray.rllib.core.models.specs.specs_base import TensorSpec
  from ray.rllib.core.models.torch.base import TorchModel
  from ray.rllib.core.models.torch.primitives import TorchCNNTranspose, TorchMLP
  from ray.rllib.models.utils import get_initializer_fn
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
label_desc: Use image node_id 4 for calling the TorchFreeLogStdMLPHead obj's underlying member method code with example usage: obj._forward(inputs) and returns: torch