class_docstr: An MLPHead that implements floating log stds for Gaussian distributions.
code: def _forward(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor:
      # Compute the mean first, then append the log_std.
      mean = self.net(inputs)
      log_std_out = tf.tile(
          tf.expand_dims(self.log_std, 0), [tf.shape(inputs)[0], 1]
      )
      logits_out = tf.concat([mean, log_std_out], axis=1)
      return logits_out
  
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.tf.base import TfModel
  from ray.rllib.core.models.tf.primitives import TfCNNTranspose, TfMLP
  from ray.rllib.models.utils import get_initializer_fn
  from ray.rllib.utils import try_import_tf
  from ray.rllib.utils.annotations import override
label_desc: Use image node_id 4 for calling the TfFreeLogStdMLPHead obj's underlying member method code with example usage: obj._forward(inputs) and returns: logits_out