class_docstr: Gaussian distribution, for continuous actions (specification key: `gaussian`).
  
  Args:
      stddev_mode ("predicted" | "global"): Whether to predict the standard deviation via a linear
          transformation of the state embedding, or to parametrize the standard deviation by a
          separate set of trainable weights
          (<span style="color:#00C000"><b>default</b></span>: "predicted").
      bounded_transform ("clipping" | "tanh"): Transformation to adjust sampled actions in case of
          bounded action space, "tanh" transforms distribution (e.g. log probability computation)
          accordingly whereas "clipping" does not
          (<span style="color:#00C000"><b>default</b></span>: tanh).
      name (string): <span style="color:#0000C0"><b>internal use</b></span>.
      action_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      input_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
class_docstr_tok: ["Gaussian","distribution",",","for","continuous","actions","(","specification","key",":","`","gaussian","`",")",".","Args",":","stddev_mode","(","``","predicted","''","|","``","global","''",")",":","Whether","to","predict","the","standard","deviation","via","a","linear","transformation","of","the","state","embedding",",","or","to","parametrize","the","standard","deviation","by","a","separate","set","of","trainable","weights","(","<","span","style=","''","color",":","#","00C000","''",">","<","b",">","default","<","\/b",">","<","\/span",">",":","``","predicted","''",")",".","bounded_transform","(","``","clipping","''","|","``","tanh","''",")",":","Transformation","to","adjust","sampled","actions","in","case","of","bounded","action","space",",","``","tanh","''","transforms","distribution","(","e.g",".","log","probability","computation",")","accordingly","whereas","``","clipping","''","does","not","(","<","span","style=","''","color",":","#","00C000","''",">","<","b",">","default","<","\/b",">","<","\/span",">",":","tanh",")",".","name","(","string",")",":","<","span","style=","''","color",":","#","0000C0","''",">","<","b",">","internal","use","<","\/b",">","<","\/span",">",".","action_spec","(","specification",")",":","<","span","style=","''","color",":","#","0000C0","''",">","<","b",">","internal","use","<","\/b",">","<","\/span",">",".","input_spec","(","specification",")",":","<","span","style=","''","color",":","#","0000C0","''",">","<","b",">","internal","use","<","\/b",">","<","\/span",">","."]
code: def state_value(self, *, parameters):
      log_stddev = parameters["log_stddev"]
  
      half_lg_two_pi = tf_util.constant(
          value=(0.5 * np.log(2.0 * np.pi)), dtype="float"
      )
      # TODO: why no e here, but for entropy?
  
      return -log_stddev - half_lg_two_pi
  
file_path: tensorforce/tensorforce/core/distributions/gaussian.py
imports: import numpy
  import tensorflow
  from tensorforce import TensorforceError, util
  from tensorforce.core import layer_modules, TensorDict, TensorSpec, TensorsSpec, tf_function, tf_util
  from tensorforce.core.distributions import Distribution
label_desc: Use image node_id 11 for calling the Gaussian obj's underlying member method code with example usage: obj.state_value() and returns: unknown