class_docstr: Bernoulli distribution, for binary boolean actions (specification key: `bernoulli`).
  
  Args:
      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: ["Bernoulli","distribution",",","for","binary","boolean","actions","(","specification","key",":","`","bernoulli","`",")",".","Args",":","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 entropy(self, *, parameters):
      true_logit, false_logit, probability = parameters.get(
          ("true_logit", "false_logit", "probability")
      )
  
      one = tf_util.constant(value=1.0, dtype="float")
  
      return (
          -probability * true_logit - (one - probability) * false_logit
      )
  
file_path: tensorforce/tensorforce/core/distributions/bernoulli.py
imports: 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 8 for calling the Bernoulli obj's underlying member method code with example usage: obj.entropy() and returns: unknown