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 action_value(self, *, parameters, action):
      true_logit, false_logit, state_value = parameters.get(
          ("true_logit", "false_logit", "state_value")
      )
  
      logits = tf.where(condition=action, x=true_logit, y=false_logit)
  
      return state_value + logits
  
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 10 for calling the Bernoulli obj's underlying member method code with example usage: obj.action_value() and returns: unknown