class_docstr: Beta distribution, for bounded continuous actions (specification key: `beta`).
  
  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 log_probability(self, *, parameters, action):
      alpha, beta, log_norm = parameters.get(
          ("alpha", "beta", "log_norm")
      )
  
      min_value = tf_util.constant(
          value=self.action_spec.min_value, dtype="float"
      )
      max_value = tf_util.constant(
          value=self.action_spec.max_value, dtype="float"
      )
  
      action = (action - min_value) / (max_value - min_value)
  
      one = tf_util.constant(value=1.0, dtype="float")
      epsilon = tf_util.constant(value=util.epsilon, dtype="float")
  
      return (
          tf.math.xlogy(x=(beta - one), y=(action + epsilon))
          + (alpha - one) * tf.math.log1p(x=(-action + epsilon))
          - log_norm
      )
  
file_path: tensorforce/tensorforce/core/distributions/beta.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 7 for calling the Beta obj's underlying member method code with example usage: obj.log_probability() and returns: unknown