class_docstr: Helper class for creating an unnormalized relaxed Bernoulli object.
  
  Parameters
  ----------
  T : scalar, default None
      Relaxation temperature
  prob : Tensor or scalar, default None
      Probability of sampling `1`.
  logit : Tensor or scalar, default None
      The log-odds of sampling `1`.
class_docstr_tok: ["Helper","class","for","creating","an","unnormalized","relaxed","Bernoulli","object",".","Parameters","--","--","--","--","--","T",":","scalar",",","default","None","Relaxation","temperature","prob",":","Tensor","or","scalar",",","default","None","Probability","of","sampling","`","1","`",".","logit",":","Tensor","or","scalar",",","default","None","The","log-odds","of","sampling","`","1","`","."]
code: def log_prob(self, value):
      # log-likelihood of `value` from (Logistic(logit, 1) / T)
      diff = self.logit - self.T * value
      return np.log(self.T) + diff - 2 * np.log1p(np.exp(diff))
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/relaxed_bernoulli.py
imports: from .distribution import Distribution
  from .transformed_distribution import TransformedDistribution
  from ..transformation import SigmoidTransform
  from .utils import prob2logit, logit2prob, cached_property
  from .constraint import OpenInterval, Real, Interval
  from ....None import np
label_desc: Use image node_id 5 for calling the _LogitRelaxedBernoulli obj's underlying member method code with example usage: obj.log_prob(value) and returns: unknown