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 __init__(self, T, prob=None, logit=None, validate_args=None):
      self.T = T
      if (prob is None) == (logit is None):
          raise ValueError(
              "Either `prob` or `logit` must be specified, but not both. "
              + "Received prob={}, logit={}".format(prob, logit)
          )
      if prob is not None:
          self.prob = prob
      else:
          self.logit = logit
      super(_LogitRelaxedBernoulli, self).__init__(
          event_dim=0, validate_args=validate_args
      )
  
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 1 to create a new _LogitRelaxedBernoulli object from inherited base classes: Distribution with example: obj = _LogitRelaxedBernoulli(T, prob, logit, validate_args)