class_docstr: Create a relaxed Bernoulli distribution 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: ["Create","a","relaxed","Bernoulli","distribution","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):
      base_dist = _LogitRelaxedBernoulli(T, prob, logit, validate_args)
      super(RelaxedBernoulli, self).__init__(
          base_dist, SigmoidTransform()
      )
  
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 RelaxedBernoulli object from inherited base classes: TransformedDistribution with example: obj = RelaxedBernoulli(T, prob, logit, validate_args)