class_docstr: Creates a LogitRelaxedBernoulli distribution parameterized by :attr:`probs`
  or :attr:`logits` (but not both), which is the logit of a RelaxedBernoulli
  distribution.
  
  Samples are logits of values in (0, 1). See [1] for more details.
  
  Args:
      temperature (Tensor): relaxation temperature
      probs (Number, Tensor): the probability of sampling `1`
      logits (Number, Tensor): the log-odds of sampling `1`
  
  [1] The Concrete Distribution: A Continuous Relaxation of Discrete Random
  Variables (Maddison et al, 2017)
  
  [2] Categorical Reparametrization with Gumbel-Softmax
  (Jang et al, 2017)
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code: def param_shape(self):
      return self._param.size()
  
imports: from numbers import Number
  import torch
  from torch.distributions import constraints
  from torch.distributions.distribution import Distribution
  from torch.distributions.transformed_distribution import TransformedDistribution
  from torch.distributions.transforms import SigmoidTransform
  from torch.distributions.utils import broadcast_all, clamp_probs, lazy_property, logits_to_probs, probs_to_logits
label_desc: Use image node_id 6 for calling the LogitRelaxedBernoulli obj's underlying member method code with example usage: obj.param_shape() and returns: self