An N-D Tensor. Each entry in the Tensor parameterizes
an independent continuous Bernoulli distribution with parameter
sigmoid(logits). Only one of logits or probs should be passed
in. Note that this does not correspond to the log-odds as in the
An N-D Tensor representing the parameter of a continuous
Bernoulli. Each entry in the Tensor parameterizes an independent
continuous Bernoulli distribution. Only one of logits or probs
should be passed in. Note that this also does not correspond to a
probability as in the Bernoulli case.
A list with two floats containing the lower and upper limits
used to approximate the continuous Bernoulli around 0.5 for
numerical stability purposes.
The type of the event samples. Default: float32.
validate_args: Python bool, default False. When True
distribution parameters are checked for validity despite possibly
degrading runtime performance. When False invalid inputs may
silently render incorrect outputs.
Python bool, default True. When True,
statistics (e.g., mean, mode, variance) use the value "NaN" to
indicate the result is undefined. When False, an exception is
raised if one or more of the statistic's batch members are
Python str name prefixed to Ops created by this class.
If probs and logits are passed, or if neither are passed.