Create a random variable for SinhArcsinh.

See SinhArcsinh for more details.


Original Docstring for Distribution

Construct SinhArcsinh distribution on (-inf, inf).

Arguments (loc, scale, skewness, tailweight) must have broadcastable shape (indexing batch dimensions). They must all have the same dtype.

loc Floating-point Tensor.
scale Tensor of same dtype as loc.
skewness Skewness parameter. Default is 0.0 (no skew).
tailweight Tailweight parameter. Default is 1.0 (unchanged tailweight)
distribution tf.Distribution-like instance. Distribution that is transformed to produce this distribution. Must have a batch shape to which the shapes of loc, scale, skewness, and tailweight all broadcast. Default is tfd.Normal(batch_shape, 1.), where batch_shape is the broadcasted shape of the parameters. Typically distribution.reparameterization_type = FULLY_REPARAMETERIZED or it is a function of non-trainable parameters. WARNING: If you backprop through a SinhArcsinh sample and distribution is not FULLY_REPARAMETERIZED yet is a function of trainable variables, then the gradient will be incorrect!
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.
allow_nan_stats 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 undefined.
name Python str name prefixed to Ops created by this class.