class_docstr: `Y = g(X) = Sinh( (Arcsinh(X) + skewness) * tailweight ) * multiplier`.
  
  For `skewness in (-inf, inf)` and `tailweight in (0, inf)`, this
  transformation is a
  diffeomorphism of the real line `(-inf, inf)`.  The inverse transform is
  `X = g^{-1}(Y) = Sinh( ArcSinh(Y) / tailweight - skewness )`.
  
  The `SinhArcsinh` transformation of the Normal is described in
  [Sinh-arcsinh distributions](https://www.jstor.org/stable/27798865)
  This Bijector allows a similar transformation of any distribution supported on
  `(-inf, inf)`.
  
  #### Meaning of the parameters
  
  * If `skewness = 0` and `tailweight = 1`, this transform is the identity.
  * Positive (negative) `skewness` leads to positive (negative) skew.
    * positive skew means, for unimodal `X` centered at zero, the mode of `Y` is
      "tilted" to the right.
    * positive skew means positive values of `Y` become more likely, and
      negative values become less likely.
  * Larger (smaller) `tailweight` leads to fatter (thinner) tails.
    * Fatter tails mean larger values of `|Y|` become more likely.
    * If `X` is a unit Normal, `tailweight < 1` leads to a distribution that is
      "flat" around `Y = 0`, and a very steep drop-off in the tails.
    * If `X` is a unit Normal, `tailweight > 1` leads to a distribution more
      peaked at the mode with heavier tails.
  * The `multiplier` term is equal to 2 / F_0(2) where F_0 is
    the bijector with `skewness = 0`. This is important
    for CDF values of distributions.SinhArcsinh.
  
  To see the argument about the tails, note that for `|X| >> 1` and
  `|X| >> (|skewness| * tailweight)**tailweight`, we have
  `Y approx 0.5 X**tailweight e**(sign(X) skewness * tailweight)`.
code: def _forward_log_det_jacobian(self, x):
      # y = sinh((arcsinh(x) + skewness) * tailweight) * multiplier
      # Using sinh' = cosh, arcsinh'(x) = 1 / sqrt(x**2 + 1),
      # dy/dx
      # = cosh((arcsinh(x) + skewness) * tailweight) * tailweight / sqrt(x**2 + 1)
      # * multiplier
  
      tailweight = tf.convert_to_tensor(self.tailweight)
  
      return (
          generic.log_cosh((tf.asinh(x) + self.skewness) * tailweight)
          - 0.5 * numeric.log1psquare(x)
          + tf.math.log(tailweight)
          + tf.math.log(self._output_multiplier(tailweight))
      )
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math import generic
  from tensorflow_probability.python.math import numeric
label_desc: Use image node_id 10 for calling the SinhArcsinh obj's underlying member method code with example usage: obj._forward_log_det_jacobian(x) and returns: unknown