class_docstr: Compute `Y = g(X) = erfc(exp(- 1/2 * (X - loc) / scale) / sqrt(2))`.
  
  This bijector maps inputs from `[-inf, inf]` to `[0, 1]`. The inverse of the
  bijector applied to a uniform random variable `X ~ U(0, 1)` gives back a
  random variable with the Moyal distribution:
  
  ```none
  Y ~ MoyalCDF(loc, scale)
  pdf(y; loc, scale) = exp(
    - 1/2 * ( (y - loc) / scale + exp(- (y - loc) / scale) ) ) /
    (sqrt(2 * pi) * scale)
  ```
code: def _inverse_log_det_jacobian(self, y):
      with tf.control_dependencies(self._maybe_assert_valid_y(y)):
          np_dtype = dtype_util.as_numpy_dtype(y.dtype)
          return (
              tf.math.square(tfp_math.erfcinv(y))
              + tf.math.log(self.scale)
              + 0.5 * np_dtype(np.log(np.pi))
              - tf.math.log(tfp_math.erfcinv(y))
          )
  
imports: import numpy
  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 special
label_desc: Use image node_id 8 for calling the MoyalCDF obj's underlying member method code with example usage: obj._inverse_log_det_jacobian(y) and returns: unknown