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 _parameter_control_dependencies(self, is_init):
      if not self.validate_args:
          return []
      assertions = []
      if is_init != tensor_util.is_ref(self.scale):
          assertions.append(
              assert_util.assert_positive(
                  self.scale,
                  message="Argument `scale` must be positive.",
              )
          )
      return assertions
  
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 11 for calling the MoyalCDF obj's underlying member method code with example usage: obj._parameter_control_dependencies(is_init) and returns: assertions, list