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 _maybe_assert_valid_y(self, y):
      if not self.validate_args:
          return []
      is_positive = assert_util.assert_non_negative(
          y,
          message="Inverse transformation input must be greater than 0.",
      )
      less_than_one = assert_util.assert_less_equal(
          y,
          tf.constant(1.0, y.dtype),
          message="Inverse transformation input must be less than or equal to 1.",
      )
      return [is_positive, less_than_one]
  
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 10 for calling the MoyalCDF obj's underlying member method code with example usage: obj._maybe_assert_valid_y(y) and returns: list, list