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)
  ```
docstr: Instantiates the `MoyalCDF` bijector.
  
  Args:
    loc: Float-like `Tensor` that is the same dtype and is
      broadcastable with `scale`.
      This is `loc` in
      `Y = g(X) = erfc(exp(- 1/2 * (X - loc) / scale) / sqrt(2))`.
    scale: Positive Float-like `Tensor` that is the same dtype and is
      broadcastable with `loc`.
      This is `scale` in
      `Y = g(X) = erfc(exp(- 1/2 * (X - loc) / scale) / sqrt(2))`.
    validate_args: Python `bool` indicating whether arguments should be
      checked for correctness.
    name: Python `str` name given to ops managed by this object.
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code: def __init__(
      self, loc=0.0, scale=1.0, validate_args=False, name="moyal_cdf"
  ):
      """Instantiates the `MoyalCDF` bijector.
  
      Args:
        loc: Float-like `Tensor` that is the same dtype and is
          broadcastable with `scale`.
          This is `loc` in
          `Y = g(X) = erfc(exp(- 1/2 * (X - loc) / scale) / sqrt(2))`.
        scale: Positive Float-like `Tensor` that is the same dtype and is
          broadcastable with `loc`.
          This is `scale` in
          `Y = g(X) = erfc(exp(- 1/2 * (X - loc) / scale) / sqrt(2))`.
        validate_args: Python `bool` indicating whether arguments should be
          checked for correctness.
        name: Python `str` name given to ops managed by this object.
      """
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          dtype = dtype_util.common_dtype(
              [loc, scale], dtype_hint=tf.float32
          )
          self._loc = tensor_util.convert_nonref_to_tensor(
              loc, dtype=dtype, name="loc"
          )
          self._scale = tensor_util.convert_nonref_to_tensor(
              scale, dtype=dtype, name="scale"
          )
          super(MoyalCDF, self).__init__(
              validate_args=validate_args,
              forward_min_event_ndims=0,
              parameters=parameters,
              name=name,
          )
  
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 1 to create a new MoyalCDF object from inherited base classes: bijector, bijector with example: obj = MoyalCDF(loc, scale, validate_args, name)