class_docstr: Create a half normal object, where
      X ~ Normal(0, scale)
      Y = |X| ~ HalfNormal(scale)
  
  Parameters
  ----------
  scale : Tensor or scalar, default 1
      Scale of the full Normal distribution.
code: def __init__(self, scale=1.0, validate_args=None):
      base_dist = Normal(0, scale)
      self.scale = scale
      super(HalfNormal, self).__init__(
          base_dist, AbsTransform(), validate_args=validate_args
      )
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/half_normal.py
imports: import math
  from numpy import inf
  from .transformed_distribution import TransformedDistribution
  from ..transformation import AbsTransform
  from .normal import Normal
  from .constraint import Positive
  from ....None import np
label_desc: Use image node_id 1 to create a new HalfNormal object from inherited base classes: TransformedDistribution with example: obj = HalfNormal(scale, validate_args)