class_docstr: Creates a half-Cauchy distribution parameterized by `scale` where::
  
      X ~ Cauchy(0, scale)
      Y = |X| ~ HalfCauchy(scale)
  
  Example::
  
      >>> # xdoctest: +IGNORE_WANT("non-deterministic")
      >>> m = HalfCauchy(torch.tensor([1.0]))
      >>> m.sample()  # half-cauchy distributed with scale=1
      tensor([ 2.3214])
  
  Args:
      scale (float or Tensor): scale of the full Cauchy distribution
class_docstr_tok: ["Creates","a","half-Cauchy","distribution","parameterized","by","`","scale","`","where",":",":","X","~","Cauchy","(","0",",","scale",")","Y","=","|X|","~","HalfCauchy","(","scale",")","Example",":",":",">",">",">","#","xdoctest",":","+IGNORE_WANT","(","``","non-deterministic","''",")",">",">",">","m","=","HalfCauchy","(","torch.tensor","(","[","1.0","]",")",")",">",">",">","m.sample","(",")","#","half-cauchy","distributed","with","scale=1","tensor","(","[","2.3214","]",")","Args",":","scale","(","float","or","Tensor",")",":","scale","of","the","full","Cauchy","distribution"]
code: def cdf(self, value):
      if self._validate_args:
          self._validate_sample(value)
      return 2 * self.base_dist.cdf(value) - 1
  
file_path: pytorch/torch/distributions/half_cauchy.py
imports: import math
  import torch
  from torch import inf
  from torch.distributions import constraints
  from torch.distributions.cauchy import Cauchy
  from torch.distributions.transformed_distribution import TransformedDistribution
  from torch.distributions.transforms import AbsTransform
label_desc: Use image node_id 8 for calling the HalfCauchy obj's underlying member method code with example usage: obj.cdf(value) and returns: unknown