class_docstr: Create a relaxed Cauchy distribution object.
  
  Parameters
  ----------
  loc : Tensor or scalar, default 0
      mode or median of the distribution
  scale : Tensor or scalar, default 1
      half width at half maximum
class_docstr_tok: ["Create","a","relaxed","Cauchy","distribution","object",".","Parameters","--","--","--","--","--","loc",":","Tensor","or","scalar",",","default","0","mode","or","median","of","the","distribution","scale",":","Tensor","or","scalar",",","default","1","half","width","at","half","maximum"]
code: def cdf(self, value):
      if self._validate_args:
          self._validate_samples(value)
      return np.arctan((value - self.loc) / self.scale) / pi + 0.5
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/cauchy.py
imports: from numbers import Number
  from numpy import nan, pi
  from .constraint import Real
  from .distribution import Distribution
  from .utils import sample_n_shape_converter
  from ....None import np
label_desc: Use image node_id 7 for calling the Cauchy obj's underlying member method code with example usage: obj.cdf(value) and returns: unknown