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 __init__(self, loc=0.0, scale=1.0, validate_args=None):
      self.loc = loc
      self.scale = scale
      super(Cauchy, self).__init__(
          event_dim=0, validate_args=validate_args
      )
  
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 1 to create a new Cauchy object from inherited base classes: Distribution with example: obj = Cauchy(loc, scale, validate_args)