class_docstr: Create a Pareto Type I distribution object.
  
  Parameters
  ----------
  alpha : Tensor or scalar
      shape parameter of the distribution.
  scale : Tensor or scalar, default 1
      scale parameter of the distribution.
class_docstr_tok: ["Create","a","Pareto","Type","I","distribution","object",".","Parameters","--","--","--","--","--","alpha",":","Tensor","or","scalar","shape","parameter","of","the","distribution",".","scale",":","Tensor","or","scalar",",","default","1","scale","parameter","of","the","distribution","."]
code: def entropy(self):
      return np.log(self.scale / self.alpha) + 1 / self.alpha + 1
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/pareto.py
imports: from .transformed_distribution import TransformedDistribution
  from .exponential import Exponential
  from .constraint import Positive, dependent_property, GreaterThan
  from ..transformation import ExpTransform, AffineTransform
  from .utils import sample_n_shape_converter
  from ....None import np
label_desc: Use image node_id 7 for calling the Pareto obj's underlying member method code with example usage: obj.entropy() and returns: unknown