class_docstr: Creates an inverse-gamma distribution parameterized by
  `concentration` and `rate`.
  
      X ~ Gamma(concentration, rate)
      Y = 1/X ~ InverseGamma(concentration, rate)
  
  :param torch.Tensor concentration: the concentration parameter (i.e. alpha).
  :param torch.Tensor rate: the rate parameter (i.e. beta).
code: def __init__(self, concentration, rate, validate_args=None):
      base_dist = Gamma(concentration, rate)
      super().__init__(
          base_dist,
          PowerTransform(-base_dist.rate.new_ones(())),
          validate_args=validate_args,
      )
  
file_path: pyro/pyro/distributions/inverse_gamma.py
imports: from torch.distributions import constraints
  from torch.distributions.transforms import PowerTransform
  from pyro.distributions.torch import Gamma, TransformedDistribution
label_desc: Use image node_id 1 to create a new InverseGamma object from inherited base classes: TransformedDistribution with example: obj = InverseGamma(concentration, rate, validate_args)