class_docstr: Non-normalized Gamma distribution.
  
      Gamma(concentration, rate) ~ (concentration - 1) * log(s) - rate * s
docstr: Integrates out the latent variable.
docstr_tok: ["Integrates","out","the","latent","variable","."]
code: def logsumexp(self):
      """
      Integrates out the latent variable.
      """
      return (
          self.log_normalizer
          + torch.lgamma(self.concentration)
          - self.concentration * self.rate.log()
      )
  
imports: import math
  import torch
  from torch.distributions.utils import lazy_property
  from torch.nn.functional import pad
  from pyro.distributions.multivariate_studentt import MultivariateStudentT
  from pyro.distributions.torch import MultivariateNormal
  from pyro.distributions.util import broadcast_shape
  from pyro.ops.tensor_utils import precision_to_scale_tril
label_desc: Use image node_id 3 for calling the Gamma obj's underlying member method code with example usage: obj.logsumexp() and returns: unknown