code: def gamma_gaussian_tensordot(x, y, dims=0):
      """
      Computes the integral over two GammaGaussians:
  
          `(x @ y)((a,c),s) = log(integral(exp(x((a,b),s) + y((b,c),s)), b))`,
  
      where `x` is a gaussian over variables (a,b), `y` is a gaussian over variables
      (b,c), (a,b,c) can each be sets of zero or more variables, and `dims` is the size of b.
  
      :param x: a GammaGaussian instance
      :param y: a GammaGaussian instance
      :param dims: number of variables to contract
      """
      assert isinstance(x, GammaGaussian)
      assert isinstance(y, GammaGaussian)
      na = x.dim() - dims
      nb = dims
      nc = y.dim() - dims
      assert na >= 0
      assert nb >= 0
      assert nc >= 0
  
      device = x.info_vec.device
      perm = torch.cat(
          [
              torch.arange(na, device=device),
              torch.arange(x.dim(), x.dim() + nc, device=device),
              torch.arange(na, x.dim(), device=device),
          ]
      )
      return (
          (x.event_pad(right=nc) + y.event_pad(left=na))
          .event_permute(perm)
          .marginalize(right=nb)
      )
  
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 4 for calling a global function with example usage: gamma_gaussian_tensordot(x, y, dims) and returns: unknown