class_docstr: Tests for the vectorized computation of KL divergences.
class_docstr_tok: ["Tests","for","the","vectorized","computation","of","KL","divergences","."]
code: def _get(self, name: str):
      if name == "h":
          mean, var = self.h_mean, self.h_var
      elif name == "r":
          mean, var = self.r_mean, self.r_var
      elif name == "t":
          mean, var = self.t_mean, self.t_var
      elif name == "e":
          mean, var = self.h_mean - self.t_mean, self.h_var + self.t_var
      else:
          raise ValueError
      return GaussianDistribution(mean=mean, diagonal_covariance=var)
  
file_path: pykeen/tests/test_nn/test_sim.py
imports: import itertools
  import unittest
  import torch
  from pykeen.nn.sim import _torch_kl_similarity, kullback_leibler_similarity
  from pykeen.typing import GaussianDistribution
  from pykeen.utils import convert_to_canonical_shape
label_desc: Use image node_id 2 for calling the KullbackLeiblerTests obj's underlying member method code with example usage: obj._get(name) and returns: GaussianDistribution