code: def test_cat(shape, cat_dim, split, dim):
      assert sum(split) == shape[cat_dim]
      gamma_gaussian = random_gamma_gaussian(shape, dim)
      parts = []
      end = 0
      for size in split:
          beg, end = end, end + size
          if cat_dim == -1:
              part = gamma_gaussian[..., beg:end]
          elif cat_dim == -2:
              part = gamma_gaussian[..., beg:end, :]
          elif cat_dim == 1:
              part = gamma_gaussian[:, beg:end]
          else:
              raise ValueError
          parts.append(part)
  
      actual = GammaGaussian.cat(parts, cat_dim)
      assert_close_gamma_gaussian(actual, gamma_gaussian)
  
imports: import math
  import pytest
  import torch
  from torch.nn.functional import pad
  import pyro.distributions
  from pyro.distributions.util import broadcast_shape
  from pyro.ops.gamma_gaussian import GammaGaussian, gamma_and_mvn_to_gamma_gaussian, gamma_gaussian_tensordot, matrix_and_mvn_to_gamma_gaussian
  from tests.common import assert_close
  from tests.ops.gamma_gaussian import assert_close_gamma_gaussian, random_gamma, random_gamma_gaussian
  from tests.ops.gaussian import random_mvn
label_desc: Use image node_id 3 for calling a global function with example usage: test_cat(shape, cat_dim, split, dim) without return types