code: def test_cat(shape, cat_dim, split, dim):
      assert sum(split) == shape[cat_dim]
      gaussian = random_gaussian(shape, dim)
      parts = []
      end = 0
      for size in split:
          beg, end = end, end + size
          if cat_dim == -1:
              part = gaussian[..., beg:end]
          elif cat_dim == -2:
              part = gaussian[..., beg:end, :]
          elif cat_dim == 1:
              part = gaussian[:, beg:end]
          else:
              raise ValueError
          parts.append(part)
  
      actual = Gaussian.cat(parts, cat_dim)
      assert_close_gaussian(actual, gaussian)
  
imports: import math
  from collections import OrderedDict
  import pytest
  import torch
  from torch.distributions import constraints, transform_to
  from torch.nn.functional import pad
  import pyro.distributions
  from pyro.distributions.util import broadcast_shape
  from pyro.ops.gaussian import AffineNormal, Gaussian, gaussian_tensordot, matrix_and_gaussian_to_gaussian, matrix_and_mvn_to_gaussian, mvn_to_gaussian, sequential_gaussian_filter_sample, sequential_gaussian_tensordot
  from tests.common import assert_close
  from tests.ops.gaussian import assert_close_gaussian, random_gaussian, 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