code: def test_reshape(old_shape, new_shape, dim):
      gaussian = random_gaussian(old_shape, dim)
  
      # reshape to new
      new = gaussian.reshape(new_shape)
      assert new.batch_shape == new_shape
  
      # reshape back to old
      g = new.reshape(old_shape)
      assert_close_gaussian(g, 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 2 for calling a global function with example usage: test_reshape(old_shape, new_shape, dim) without return types