code: def test_sequential_gaussian_tensordot(
      batch_shape, state_dim, num_steps
  ):
      g = random_gaussian(
          batch_shape + (num_steps,), state_dim + state_dim
      )
      actual = sequential_gaussian_tensordot(g)
      assert actual.dim() == g.dim()
      assert actual.batch_shape == batch_shape
  
      # Check against hand computation.
      expected = g[..., 0]
      for t in range(1, num_steps):
          expected = gaussian_tensordot(expected, g[..., t], state_dim)
      assert_close_gaussian(actual, expected)
  
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 19 for calling a global function with example usage: test_sequential_gaussian_tensordot(batch_shape, state_dim, num_steps) without return types