code: def test_convolve(batch_shape, m, n, mode):
      signal = torch.randn(*batch_shape, m)
      kernel = torch.randn(*batch_shape, n)
      actual = convolve(signal, kernel, mode)
      expected = torch.stack(
          [
              torch.tensor(np.convolve(s, k, mode=mode))
              for s, k in zip(
                  signal.reshape(-1, m), kernel.reshape(-1, n)
              )
          ]
      ).reshape(*batch_shape, -1)
      assert_close(actual, expected)
  
imports: import math
  import numpy
  import pytest
  import scipy.fftpack
  import torch
  import pyro
  from pyro.ops.tensor_utils import block_diag_embed, block_diagonal, convolve, dct, idct, next_fast_len, periodic_cumsum, periodic_features, periodic_repeat, precision_to_scale_tril, repeated_matmul
  from tests.common import assert_close, assert_equal
label_desc: Use image node_id 7 for calling a global function with example usage: test_convolve(batch_shape, m, n, mode) without return types