code: def test_convolve_shape(m, n, mode):
      signal = torch.randn(m)
      kernel = torch.randn(n)
      actual = convolve(signal, kernel, mode)
      expected = np.convolve(signal, kernel, mode=mode)
      assert actual.shape == expected.shape
  
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 6 for calling a global function with example usage: test_convolve_shape(m, n, mode) without return types