code: def test_svd_LM_zeros_matrix_gh_3452():
      # Regression test for a github issue.
      # https://github.com/scipy/scipy/issues/3452
      # Note that for complex dype the size of this matrix is too small for k=1.
      n, m, k = 4, 2, 1
      A = np.zeros((n, m))
      for solver in [None, "arpack", "lobpcg"]:
          U, s, VH = svds(A, k, solver=solver)
  
          # Check some generic properties of svd.
          _check_svds(A, k, U, s, VH)
  
          # Check that the singular values are zero.
          assert_array_equal(s, 0)
  
imports: import threading
  import itertools
  import sys
  import platform
  import numpy
  from numpy.testing import assert_allclose, assert_array_almost_equal_nulp, assert_equal, assert_array_equal, suppress_warnings
  from pytest import raises
  import pytest
  from numpy import dot, conj, random
  from scipy.linalg import eig, eigh, hilbert, svd
  from scipy.sparse import csc_matrix, csr_matrix, isspmatrix, diags, rand
  from scipy.sparse.linalg import LinearOperator, aslinearoperator
  from scipy.sparse.linalg.eigen.arpack import eigs, eigsh, svds, ArpackNoConvergence, arpack
  from scipy._lib._gcutils import assert_deallocated, IS_PYPY
label_desc: Use image node_id 32 for calling a global function with example usage: test_svd_LM_zeros_matrix_gh_3452() without return types