code: def test_torch_svd(
      *,
      dtype_and_x,
      full_matrices,
      frontend,
      test_flags,
      fn_tree,
      backend_fw,
      on_device,
  ):
      dtype, x = dtype_and_x
      x = np.asarray(x[0], dtype=dtype[0])
      # make symmetric positive definite beforehand
      x = np.matmul(x.T, x) + np.identity(x.shape[0]) * 1e-3
      ret, frontend_ret = helpers.test_frontend_function(
          input_dtypes=dtype,
          backend_to_test=backend_fw,
          frontend=frontend,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          test_values=False,
          atol=1e-03,
          rtol=1e-05,
          A=x,
          full_matrices=full_matrices,
      )
      ret = [ivy.to_numpy(x) for x in ret]
      frontend_ret = [np.asarray(x) for x in frontend_ret]
  
      u, s, vh = ret
      frontend_u, frontend_s, frontend_vh = frontend_ret
  
      assert_all_close(
          ret_np=u @ np.diag(s) @ vh,
          ret_from_gt_np=frontend_u @ np.diag(frontend_s) @ frontend_vh,
          rtol=1e-2,
          atol=1e-2,
          ground_truth_backend=frontend,
          backend=backend_fw,
      )
  
imports: import math
  import sys
  import numpy
  from hypothesis import strategies, assume
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import assert_all_close
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  from ivy_tests.test_ivy.test_frontends.test_torch.test_miscellaneous_ops import dtype_value1_value2_axis
  from ivy_tests.test_ivy.helpers.hypothesis_helpers.general_helpers import matrix_is_stable
  from ivy_tests.test_ivy.test_functional.test_core.test_linalg import _matrix_rank_helper
label_desc: Use image node_id 32 for calling a global function with example usage: test_torch_svd() without return types