code: def test_paddle_eigh(
      *,
      dtype_and_input,
      UPLO,
      frontend,
      test_flags,
      fn_tree,
      backend_fw,
      on_device,
  ):
      input_dtype, x = dtype_and_input
      x = np.matmul(x.T, x) + np.identity(x.shape[0]) * 1e-3
      if x.dtype == ivy.float32:
          x = x.astype("float64")
          input_dtype = [ivy.float64]
      ret, frontend_ret = helpers.test_frontend_function(
          input_dtypes=input_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-4,
          x=x,
          UPLO=UPLO,
      )
      ret = [ivy.to_numpy(x).astype("float64") for x in ret]
      frontend_ret = [
          np.asarray(x, dtype=np.float64) for x in frontend_ret
      ]
  
      l, v = ret  # noqa: E741
      front_l, front_v = frontend_ret
  
      assert_all_close(
          ret_np=v @ np.diag(l) @ v.T,
          ret_from_gt_np=front_v @ np.diag(front_l) @ front_v.T,
          rtol=1e-2,
          atol=1e-2,
          backend=backend_fw,
          ground_truth_backend=frontend,
      )
  
imports: import ivy
  from hypothesis import strategies, assume
  import numpy
  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, matrix_is_stable
  from ivy_tests.test_ivy.test_functional.test_core.test_linalg import _get_dtype_and_matrix
  from ivy_tests.test_ivy.test_frontends.test_tensorflow.test_linalg import _get_second_matrix, _get_cholesky_matrix
  from ivy_tests.test_ivy.test_frontends.test_torch.test_blas_and_lapack_ops import _get_dtype_input_and_mat_vec
label_desc: Use image node_id 18 for calling a global function with example usage: test_paddle_eigh() without return types