TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  input_tensors->reserve(ninputs);
  for (int i = 0; i < ninputs; ++i) {
    Node* node = inputs[i].oper ? &inputs[i].oper->node : nullptr;
    int idx = inputs[i].index;

    TF_RETURN_WITH_CONTEXT_IF_ERROR(
        fn_body->graph.IsValidOutputTensor(node, idx),
        "Encountered while processing input ", i, " into function '", fn_name,
        "'");

	for i = range uint(8) {
		fi := FileInfo{
			TransitionTier:      inputs[0].tier,
			TransitionedObjName: inputs[0].remoteObjName,
			TransitionVersionID: inputs[0].remoteVersionID,
			TransitionStatus:    inputs[0].status,
		}
		ofi := fi
		if i&(1<<0) != 0 {
			ofi.TransitionTier = inputs[1].tier
		}
		if i&(1<<1) != 0 {
			ofi.TransitionedObjName = inputs[1].remoteObjName
		}
		if i&(1<<2) != 0 {

	testCases := []struct {
		args       []string
		totalSizes []uint64
		indexes    [][]uint64
		success    bool
	}{
		// Invalid inputs.
		{
			[]string{"data{1...17}/export{1...52}"},
			[]uint64{14144},
			nil,
			false,
		},
		// Valid inputs.
		{
			[]string{"data{1...3}"},
			[]uint64{3},
			[][]uint64{{3}},
			true,
		},
		{

				"us-west-1",
				"s3",
				"ABCD"),
			expectedCredentials: credentialHeader{},
			expectedErrCode:     ErrInvalidRequestVersion,
		},
		// Test Case - 9.
		// Test case with right inputs. Expected to return a valid CredentialHeader.
		// "aws4_request" is the valid request version.
		{
			inputCredentialStr: generateCredentialStr(
				"Z7IXGOO6BZ0REAN1Q26I",
				sampleTimeStr,
				"us-west-1",

	var errs []error
	allAtEOF := true
	var inputs []chan metaCacheEntry
	mu.Lock()
	// Ask all sets and merge entries.
	listCtx, cancelList := context.WithCancel(ctx)
	defer cancelList()
	for _, pool := range z.serverPools {
		for _, set := range pool.sets {
			wg.Add(1)
			innerResults := make(chan metaCacheEntry, 100)
			inputs = append(inputs, innerResults)
			go func(i int, set *erasureObjects) {

    }

    @ParameterizedTest
    @ValueSource(strings = { "", "password", "pässwörd", "1234567890abcdef" })
    @DisplayName("nTOWFv1 equals getNTHash across inputs")
    void testNTOWFv1_delegatesToGetNTHash(String password) {
        // Act
        byte[] h1 = NtlmUtil.nTOWFv1(password);
        byte[] h2 = NtlmUtil.getNTHash(password);

        // Assert

  TF_Operation* neg = Neg(feed, func_graph.get(), s.get());

  TF_Output inputs[] = {{feed, 0}};
  TF_Output outputs[] = {{neg, 0}};
  *func = TF_GraphToFunction(func_graph.get(), name, append_hash, -1,
                             /*opers=*/nullptr, 1, inputs, 1, outputs,
                             /*output_names=*/nullptr,
                             /*opts=*/nullptr, description, s.get());

        SmbSession result = transport.getSmbSession(ctx);
        assertSame(session, result);
        verify(transport).getSmbSession(eq(ctx));
    }

    // Edge inputs: null context returns null session per mock setup
    @Test
    @DisplayName("getSmbSession(ctx) with null context returns null as stubbed")
    void getSmbSession_byContext_null() {

					Signature: [4]byte{2, 1, 1, 1},
					Type:      ObjectType,
					Flags:     0,
				}},
			},
		},
	}
	inputSerialized := make([]metaCacheEntry, len(inputs))
	for i, xl := range inputs {
		xl.sortByModTime()
		var err error
		entry := metaCacheEntry{
			name: "testobject",
		}
		entry.metadata, err = xl.AppendTo(nil)
		if err != nil {
			t.Fatal(err)
		}

// `shape_inference::InferenceContext` constructor. Note the following:
//   - The inputs of the `op` are not used for shape inference. So, it is
//     OK to not have the inputs properly set in `op`. See `input_tensors`
//     if you want shape inference to consider the input tensors of the
//     op for shape inference.
//   - The types need not be set in `input_shapes` as it is not used.