}

// RemoveTarget - removes a remote bucket target for this source bucket.
func (sys *BucketTargetSys) RemoveTarget(ctx context.Context, bucket, arnStr string) error {
	if arnStr == "" {
		return BucketRemoteArnInvalid{Bucket: bucket}
	}

	arn, err := madmin.ParseARN(arnStr)
	if err != nil {
		return BucketRemoteArnInvalid{Bucket: bucket}
	}

	if arn.Type == madmin.ReplicationService {

					val = fmt.Sprintf("%#v", out)
				} else {
					val = fmt.Sprintf("%T(%v)", out, out)
				}
				fmt.Fprintf(&buf, "\t%s, // %s(%s)\n", val, m.Name, argstr)
			} else if p >= len(regressGolden) {
				t.Errorf("r.%s(%s) = %v, missing golden value", m.Name, argstr, out)
			} else {
				want := regressGolden[p]
				if m.Name == "Int" {
					want = int64(int(uint(want.(int64)) << 1 >> 1))
				}
				if m.Name == "Uint" {

        for (Object argument : arguments) {
            String argStr = argument.toString();

            if (argStr.equals("-ea") || argStr.equals("-enableassertions")) {
                assertionsEnabled = true;
            } else if (argStr.equals("-da") || argStr.equals("-disableassertions")) {
                assertionsEnabled = false;
            } else if (argStr.startsWith(XMS_PREFIX)) {

			// Parse line.
			if len(parts) != 3 {
				log.Fatalf(`%s:%d: invalid symabi: syntax is "%s sym abi"`, file, lineNum, parts[0])
			}
			sym, abistr := parts[1], parts[2]
			abi, valid := obj.ParseABI(abistr)
			if !valid {
				log.Fatalf(`%s:%d: invalid symabi: unknown abi "%s"`, file, lineNum, abistr)
			}

			sym = s.canonicalize(sym)

			// Record for later.
			if parts[0] == "def" {
				s.defs[sym] = abi
			} else {

  TFE_DeleteContextOptions(opts);

  std::string model_dir = SavedModelPath("VarsAndArithmeticObjectGraph");

  TF_SavedModel* saved_model =
      TF_LoadSavedModelWithTags(model_dir.c_str(), ctx, kServeTag, 1, status);

  // TODO(bmzhao): Change this to expect TF_OK when loading is implemented.
  // That unblocks writing other tests that require a TF_SavedModel*,

  std::string mat_mul_method =
      absl::StrFormat(kMatMulModuleStr, test_case.mat_mul_method);
  TF_ASSERT_OK_AND_ASSIGN(
      XlaCompiler::CompilationResult result,
      CompileMlirModule(
          mat_mul_method.c_str(),
          ConfigProto::Experimental::MLIR_BRIDGE_ROLLOUT_UNSPECIFIED));
}

INSTANTIATE_TEST_SUITE_P(
    BatchMatMulTest, BatchMatMulTest,
    ::testing::ValuesIn<MatMulTestCase>({
        {"BatchMatMul"},

        {
            "detail": [
                {
                    "loc": ["path", "item_id"],
                    "msg": "ensure this value has at least 3 characters",
                    "type": "value_error.any_str.min_length",
                    "ctx": {"limit_value": 3},
                }
            ]
        }
    )


def test_path_param_maxlength_foo():

        | IsDict(
            {
                "ctx": {"limit_value": 1},
                "loc": ["query", "foo"],
                "msg": "ensure this value has at least 1 characters",
                "type": "value_error.any_str.min_length",
            }
        )
    ]
}


@pytest.mark.parametrize(
    "path,expected_status,expected_response",
    [
        ("/default", 200, {"foo": "foo"}),

				req.AddCookie(c)
			}

			got := req.CookiesNamed(tt.filter)

			if !reflect.DeepEqual(got, tt.want) {
				asStr := func(v any) string {
					blob, _ := json.MarshalIndent(v, "", "  ")
					return string(blob)
				}
				t.Fatalf("Result mismatch\n\tGot: %s\n\tWant: %s", asStr(got), asStr(tt.want))
			}
		})
	}
}

const (
	fileaContents = "This is a test file."

  return FlatBufferModel::BuildFromFile(model_path.c_str());
}

std::unique_ptr<FlatBufferModel> ReadSharedWeightsTestModel() {
  auto model_path = tensorflow::io::JoinPath(*g_test_model_dir,
                                             internal::kModelWithSharedWeights);
  return FlatBufferModel::BuildFromFile(model_path.c_str());
}

std::unique_ptr<FlatBufferModel> ReadGatherTestModel() {