if !kl.syncLoopIteration(ctx, updates, handler, syncTicker.C, housekeepingTicker.C, plegCh) {
			break
		}
		kl.syncLoopMonitor.Store(kl.clock.Now())
	}
}

// syncLoopIteration reads from various channels and dispatches pods to the
// given handler.
//
// Arguments:
// 1.  configCh:       a channel to read config events from
// 2.  handler:        the SyncHandler to dispatch pods to

			t.Fatalf("Test %d: %s: Failed to create HTTP request for Head Object: <ERROR> %v", i+1, instanceType, err)
		}
		// Since `apiRouter` satisfies `http.Handler` it has a ServeHTTP to execute the logic of the handler.
		// Call the ServeHTTP to execute the handler,`func (api objectAPIHandlers) GetObjectHandler`  handles the request.
		apiRouter.ServeHTTP(rec, req)

		// Assert the response code with the expected status.

                       ResourceEffects::TPUExecute::get());

  // Conservatively mark resource handles as read and write, as without
  // analyzing TPUCompile, there is not sufficient information to determine
  // effects on resources. For the MLIR bridge, this op will never be
  // populated with resource handles and tf.TPUExecuteAndUpdateVariables is
  // used instead.
  for (Value value : getArgs()) {

    decomposition/lifting occurs with the unpartitioned resource variables,
    transforming the IR in such a manner will allow for subsequent passes to operate
    on individual resource variable handles per core/device.

    For example, the following:

    ```mlir
    func @cluster(%arg0: tensor<!tf_type.resource<tensor<i32>>>, %arg1: tensor<!tf_type.resource<tensor<i32>>>) {

// always refers to user code.

// Handle registers the handler for the given pattern.
// If the given pattern conflicts, with one that is already registered, Handle
// panics.
func (mux *ServeMux) Handle(pattern string, handler Handler) {
	if use121 {
		mux.mux121.handle(pattern, handler)
	} else {
		mux.register(pattern, handler)
	}
}

    auto params_type = dyn_cast<RankedTensorType>(params_value.getType());
    auto indices_type = dyn_cast<RankedTensorType>(indices_value.getType());

    // Handle static shape cases only.
    if (!result_type || !params_type || !indices_type) return failure();

    // Handle i32 indices and f32 input only.
    if (indices_type.getElementType() != rewriter.getI32Type() ||
        params_type.getElementType() != rewriter.getF32Type()) {

//			000000000000013d:  IMAGE_REL_AMD64_REL32	_errno
//
// The assembly below dispenses with the import symbol and just makes
// a direct call to _errno.
//
// The code below handles indirect refs by redirecting the target of
// the relocation from "__imp_XYZ" to "XYZ" (since the latter symbol
// is what the Windows loader is expected to resolve). For direct refs

	handler := genericapifilters.WithAudit(mux, auditSink, fakeRuleEvaluator, longRunningCheck)
	handler = genericapifilters.WithRequestDeadline(handler, auditSink, fakeRuleEvaluator, longRunningCheck, codecs, 60*time.Second)
	handler = genericapifilters.WithRequestInfo(handler, testRequestInfoResolver())
	handler = genericapifilters.WithAuditInit(handler)

	return &defaultAPIServer{handler, container}
}

  // expected-remark@above {{Sinks: {6}}}
}

// -----

// Tests that two resources allocated with identical non-empty `shared_name`
// attributes are dependent. That means, `%handle1` and `%handle2` point to
// the same resources and the second `InitializeTableV2` op depends on the
// first one.
func.func @resources_allocated_with_same_nonempty_shared_name(
  // expected-remark@above {{ID: 9}}

		closec: make(chan bool, 1),
	}
	handled := 0
	handler := HandlerFunc(func(rw ResponseWriter, r *Request) {
		handled++
		rw.Header().Set("Content-Type", "text/html; charset=utf-8")
		rw.Write(res)
	})
	ln := &oneConnListener{conn: conn}
	go Serve(ln, handler)
	<-conn.closec
	if b.N != handled {
		b.Errorf("b.N=%d but handled %d", b.N, handled)
	}
}