// Prefer to re-use existing types from expanding context, if it exists, to reduce
	// the memory pinned by the Named type.
	updateContexts := func(res Type) Type {
		for i := len(ctxts) - 1; i >= 0; i-- {
			res = ctxts[i].update(hashes[i], orig, targs, res)
		}
		return res
	}

	// typ may already have been instantiated with identical type arguments. In
	// that case, re-use the existing instance.

// Size(x) = Prod(Shape(x), reduction_indices=0, keep_dims=false)
def LowerSizeOp : Pat<
  (TF_SizeOp:$res $arg),
  (TF_ProdOp
    (CreateTFShapeOp
      $res,
      $arg,
      (IsI32 $res)
    ),
    /*reduction_indices=*/
    (TF_ConstOp
      (GetScalarOfType<0> $res)
    ),
    /*keep_dims=*/
    ConstBoolAttrFalse
  )>;

func (m *Manager) HostName() string {
	return m.local
}

// Targets returns the names of all remote targets.
func (m *Manager) Targets() []string {
	var res []string
	for k := range m.targets {
		res = append(res, k)
	}
	return res
}

// debugMsg should *only* be used by tests.
//
//lint:ignore U1000 This is used by tests.
func (m *Manager) debugMsg(d debugMsg, args ...any) {

    The ExecuteOp executes an operation on the specified device.

    Example:
      %res = tfrt_fallback_async.executeop key(0) cost(100) device("/CPU:0") "some.op"(%arg) : 1

    Note that the trailing number indicates the number of results.

    The func attrs and op attrs are placed in two separate regions.

    Example:
      %res = tfrt_fallback_async.executeop key(0) device("/CPU:0") "some.op"(%arg)

				&model.WatchedResource{ResourceNames: tt.watchedResources}, tt.request)
			gotExtensions := sets.String{}
			gotSecrets := sets.String{}
			for _, res := range resources {
				gotExtensions.Insert(res.Name)
				ec := &core.TypedExtensionConfig{}
				res.Resource.UnmarshalTo(ec)
				wasm := &wasm.Wasm{}
				ec.TypedConfig.UnmarshalTo(wasm)

	if err != nil {
		t.Fatal(err)
	}
	res, err := downstream.Recv()
	if err != nil {
		t.Fatal(err)
	}
	if res == nil || res.TypeUrl != v3.ClusterType {
		t.Fatalf("Expected to get cluster response but got %v", res)
	}
	err = downstream.Send(&discovery.DiscoveryRequest{TypeUrl: v3.ListenerType, Node: node})
	if err != nil {
		t.Fatal(err)
	}
	res, err = downstream.Recv()
	if err != nil {
		t.Fatal(err)

	storeArgs(args, argVals, argRefs)
	res, ok := valueInvoke(v.ref, argRefs)
	runtime.KeepAlive(v)
	runtime.KeepAlive(argVals)
	if !ok {
		if vType := v.Type(); vType != TypeFunction { // check here to avoid overhead in success case
			panic(&ValueError{"Value.Invoke", vType})
		}
		panic(Error{makeValue(res)})
	}
	return makeValue(res)
}

        <constraint name="__context__" within="" contains="" />

  %biasadd = "tf.BiasAdd"(%conv, %dq_bias) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>
  %res = "tf.Relu6"(%biasadd) : (tensor<*xf32>) -> tensor<*xf32>
  %q_res = "quantfork.qcast"(%res) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 0.023529411764705882:-128>>

// MIN-MAX-CHECK-NEXT:  [[res:%.*]], {{.*}}, {{.*}}, {{.*}} = "tf.CustomAggregator"([[add]]) <{calibration_method = 1 : i32, id = "composite_conv2d_with_relu6_fn_calibration_method_1", max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32, num_bins = 0 : i32}> : (tensor<*xf32>) -> (tensor<*xf32>, tensor<f32>, tensor<f32>, tensor<0xi64>)
// MIN-MAX-CHECK-NEXT:  return [[res]] : tensor<*xf32>