%gen0 = "tf.GeneratorDataset"(%str, %arg0, %arg1, %arg2, %arg3) {
        finalize_func = @__finalize_func_790,
        init_func = @__init_func_530, next_func = @__next_func_680,
        next_func.experimental_ints_on_device = true,
        operandSegmentSizes = array<i32: 2, 2, 1>,
        output_shapes = [#tf_type.shape<?>],
        output_types = [f32],
        metadata = ""
      } :

          %outputs_5, %control_6 = tf_executor.island(%control_4) wraps "tf._XlaHostComputeMlir"() {host_mlir_module = "module {\0A func.func @host_func() -> tensor<1x2xf32> {\0A %0 = \22tf.Const\22() {value = dense<0.1> : tensor<1x2xf32>} : () -> tensor<1x2xf32> \0A return %0 : tensor<1x2xf32>}}", manual_sharding = true, recv_key = "host_compute_channel_1_retvals", send_key = "host_compute_channel_1_args"}...

  %0 = "tf.GeneratorDataset"(%arg0, %arg1, %arg2, %arg3) {
      device = "/job:tpu_host_worker/replica:0/task:0/device:CPU:0",
      finalize_func = @finalize,
      init_func = @init,
      next_func = @next,
      operandSegmentSizes = array<i32: 1, 2, 1>,
      output_shapes = [#tf_type.shape<>],
      output_types = [!tf_type.string],
      metadata = ""} : (
              tensor<4xf32>,

	{"variable declared by template", `{{template "nested" $x:=.SI}},{{index $x 1}}`, "[3 4 5],4", tVal, true},

	// User-defined function: test argument evaluator.
	{"testFunc literal", `{{oneArg "joe"}}`, "oneArg=joe", tVal, true},
	{"testFunc .", `{{oneArg .}}`, "oneArg=joe", "joe", true},
}

// These strings are also in testdata/*.
const multiText1 = `
	{{define "x"}}TEXT{{end}}
	{{define "dotV"}}{{.V}}{{end}}

				featuregatetesting.SetFeatureGateDuringTest(t, utilfeature.DefaultFeatureGate, fg, f)
				t.Run(fmt.Sprintf("%v (%t)", fg, f), tf)
			}()
		}
	}
}

func TestGetTargetNodeName(t *testing.T) {
	testFun := func(t *testing.T) {
		tests := []struct {
			pod         *v1.Pod
			nodeName    string
			expectedErr bool
		}{
			{
				pod: &v1.Pod{
					ObjectMeta: metav1.ObjectMeta{
						Name:      "pod1",

      %gen0 = "tf.GeneratorDataset"(%str, %arg0, %arg1, %arg2, %arg3) {
        finalize_func = @__finalize_func_790,
        init_func = @__init_func_530, next_func = @__next_func_680,
        next_func.experimental_ints_on_device = true,
        operandSegmentSizes = array<i32: 2, 2, 1>,
        output_shapes = [#tf_type.shape<?>],
        output_types = [f32],
        metadata = ""
      } :

    for (Type ty : op.getResultTypes()) {
      shape_attrs.push_back(TF::ShapeAttr::get(rewriter.getContext(),
                                               mlir::cast<ShapedType>(ty)));
    }

    // Clone the `host_func` in the `host_mlir_module` attribute if it exists
    // and use it for `shape_inference_graph` attribute on XlaHostCompute.
    func::FuncOp cloned_func;
    SymbolTable manager(op->getParentOfType<ModuleOp>());

//
// VR and FPR disjointly overlap VSR, interpreting as VSR registers should produce the correctly overlapped VSR.
// REG_FPx & 63 == x
// REG_Vx & 63 == x + 32
func TestRegValueAlignment(t *testing.T) {
	tstFunc := func(rstart, rend, msk, rout int) {
		for i := rstart; i <= rend; i++ {
			if i&msk != rout {
				t.Errorf("%v is not aligned to 0x%X (expected %d, got %d)\n", rconv(i), msk, rout, rstart&msk)
			}
			rout++
		}

		i := int(atomic.AddInt32(&n, 1)-1) % len(procKeys)
		keys := procKeys[i]

		for pb.Next() {
			for _, k := range keys {
				m.Add(k, 1)
			}
		}
	})
}

func TestFunc(t *testing.T) {
	RemoveAll()
	var x any = []string{"a", "b"}
	f := Func(func() any { return x })
	if s, exp := f.String(), `["a","b"]`; s != exp {
		t.Errorf(`f.String() = %q, want %q`, s, exp)
	}

		if err != nil {
			t.Errorf("toNorm(%s) failed: %v (wd=%s)\n", arg, err, wd)
		} else if got != want {
			t.Errorf("toNorm(%s) returns %s, but %s expected (wd=%s)\n", arg, got, want, wd)
		}
	}
}

func TestUNC(t *testing.T) {
	// Test that this doesn't go into an infinite recursion.
	// See golang.org/issue/15879.
	defer debug.SetMaxStack(debug.SetMaxStack(1e6))
	filepath.Glob(`\\?\c:\*`)
}