Output id1 = ops::Identity(root.WithOpName("id1"), sw.output_true);

  Output const0 = ops::Const(root.WithOpName("const0"), 1);
  Output const1 = ops::Const(root.WithOpName("const1"), 2);

  Output add = ops::Add(root.WithOpName("add"), const0, const1);

  root.graph()->AddControlEdge(id0.node(), const0.node());
  root.graph()->AddControlEdge(id1.node(), const1.node());

    }
    tf_executor.fetch
  }
  func.return
}

// CHECK:      tf_executor.island
// CHECK:      [[CONST_0:%.+]] = "tf.Const"
// CHECK-SAME: value = dense<1> : tensor<i64>
// CHECK:      tf_executor.yield [[CONST_0]]
// CHECK:      tf_executor.island
// CHECK:      [[CONST_1:%.+]] = "tf.Const"
// CHECK-SAME: value = dense<2> : tensor<i64>

		C           // 1
	)
	assert(A == 0 && B == 1 && C == 1)

	const (
		X = X + X
		Y
		Z = iota
	)
	assert(X == 4 && Y == 8 && Z == 1)
}

  func.return %8 : !tfr.tensor

// CHECK: %[[CONST_0:.*]] = "tf.Const"() <{value = dense<1> : tensor<i64>}> : () -> tensor<i64>
// CHECK: %[[BUILD_LIST_0:.*]] = "tfr.build_list"(%arg1, %arg0) : (!tfr.tensor, !tfr.tensor) -> !tfr.tensor_list
// CHECK: %[[CAST_0:.*]] = "tfr.cast"(%[[CONST_0]]) : (tensor<i64>) -> !tfr.tensor

  %1 = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>
  // CHECK-NEXT: %[[ADD:[a-z0-9]+]] = "tf.AddV2"(%[[CONST_0]], %[[CONST_0]])
  %2 = "tf.AddV2"(%1, %1) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  // CHECK-NEXT: %[[CONST_1:.*]] = "tf.Const"() <{value = dense<1.050000e+03> : tensor<f32>}>
  %3 = "tf.Const"() {value = dense<1.050000e+03> : tensor<f32>} : () -> tensor<f32>

// CHECK:   %[[CONST_0:.*]] = "tf.Const"() <{value = dense<2.000000e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK:   %[[MUL_1:.*]] = "tf.Mul"(%arg1, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
// CHECK:   %[[MUL_2:.*]] = "tf.Mul"(%[[MUL_1]], %[[CONST_0]]) : (tensor<1xf32>, tensor<f32>) -> tensor<1xf32>
// CHECK:   return %[[MUL_2]] : tensor<1xf32>
// CHECK: }

  // CHECK-NEXT: %[[CAST_ZERO:.*]] = "tf.Cast"(%[[ZERO_SCALAR]]) : (tensor<i32>) -> tensor<f32>
  // CHECK-NEXT: %[[CONST10:.*]] = "tf.Const"() <{value = dense<10> : tensor<1xi32>}> : () -> tensor<1xi32>
  // CHECK-NEXT: %[[BROADCAST:.*]] = "tf.BroadcastTo"(%[[CAST_ZERO]], %[[CONST10]]) : (tensor<f32>, tensor<1xi32>) -> tensor<10xf32>
  // CHECK-NEXT: %[[ZERO:.*]] = "tf.Const"() <{value = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>

  // CHECK-NEXT: %[[CAST_ZERO:.*]] = "tf.Cast"(%[[ZERO_SCALAR]]) : (tensor<i32>) -> tensor<f32>
  // CHECK-NEXT: %[[CONST10:.*]] = "tf.Const"() <{value = dense<10> : tensor<1xi32>}> : () -> tensor<1xi32>
  // CHECK-NEXT: %[[BROADCAST:.*]] = "tf.BroadcastTo"(%[[CAST_ZERO]], %[[CONST10]]) : (tensor<f32>, tensor<1xi32>) -> tensor<10xf32>
  // CHECK-NEXT: %[[BUFFER:.*]] = "tf.MlirLocalVarOp"() : () -> tensor<!tf_type.resource<tensor<10xf32>>>

	// Must match runtime/mwbbuf.go:wbMaxEntriesPerCall.
	// It must also match the number of instances of runtime.gcWriteBarrier{X}.
	const maxEntries = 8

	var sb, sp, wbaddr, const0 *Value
	var cgoCheckPtrWrite, cgoCheckMemmove *obj.LSym
	var wbZero, wbMove *obj.LSym
	var stores, after []*Value
	var sset, sset2 *sparseSet
	var storeNumber []int32

  %const_10000 = "tf.Const"() {value = dense<10000> : tensor<i32>} : () -> tensor<i32>
  %const_0 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
  %const_1 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %const_64_1 = "tf.Const"() {value = dense<[64, 1]> : tensor<2xi32>} : () -> tensor<2xi32>
  %1 = "tf.Range"(%const_0, %const_10000, %const_1) : (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<10000xi32>