// swap in and out to exercise copy-up and copy-down
	input8, output8 = output8, input8
	input16, output16 = output16, input16
	input32, output32 = output32, input32
	input64, output64 = output64, input64
	in := 0
	out := 13
	for i := range input8 {
		input8[i] = u8(in)
		output8[i] = u8(out)
		outputS[i] = u8(out)
		input16[i] = u16(in)
		output16[i] = u16(out)
		input32[i] = u32(in)
		output32[i] = u32(out)

        void theRule(List subject, String input1, Integer input2, Set input3) {
            subject.add(input1)
            subject.add(input2)
            subject.addAll(input3)
        }
    }

    static class ArrayListRuleSource {
        @Mutate
        void theRule(ArrayList subject, String input1, Integer input2, Set input3) {
            subject.add(input1)
            subject.add(input2)
            subject.addAll(input3)

// CHECK: func @add(
// CHECK-SAME: {tf_saved_model.index_path = ["input1"]}
// CHECK-SAME: {tf_saved_model.index_path = ["input2"]}
// CHECK-SAME: {tf_saved_model.index_path = ["result"]}
// CHECK-SAME: tf.entry_function = {inputs = "input1:0,input2:0", outputs = "result:0"}
// CHECK-SAME: tf_saved_model.exported_names = ["add"]

// CHECK: func @sub(
// CHECK-SAME: {tf_saved_model.index_path = ["input2"]}
// CHECK-SAME: {tf_saved_model.index_path = ["input1"]}

node {
  name: "Add"
  op: "Add"
  input: "input0"
  input: "input1"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
}
node {
  name: "input0"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-input-arrays=input0,input1 -tf-input-data-types=DT_INT32,DT_INT32 -tf-input-shapes=10:10 -tf-output-arrays=Sub -o - | FileCheck %s

node {
  name: "Add"
  op: "Add"
  input: "input0"
  input: "input1"
  # If device type or id doesn't exist, assign a default one (device:CPU:0).
  device: "/job:localhost/replica:0/task:0"
  attr {
    key: "T"
    value {

        0 * calculated._
    }

    def "notifies each of the inputs of task start and complete"() {
        def input1 = Mock(LifecycleAwareValue)
        def input2 = "other"
        def input3 = Mock(LifecycleAwareValue)
        def fileCollection = new CalculatedTaskInputFileCollection(taskDependencyFactory, ":task", Stub(MinimalFileSet), input1, input2, input3)

        when:
        fileCollection.prepareValue()

// RUN: tac-translate -input-mlir -output-mlir -device-specs=GPU %s -o - 2>&1 | FileCheck %s

module {
func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<2x1xf32> attributes {tf.entry_function = {inputs = "input0,input1,input2,input3", outputs = "output"}} {
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-input-arrays=input -tf-input-shapes='' -tf-output-arrays=input -tf-convert-legacy-fed-inputs -o - | FileCheck --check-prefix=NODATATYPE %s

        return new AbstractModelAction<T>(reference, descriptor, input1, input2) {
            @Override
            public void execute(MutableModelNode modelNode, List<ModelView<?>> inputs) {
                action.execute(modelNode,
                    Cast.<I>uncheckedCast(inputs.get(0).getInstance()),
                    Cast.<J>uncheckedCast(inputs.get(1).getInstance())
                );
            }
        };
    }

  // CHECK: %[[INPUT0:.*]] = "tf.Placeholder.input"
  %0 = "tf.Placeholder.input"(%arg0) : (tensor<f32>) -> tensor<f32>
  // CHECK: %[[INPUT1:.*]] = "tf.Placeholder.input"
  %1 = "tf.Placeholder.input"(%arg1) : (tensor<f32>) -> tensor<f32>
  %2 = arith.constant dense<true> : tensor<i1>

  // CHECK: "tf.Multiply"(%[[INPUT1]], %[[INPUT0]])