"is_variable": true,
          "quantization": {
            "min": [-0.5],
            "max": [0.5]
          }
        }
      ],
      "inputs": [0],
      "outputs": [24],
      "operators": [
        {
          "inputs": [
            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
          ],
          "outputs": [24],
          "intermediates": [

// RUN: tf-tfrt-opt -tf-to-tfrt %s | FileCheck %s --dump-input=fail --dump-input-filter=all
// RUN: tf-tfrt-opt -pass-pipeline='builtin.module(tf-to-tfrt{target-tpurt=true tpu-use-core-selector=false})' %s | FileCheck %s --dump-input=fail --dump-input-filter=all

// CHECK-LABEL: func @_tfrt_fallback_init
// CHECK-SAME: {{.*}} !tfrt.chain
// CHECK: tfrt_fallback_async.createop(%arg0) key(0) device("/device:CPU:0") "tf.ParseExampleV2"()

        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)

        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()

// 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"]}

package hash_test

import (
	"bytes"
	"crypto/sha256"
	"encoding"
	"fmt"
	"log"
)

func Example_binaryMarshaler() {
	const (
		input1 = "The tunneling gopher digs downwards, "
		input2 = "unaware of what he will find."
	)

	first := sha256.New()
	first.Write([]byte(input1))

	marshaler, ok := first.(encoding.BinaryMarshaler)
	if !ok {
		log.Fatal("first does not implement encoding.BinaryMarshaler")
	}

    def "can read from stream and then restart to use another stream"() {
        def input1 = encoded("string 1")
        def input2 = encoded("string 2")

        given:
        def decoder = new KryoBackedDecoder(input1)
        decoder.readString()
        decoder.restart(input2)

        expect:
        decoder.readPosition == 0
        decoder.readString() == "string 2"

        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())
                );
            }
        };
    }

        then:
        ruleAction.inputTypes == []
        closureCalled == "it"

        when:
        ruleAction = ruleActionAdapter.createFromClosure(String, { String s, String input1, Integer input2 -> closureCalled = input1 + input2 })
        ruleAction.execute("", ["foo", 3])

        then:
        ruleAction.inputTypes == [String, Integer]
        closureCalled == "foo3"
    }

    def "can adapt from action" () {

// CHECK-NEXT:    name: "min_runtime_version",
// CHECK-NEXT:    buffer: 6
// CHECK-NEXT:  } ],
// CHECK-NEXT:  signature_defs: [ {
// CHECK-NEXT:    inputs: [ {
// CHECK-NEXT:      name: "input1",
// CHECK-NEXT:      tensor_index: 1
// CHECK-NEXT:    }, {
// CHECK-NEXT:      name: "input2"
// CHECK-NEXT:    } ],
// CHECK-NEXT:    outputs: [ {
// CHECK-NEXT:      name: "end_logits",
// CHECK-NEXT:      tensor_index: 4