TF_Tensor* input = nullptr;
    TF_Status* s = TF_NewStatus();
    TF_GetInput(ctx, 0, &input, s);
    EXPECT_EQ(TF_OK, TF_GetCode(s)) << "Failed to get input: " << TF_Message(s);
    EXPECT_EQ(123, *static_cast<tensorflow::uint8*>(TF_TensorData(input)));
    TF_GetInput(ctx, -1, &input, s);
    EXPECT_EQ(TF_OUT_OF_RANGE, TF_GetCode(s));
    TF_GetInput(ctx, 3, &input, s);

        connection.queueIncoming(input1)
        connection.queueIncoming(input2)
        connection.queueIncoming(closeInput)
        received.await()
        daemonConnection.stop()

        then:
        1 * handler.onInput(input1)
        1 * handler.onInput(input2)
        1 * handler.onEndOfInput() >> { received.countDown() }
        0 * handler._
    }

        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()

            test {
                inputs1 = files("input1.txt")
                inputs2 = files("input2.txt", "input3.txt")
            }
        """

        when:
        succeeds "test", "--info"

        then:
        executedAndNotSkipped ':test'
        outputContains "Input property 'inputs1' file ${file("input2.txt")} has been removed."

    Arg<TensorOf<[TF_Int16, TF_Int32, TF_Int64]>, [{int16, int32 or int64, must be in the range `[-rank(input), rank(input))`.
Describes which dimension of the input Tensor to reduce across. For vectors,
use dimension = 0.}]>:$dimension
  );

  let results = (outs

    key: "predict0"
    value: {
      inputs: {
        key: "inputs"
        value: {
          name: "input0"
          dtype: DT_STRING
        }
      }
      outputs: {
        key: "outputs"
        value: {
          name: "batch_func:0"
        }
      }
    }
  }
  signature_def: {
    key: "predict1"
    value: {
      inputs: {
        key: "tf_example_input"

    }

    def "out-of-date when input file changes"() {
        given:
        def inputFile = file("input.txt")
        inputFile.text = "foo"
        buildFile << """
            ${keyInfo.addAsPropertiesScript()}
            signing {
                ${signingConfiguration()}
            }
            task signCustomFile(type: Sign) {
                sign(file("input.txt"))
            }
        """

    }

    private static String customTaskCode(String input1, String input2) {
        """
            ${customTaskImpl()}
            task customTask(type: CustomTask){
                input1 = '$input1'
                input2 = '$input2'
            }
        """
    }

    private static String customTaskImpl() {
        """

// RUN: tf-opt -split-input-file -verify-diagnostics -tf-tpu-annotate-dynamic-shape-inputs %s | FileCheck %s

// Test that annotate the inputs of the cluster func to be dynamic shaped.

module attributes {tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:TPU_SYSTEM:0", "/job:worker/replica:0/task:0/device:TPU:0"]} {
   func.func @main(
      %arg0: tensor<2048xi64> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"},

                inputDirectory = file("input")
                outputDirectory = file("build/output")
            }
        """
        def toBeModifiedPath = "in/some/subdir/input1.txt"
        def toBeRemovedPath = "in/some/subdir/input2.txt"
        def toBeAddedPath = "in/some/other/subdir/other-input.txt"
        file("input/$toBeModifiedPath").text = "input to copy"
        file("input/${toBeRemovedPath}").text = "input to copy"