== Incremental builds

An *incremental build* is a build that avoids running tasks whose inputs have not changed since the previous build.
Re-executing such tasks is unnecessary if they would only re-produce the same output.

For incremental builds to work, tasks must define their inputs and outputs.
Gradle will determine whether the input or outputs have changed at build time.
If they have changed, Gradle will execute the task.

        val gradleUserHomeDir: File,
        val jvm: String,
        val startParameterProperties: Map<String, Any?>,
        /**
         * Whether the undeclared inputs accessed while serializing the task graph will be
         * excluded from input tracking. This is a temporary opt-out flag after a change
         * was made in that behavior.
         */
        val ignoreInputsInConfigurationCacheTaskGraphWriting: Boolean,

	t.Cleanup(func() {
		t.Helper()
		types := slices.Map(mc.inputs, func(e any) string {
			return fmt.Sprintf("%T", e)
		})
		assert.Equal(t, len(mc.inputs), 0, fmt.Sprintf("some inputs were not consumed: %v (%v)", mc.inputs, types))
	})
	return mc
}

func GetMockCollection[T any](mc *MockCollection) krt.Collection[T] {
	return krt.NewStaticCollection(extractType[T](&mc.inputs))
}

	}

	for _, c := range cases {
		input := TimeHolder{c.input}
		result, err := json.Marshal(&input)
		if err != nil {
			t.Errorf("Failed to marshal input: '%v': %v", input, err)
		}
		if string(result) != c.result {
			t.Errorf("Failed to marshal input: '%v': expected %+v, got %q", input, c.result, string(result))
		}
	}
}

func TestTimeUnmarshalJSON(t *testing.T) {

                       Inputs(Out(NodeWith(Name("placeholder_a"))),
                              Out(NodeWith(Name("placeholder_b"))))));

  EXPECT_EQ(Explain(add.node(), NodeWith(Inputs())),
            "\nexpected 0 inputs but node has 2");
  EXPECT_EQ(
      Explain(add.node(), NodeWith(Inputs(Out(NodeWith(Name("blah"))), _))),
      "\ninput 0 does not match expected:\nname: blah, \nsource does not match "

  3: 13
Enter selection (default: 12) [1..3] """)
    }

    def "accepts valid input"() {
        def event = new SelectOptionPromptEvent(123, "question", ["1", "2", "3", "4"], 1)

        expect:
        def result = event.convert(input)
        result.response == expected
        result.newPrompt == null

        where:
        input | expected
        '1'   | 0
        '4'   | 3
        ' 1 ' | 0
    }

	// in the analyzed source code such as a potential mistake.
	// It may be called by the Run function.
	Report func(Diagnostic)

	// ResultOf provides the inputs to this analysis pass, which are
	// the corresponding results of its prerequisite analyzers.
	// The map keys are the elements of Analysis.Required,
	// and the type of each corresponding value is the required

            @Override
            public void onInput(ForwardInput input) {
                LOGGER.debug("Writing forwarded input on this process' stdin.");
                stdInStream.received(input.getBytes());
            }

            @Override
            public void onUserResponse(UserResponse input) {
                inputReader.putInput(new UserInputReader.TextResponse(input.getResponse()));
            }

            @Override

        Converts a TensorFlow SavedModel into StableHLO bytecode.

        * input-path: The path to the input TensorFlow SavedModel.
        * exported-model-signatures: Comma-separated list of exported model
          signatures to convert.
        * tag_names: Comma-separated list of tags for loading SavedModel.
        * input-arg-shapes: A string representation of input argument shapes for

        event.prompt == "question? [yes, no] " // trailing space
    }

    def "accepts valid input"() {
        def event = new YesNoQuestionPromptEvent(123, "question?")

        expect:
        def result = event.convert(input)
        result.response == expected
        result.newPrompt == null

        where:
        input   | expected
        "yes"   | true
        " yes " | true
        "no"    | false