"same location"        | null           | "file"     | "output.txt"               | "output.txt"
        "consuming ancestor"   | null           | "file"     | "build/dir/sub/output.txt" | "build/dir"
        "consuming descendant" | 'build/dir'    | "dir"      | "build/dir/sub/output.txt" | "build/dir/sub/output.txt"
    }

        createBuildScripts("""
            def customTask = tasks.register("myTask") {
                outputs.file("\${buildDir}/output.txt")
                outputs.file("\${buildDir}/output2.txt")
                doLast {
                    file("\${buildDir}/output.txt") << 'custom task'
                    file("\${buildDir}/output2.txt") << 'custom task'
                }

  const TF_TensorSpec* tensor_spec_out =
      TF_SignatureDefParamTensorSpec(param_out);
  const TF_Shape* shape_out = TF_TensorSpecShape(tensor_spec_out);

  // Output "output_0" is a scalar, float32 tensor
  EXPECT_EQ("output_0", std::string(TF_SignatureDefParamName(param_out)));
  EXPECT_EQ(TF_FLOAT, TF_TensorSpecDataType(tensor_spec_out));
  EXPECT_EQ(0, TF_ShapeDims(shape_out));

            file("build.gradle") << """
                task foo {
                    inputs.dir('src').ignoreEmptyDirectories(false).withPropertyName('src')
                    outputs.file('output.txt')
                    doLast {
                        file('output.txt') << 'do stuff'
                    }
                }
            """
            dir("src") {
                file("A.txt") << "fooA"

                            const std::vector<Output>& outputs,
                            const std::vector<Output>& inputs,
                            std::vector<Output>* grad_outputs) {
  std::vector<Output> grad_inputs;
  grad_inputs.reserve(outputs.size());
  for (const Output& output : outputs) {
    grad_inputs.emplace_back(ops::OnesLike(scope, output));
  }

        %0 = tf_executor.graph {
          %output_0, %control_0 = tf_executor.island wraps "tf.Const"() <{value = dense<1.0> : tensor<1x2xf32>}> : () -> tensor<1x2xf32>

  func.func @serving_default(%arg0: tensor<1024x1024xf32> {tf_saved_model.index_path = ["input_tensor"]}) -> (tensor<1024x3xf32> {tf_saved_model.index_path = ["output1"]}, tensor<1024x3xf32> {tf_saved_model.index_path = ["output2"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input_tensor:0", outputs = "PartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    %0 = stablehlo.constant dense<1.000000e+03> : tensor<1024x3xf32>

        then:
        outputs.stdOut.readLines() == ['message']
        outputs.stdErr == ''
    }

    def rendersErrorLogEventsToStdErr() {
        when:
        renderer.attachSystemOutAndErr()
        renderer.onOutput(event('message', LogLevel.ERROR))

        then:
        outputs.stdOut == ''
        outputs.stdErr.readLines() == ['message']
    }

For the aforementioned reasons, task output caching is automatically disabled for tasks whose output directories overlap with another task.

Build scans show tasks where caching was disabled due to overlapping outputs in the timeline:

[.screenshot]
image::build-cache/overlapping-outputs-timeline.png[]

== Reuse of outputs between different tasks

                void transform(TransformOutputs outputs) {
                    def input = inputArtifact.get().asFile
                    println "processing [\${input.name}]"
                    assert ${lenient} || input.file
                    def output = outputs.file(input.name + "." + parameters.targetColor.get())
                    if (input.file) {
                        output.text = input.text + "-" + parameters.targetColor.get()