output->DebugString(100));
}

REGISTER_OP("AllocateOutputOp0").Output("output1: float");

TEST_F(DeviceKernelOpTest, TestAllocateEmptyOutput) {
  auto my_compute_func = [](void* kernel, TF_OpKernelContext* ctx) {
    TF_Status* s = TF_NewStatus();
    // Allocate empty output
    int64_t dim = 0;
    TF_Tensor* output = TF_AllocateOutput(

        }

        and:
        outputs.stdOut.contains("BEFORE OUT")
        stripped(outputs.stdOut).contains(result1.output)
        stripped(outputs.stdOut).contains(result2.output)
        outputs.stdOut.contains("AFTER OUT")

        and:
        outputs.stdErr.contains("BEFORE ERR")
        stripped(outputs.stdOut).contains(result1.error)
        stripped(outputs.stdOut).contains(result2.error)

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

  func.func @conv_with_multiple_uses(%arg0: tensor<1x3x4x3xf32> {tf_saved_model.index_path = ["input"]}) -> (tensor<1x3x2x2xf32> {tf_saved_model.index_path = ["output"]}, tensor<1x3x2x1xf32> {tf_saved_model.index_path = ["output2"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input:0", outputs = "PartitionedCall:0, Sum:0"}, tf_saved_model.exported_names = ["serving_default"]} {

  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>

  return options;
}

Status ClientSession::Run(const std::vector<Output>& fetch_outputs,
                          std::vector<Tensor>* outputs) const {
  return Run(FeedType{}, fetch_outputs, {}, outputs);
}

Status ClientSession::Run(const FeedType& inputs,
                          const std::vector<Output>& fetch_outputs,
                          std::vector<Tensor>* outputs) const {
  return Run(inputs, fetch_outputs, {}, outputs);
}

            apply plugin: "base"
            task customTask {
                inputs.file "input.txt"
                outputs.file "build/output" withPropertyName "output"
                outputs.cacheIf { true }
                doLast {
                  mkdir('build/output')
                  file('build/output/output.txt').text = file('input.txt').text
                }
            }
        """

        then:

        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']
    }

        [_AsClassName(x) for x in ctx.attr.srcs],
    )
    ctx.actions.write(output = ctx.outputs.out, content = _OUTPUT % (
        classes,
        ctx.attr.outname,
    ))

_GenSuite = rule(
    attrs = {
        "srcs": attr.label_list(allow_files = True),
        "outname": attr.string(),
    },
    outputs = {"out": "%{name}.java"},
    implementation = _impl,
)

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