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

        def clean = rootBuild.task('clean').dependsOn(cleanFoo).dependsOn(cleanBar)
        def generateFoo = foo.task('generateFoo').outputs('build/foo')
        def generateBar = bar.task('generateBar').outputs('build/bar')
        def generate = rootBuild.task('generate').dependsOn(generateBar).dependsOn(generateFoo)

        // conflicts with command line order

  }

  std::vector<Value> inputs;
  std::vector<Value> outputs;
  computeInputsOutputs(ops, &inputs, &outputs);

  std::vector<Type> input_types;
  std::vector<Type> output_types;

  input_types.reserve(inputs.size());
  for (Value v : inputs) {
    input_types.push_back(v.getType());
  }
  output_types.reserve(outputs.size());
  for (Value v : outputs) {
    output_types.push_back(v.getType());
  }

  unwrap(o)->expected_num_outputs = num_outputs;
  unwrap(o)->outputs.clear();
  unwrap(o)->outputs.resize(num_outputs);
}
int TF_OutputListNumOutputs(TF_OutputList* o) {
  return unwrap(o)->outputs.size();
}
TF_AbstractTensor* TF_OutputListGet(TF_OutputList* o, int i) {
  return wrap(unwrap(o)->outputs[i]);
}
void TF_OutputListPushBack(TF_OutputList* o, TF_AbstractTensor* tensor,

# UNKNOWN:           %[[add:.*]], %[[add_control:.*]] = tf_executor.island wraps "tf.Add"(%[[ARG_0]], %[[ARG_1]])
# UNKNOWN:           fetch %[[add]]

# DYNAMIC-LABEL: func @main
# DYNAMIC-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<?x1x?xi32>, %[[ARG_1:[a-z0-9]+]]: tensor<1x?x1xi32>) -> tensor<*xi32>
# DYNAMIC-SAME:  control_outputs = ""
# DYNAMIC-SAME:  inputs = "input0,input1"
# DYNAMIC-SAME:  outputs = "Add"

                            std::vector<Output>* grad_outputs);

// Same as above, but uses 'OnesLike' for all shapes in
// 'outputs' as grad_inputs.
Status AddSymbolicGradients(const Scope& scope,
                            const std::vector<Output>& outputs,
                            const std::vector<Output>& inputs,
                            std::vector<Output>* grad_outputs);

    /**
     * Returns true if this task has declared any outputs. Note that a task may be able to produce output files and
     * still have an empty set of output files.
     *
     * @return true if this task has declared any outputs, otherwise false.
     */
    boolean getHasOutput();

    /**
     * Returns the output files of this task.
     *

    // Add StopGradients according to 'stop_outputs'.
    auto out0 =
        stop_outputs[0] ? StopGradient(scope_, y.output[0]) : y.output[0];
    auto out1 =
        stop_outputs[1] ? StopGradient(scope_, y.output[1]) : y.output[1];
    auto out2 =
        stop_outputs[2] ? StopGradient(scope_, y.output[2]) : y.output[2];

    auto g0 = Const(scope_, {1, 2, 3, 4, 5, 6, 7, 8}, {2, 4});

  %0 = tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.VarHandleOp"() {container = "a", device = "/CPU:0", dtype = i64, shape = "tfshape$", shared_name = "x"} : () -> tensor<!tf_type.resource<tensor<i64>>> loc("x")

        output.contains("Appending output property name to build cache key: outputDirectory")
        output.contains("Appending output property name to build cache key: resultsFile")
        output.contains("Build cache key for MakeGreen")
    }

    def "honors @PathSensitive(NONE) on input artifact property for project artifact file when caching"() {
        createDirs("a", "b", "c")
        settingsFile << "include 'a', 'b', 'c'"