};

// Idenitfy tf_device.cluster_func input-output alias pairs.
// This is currently conservative, primarily handling the following base case:
// ```
// %value = tf.ReadVariableOp(%resource_var)
// %output:N = tf_device.cluster_func(..., /*input index = a*/ %value, ...)
// tf.AssignVariableOp(%resource_var, %output#b) // write output #b to resource
// ```
// where `%value` and `%output#b` have only one use. (a, b) would be added as

OutputTensor ToOutputTensor(const Output& output) {
  return OutputTensor(output.node(), output.index());
}

// Utility function for converting to internal C++ datatypes.
std::vector<OutputTensor> ToOutputTensors(const std::vector<Output>& outputs) {
  std::vector<OutputTensor> result(outputs.size());
  for (int i = 0; i < outputs.size(); ++i) {
    result[i] = ToOutputTensor(outputs[i]);
  }
  return result;
}

        then:
        repository.isGeneratedByGradle(file('build'))
        repository.isGeneratedByGradle(file('build/outputs'))
        repository.isGeneratedByGradle(file('build/outputs/directory'))
        repository.isGeneratedByGradle(file('build/outputs/directory/subdir'))
        repository.isGeneratedByGradle(file('build/file'))
        repository.isGeneratedByGradle(file('build/file/other'))

# RUN: tf_tfl_translate -unfold_batchmatmul=false -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=2,5,3:3,7 -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-output-arrays=MatMul -output-mlir %s -o - 2>&1 | FileCheck %s

node {
  name: "Placeholder"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        dim {
          size: 2

    @get:PathSensitive(PathSensitivity.RELATIVE)
    @get:InputArtifact
    abstract val input: Provider<FileSystemLocation>

    override
    fun transform(outputs: TransformOutputs) {
        val outputDir = outputs.dir("${input.get().asFile.name}.loc")
        println("Running transform on ${input.get().asFile.name}, incremental: ${inputChanges.isIncremental}")

      - opened
      - synchronize
jobs:
  changes:
    runs-on: ubuntu-latest
    # Required permissions
    permissions:
      pull-requests: read
    # Set job outputs to values from filter step
    outputs:
      docs: ${{ steps.filter.outputs.docs }}
    steps:
    - uses: actions/checkout@v4
    # For pull requests it's not necessary to checkout the code but for master it is
    - uses: dorny/paths-filter@v3

    def "snapshots outputs"() {
        def outputSnapshot = Mock(FileSystemSnapshot)

        when:
        def result = outputSnapshotter.snapshotOutputs(work, workspace)

        then:
        1 * work.visitOutputs(workspace, _ as UnitOfWork.OutputVisitor) >> { File workspace, UnitOfWork.OutputVisitor outputVisitor ->

                void transform(TransformOutputs outputs) {
                    def input = inputArtifact.get().asFile
                    println "processing [\${input.name}]"
                    assert input.file
                    for (def i : 1..parameters.multiplier.get()) {
                        def output = outputs.file(input.name + "." + parameters.targetColor.get() + "-" + i)

    @Override
    public void transform(TransformOutputs outputs) {
        // We simulate fan-in behaviour:
        // We expect that a transform before this one outputs three artifacts: 1. analysis metadata, 2. the original file and 3. instrumentation marker file.
        // So if the input is analysis metadata we use it and create instrumented artifact, otherwise it's original artifact and we output that.
        File input = getInput().get().getAsFile();

            createNewFile(outputs.file(AGENT_INSTRUMENTATION_MARKER.getFileName()));
            doTransform(artifactToTransform, outputs, originalName -> INSTRUMENTED_ENTRY_PREFIX + originalName);
        } else {
            createNewFile(outputs.file(LEGACY_INSTRUMENTATION_MARKER.getFileName()));
            doTransform(artifactToTransform, outputs, originalName -> originalName);
        }
    }