}
	}

	var inputs []string

	// Parse parameters
	switch {
	case *stdin:
		// Parse 'mc support inspect --json' output
		input := struct {
			File string `json:"file"`
			Key  string `json:"key"`
		}{}
		got, err := io.ReadAll(os.Stdin)
		if err != nil {
			fatalErr(err)
		}
		fatalErr(json.Unmarshal(got, &input))
		inputs = []string{input.File}
		*keyHex = input.Key

  const llvm::MemoryBuffer* input =
      source_mgr->getMemoryBuffer(source_mgr->getMainFileID());
  std::string error;
  auto loc =
      mlir::FileLineColLoc::get(context, input->getBufferIdentifier(), 0, 0);
  std::vector<std::string> inputs;
  std::vector<std::string> outputs;
  return tflite::FlatBufferToMlir(
      absl::string_view(input->getBufferStart(), input->getBufferSize()),

        def actualDir = file("actualDir").createDir()
        file("symlink1").createLink(actualDir)
        file("symlink2").createLink(actualDir)

        buildFile << """
            task myTask {
                def outputFile = file("build/output.txt")
                inputs.dir("symlink1")
                inputs.dir("symlink2")

Anis Eleuch <******@****.***> 1712232280 +0100

    def "fails build when packing archive fails"() {
        when:
        file("input.txt") << "data"

        // Just a way to induce a packing error, i.e. corrupt/partial archive
        buildFile << """
            apply plugin: "base"
            task customTask {
                inputs.file "input.txt"
                outputs.file "build/output" withPropertyName "output"
                outputs.cacheIf { true }

    def "ad hoc tasks with the same action share results"() {
        file("input.txt").text = "data"
        buildFile << """
            def input = file("input.txt")
            def action = {
                println "Same action"
            }

            task taskA {
                def output = file("build/task-a/output.txt")
                inputs.file input
                outputs.file output
                outputs.cacheIf { true }

  }

  func.func @serving_default(%arg0: tensor<i64> {tf_saved_model.index_path = ["x"]}) -> (tensor<*x!tf_type.string> {tf_saved_model.index_path = ["r"]})
  attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "hash_table_Lookup/LookupTableFindV2:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    %cst = arith.constant dense<"f"> : tensor<!tf_type.string>

# 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

	for _, a := range c.analyzers {
		for _, inputKind := range a.Metadata().Inputs {
			if kinds.Contains(inputKind) {
				selected = append(selected, a)
				break
			}
		}
	}
	return Combine("subset", selected...)
}

// Metadata implements Analyzer
func (c *InternalCombinedAnalyzer) Metadata() Metadata {
	return Metadata{
		Name:   c.name,
		Inputs: combineInputs(c.analyzers),
	}
}

// Analyze implements Analyzer

  // Retrieve input names from model flags.
  std::vector<std::string> input_names;
  for (const auto& input : model_flags.input_arrays()) {
    input_names.push_back(input.name());
  }

  const auto& inputs = absl::StrJoin(input_names, ",");
  mlir::OpBuilder builder(*module);
  llvm::SmallVector<mlir::NamedAttribute> attrs;
  attrs.push_back(