}

        then:
        1 * deleter.deleteRecursively(temporaryWorkspace)

        then:
        immutableWorkspace.file("output.txt").text == "output"
        0 * _
    }

    def "keeps failed outputs in temporary workspace"() {
        def delegateFailure = Mock(Exception)
        def delegateDuration = Duration.ofSeconds(1)
        def delegateResult = Stub(CachingResult) {

	"top":      {report.Text, nil, nil, false, "Outputs top entries in text form", reportHelp("top", true, true)},
	"traces":   {report.Traces, nil, nil, false, "Outputs all profile samples in text form", ""},
	"tree":     {report.Tree, nil, nil, false, "Outputs a text rendering of call graph", reportHelp("tree", true, true)},

	// Save binary formats to a file

          e->dst_input());
    }
  }

  auto output = std::make_unique<Graph>((*graph)->op_registry());
  TF_RETURN_WITH_CONTEXT_IF_ERROR(
      EncapsulateSubgraphsInFunctions(
          kXlaClusterIdAttr, **graph, RewriteSubgraph,
          /*reuse_existing_functions=*/true, &output, flib_def),
      "EncapsulateXlaComputationsPass failed");
  graph->swap(output);
  return absl::OkStatus();
}

  params_->body_outputs[1] = params_->body_inputs[1];

  // Finalize while loop
  ExpectOK();

  // Validate while loop outputs returned by TF_FinishWhile()
  EXPECT_TRUE(outputs_[0].oper != nullptr);
  EXPECT_GE(outputs_[0].index, 0);
  EXPECT_TRUE(outputs_[1].oper != nullptr);
  EXPECT_GE(outputs_[1].index, 0);

  // Check that cond and body inputs are not present
  for (int i = 0; i < params_->ninputs; ++i) {

    if (!output_tensor_type) {
      return switchn.emitOpError()
             << "expects outputs to have tensor type but got " << output_type;
    }

    // If the output type is a ref type, then the operand type should also be of
    // the same ref type. However, if the output type is a non-ref type T, then
    // the operand can be tensor of type T or T_REF.

// CHECK: "tf.LookupTableSizeV2"(%arg1)
// CHECK: "tf.LookupTableFindV2"(%arg1

                    @Override
                    public void transform(TransformOutputs outputs) {
                        File input = getInputArtifact().get().getAsFile();
                        File output = outputs.file(input.getName() + ".txt");

                        try {
                            Files.write(output.toPath(), Collections.singleton(String.valueOf(input.length())));

		gpcas       = regInfo{inputs: []regMask{gp | sp | sb, gp, gp}, outputs: []regMask{gp}}
		fp01        = regInfo{inputs: nil, outputs: []regMask{fp}}
		fp11        = regInfo{inputs: []regMask{fp}, outputs: []regMask{fp}}
		fpgp        = regInfo{inputs: []regMask{fp}, outputs: []regMask{gp}}
		gpfp        = regInfo{inputs: []regMask{gp}, outputs: []regMask{fp}}

  for (const Value original_output_value : outputs) {
    // Use the mapped values in the newly created function that correspond to
    // outputs in the original function.
    result_values.push_back(mapper.lookup(original_output_value));
  }
  builder.create<func::ReturnOp>(module_op.getLoc(), result_values);

  // 2) Create XlaCallModuleOp (with ops mapped).
  CreateXlaCallModuleOp(inputs, outputs, result_types, reverse_subgraph,

                void transform(TransformOutputs outputs) {
                    def input = inputArtifact.get().asFile
                    ${server.callFromBuild('size-transform')}
                    File output = outputs.file(input.name + parameters.suffix)
                    output.text = String.valueOf(input.length())
                }
            }