return tensorflow::errors::InvalidArgument(
          "Unable to cast input to GraphTensor");
    }
    TF_AddInput(op_.get(), t->output_);
    return absl::OkStatus();
  }
  Status AddInputList(absl::Span<AbstractTensorHandle* const> inputs) override {
    std::vector<TF_Output> tf_outputs(inputs.size());
    for (int i = 0; i < inputs.size(); i++) {
      GraphTensor* t = dyn_cast<GraphTensor>(inputs[i]);
      if (!t) {

  const string& DeviceName() const override;

  Status SetDeviceName(const char* name) override;

  Status AddInput(AbstractTensorHandle* input) override;
  Status AddInputList(absl::Span<AbstractTensorHandle* const> inputs) override;
  Status Execute(absl::Span<AbstractTensorHandle*> retvals,
                 int* num_retvals) override;

  Status SetAttrString(const char* attr_name, const char* data,

		if err != nil {
			scope.err(err, w, req)
			return
		}
		span.AddEvent("Object stored in database")

		status := http.StatusOK
		if wasCreated {
			status = http.StatusCreated
		}

		span.AddEvent("About to write a response")
		defer span.AddEvent("Writing http response done")
		transformResponseObject(ctx, scope, req, w, status, outputMediaType, result)
	}
}

      <button id="close-dialog" type="button" class="rounded-md bg-white text-gray-400 hover:text-gray-500 focus:outline-none focus:ring-2 focus:ring-blue-500 focus:ring-offset-2">
        <span class="sr-only">Close</span>
        <svg class="h-6 w-6" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" aria-hidden="true">
          <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" />

  }
  return absl::OkStatus();
}

absl::StatusOr<std::vector<XlaCompiler::Argument>>
XlaComputationLaunchContext::BuildXlaCompilerArguments(
    absl::Span<int const> must_be_constant_idxs,
    absl::Span<const Tensor* const> inputs,
    absl::Span<VariableInfo const> variable_args, Device* device) {
  if (!must_be_constant_idxs.empty() &&
      !absl::c_is_sorted(must_be_constant_idxs)) {

	// Reset resets the state and allows a Transformer to be reused.
	Reset()
}

// SpanningTransformer extends the Transformer interface with a Span method
// that determines how much of the input already conforms to the Transformer.
type SpanningTransformer interface {
	Transformer

	// Span returns a position in src such that transforming src[:n] results in
	// identical output src[:n] for these bytes. It does not necessarily return

	ctx *testContext
	tc  context2.Context
	ts  traceapi.Span
}

// NewTest returns a new test wrapper for running a single test.
func NewTest(t *testing.T) Test {
	rtMu.Lock()
	defer rtMu.Unlock()

	if rt == nil {
		panic("call to scope without running the test framework")
	}

	ctx, span := tracing.Start(rt.suiteContext().traceContext, t.Name())

	runner := &testImpl{

            extractTestCaseTo("tr > td.skipped:eq(0)", testsSkipped)
        }

        List<String> getFailureMessages(String testmethod) {
            html.select("div.test:has(a[name='$testmethod']) > span > pre").collect { it.text() }
        }

        TestClassExecutionResult assertTestsExecuted(String... testNames) {
            return assertTestsExecuted(testNames.collect { testCase(it) } as TestCase[])
        }

		<td>
			<div class="stacked-bar-graph">
			{{$Goroutine := .}}
			{{range $.NonOverlappingStats}}
				{{$Time := index $Goroutine.NonOverlappingStats .}}
				{{if $Time}}
					<span {{barStyle . $Time $.MaxTotal}}>&nbsp;</span>
				{{end}}
			{{end}}
			</div>
		</td>
		{{$Goroutine := .}}
		{{range $.NonOverlappingStats}}
			{{$Time := index $Goroutine.NonOverlappingStats .}}
			<td> {{$Time.String}}</td>

absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>> ImportSavedModel(
    const std::string& input_filename, const int saved_model_version,
    const std::unordered_set<std::string>& tags,
    absl::Span<const std::string> extra_tf_opdefs,
    absl::Span<std::string> exported_names, const GraphImportConfig& specs,
    bool enable_variable_lifting, mlir::MLIRContext* context,
    std::unique_ptr<SavedModelBundle>* saved_model_bundle) {