```
.
├── app                  # "app" is a Python package
│   ├── __init__.py      # this file makes "app" a "Python package"
│   ├── main.py          # "main" module, e.g. import app.main
│   ├── dependencies.py  # "dependencies" module, e.g. import app.dependencies
│   └── routers          # "routers" is a "Python subpackage"
│   │   ├── __init__.py  # makes "routers" a "Python subpackage"

 * artifact publishing by reducing the artifacts and packaging reports that consist of multiple files.
 *
 * Reducing the number of reports also makes it easier to find the important ones when analysing a failed build in
 * Team City.
 */

val testFilesCleanup = extensions.create<TestFileCleanUpExtension>("testFilesCleanup").apply {
    reportOnly.convention(false)
}

		return nil
	}

	switch err.Error() {
	case errLockConflict.Error():
		return errLockConflict
	case errLockNotFound.Error():
		return errLockNotFound
	}
	return err
}

// Call makes a REST call to the remote endpoint using the msgp codec
func (restClient *ReconnectRESTClient) Call(method string, args LockArgs) (status bool, err error) {
	buf, err := args.MarshalMsg(nil)
	if err != nil {

	}.NewRemoteTargetHTTPTransport(insecure)
}

// ceilFrac takes a numerator and denominator representing a fraction
// and returns its ceiling. If denominator is 0, it returns 0 instead
// of crashing.
func ceilFrac(numerator, denominator int64) (ceil int64) {
	if denominator == 0 {
		// do nothing on invalid input
		return
	}
	// Make denominator positive
	if denominator < 0 {
		numerator = -numerator

			BitrotChecksumAlgo: HighwayHash,
			ErasureDist:        make([]uint8, len(fi.Erasure.Distribution)),
			PartNumbers:        make([]int, len(fi.Parts)),
			PartETags:          nil,
			PartSizes:          make([]int64, len(fi.Parts)),
			PartActualSizes:    make([]int64, len(fi.Parts)),
			MetaSys:            make(map[string][]byte),
			MetaUser:           make(map[string]string, len(fi.Metadata)),
		}

		Description:          m.Description,
		Value:                m.Value,
		HistogramBucketLabel: m.HistogramBucketLabel,
		StaticLabels:         make(map[string]string, len(m.StaticLabels)),
		VariableLabels:       make(map[string]string, len(m.VariableLabels)),
		Histogram:            make(map[string]uint64, len(m.Histogram)),
	}
	for k, v := range m.StaticLabels {
		metric.StaticLabels[k] = v
	}

func newBatchJobPool(ctx context.Context, o ObjectLayer, workers int) *BatchJobPool {
	jpool := &BatchJobPool{
		ctx:          ctx,
		objLayer:     o,
		jobCh:        make(chan *BatchJobRequest, 10000),
		workerKillCh: make(chan struct{}, workers),
		jobCancelers: make(map[string]context.CancelFunc),
	}
	jpool.ResizeWorkers(workers)

	randomWait := func() time.Duration {

eData":"2015-12-21T00:00:00","IssueTime":"1251","RPState":"CA","PlateExpiry":"200304","Make":"HOND","BodyStyle":"PA","Color":"GY","Location":"13147 WELBY WAY","Route":"01521","Agency":1,"ViolationCode":"4000A1","ViolationDescr":"NO EVIDENCE OF REG","Fine":50,"Latitude":99999,"Longitude":99999} {"Ticket":1103700150,"IssueData":"2015-12-21T00:00:00","IssueTime":"1435","RPState":"CA","PlateExpiry":"201512","Make":"GMC","BodyStyle":"VN","Color":"WH","Location":"525 S MAIN ST","Route":"1C51","Agency"...

//       .Attr("N: int >= 2")
//       .Attr("T: type");
// that defines two inputs, "concat_dim" and "values" (in that order).
// You must use TF_AddInput() for the first input (since it takes a
// single tensor), and TF_AddInputList() for the second input (since
// it takes a list, even if you were to pass a list with a single
// tensor), as in:
//   TF_OperationDescription* desc = TF_NewOperation(graph, "Concat", "c");

# installed into a virtual environment, and then that venv is used to run all
# bazel tests with a special flag "--define=no_tensorflow_py_deps=true", which
# drops all the bazel dependencies for each py_test; this makes all the tests
# use the wheel's TensorFlow installation instead of the one made available
# through bazel. This must be done in a different root directory, //bazel_pip/...,