ifneq ($(findstring google,$(HOSTNAME)),)
warning+=Googlers: go/installdocker\#the-version-of-docker-thats-installed-is-old-eg-1126
endif
# The old docker issue manifests as not being able to run *any* binary. So we can test
# by trying to run a trivial program and ensuring it actually ran. If not, emit our warning.
# Note: we cannot do anything like $(shell docker version) to check, since that would also fail.
CAN_RUN := $(shell echo "can I run echo")

	p.diskID.Store(&id)
}

func (p *xlStorageDiskIDCheck) checkDiskStale() error {
	if *p.diskID.Load() == emptyDiskID {
		// For empty disk-id we allow the call as the server might be
		// coming up and trying to read format.json or create format.json
		return nil
	}
	storedDiskID, err := p.storage.GetDiskID()
	if err != nil {
		// return any error generated while reading `format.json`
		return err
	}

    tensorflow::ImmediateExecutionTensorHandle* unwrapped_handle =
        tensorflow::unwrap(handles[i]);
    if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) {
      // One of the inputs we're trying to pack is on a custom device. We'll let
      // the first custom device we see handle all of the packing.
      auto* custom_device_handle =
          tensorflow::down_cast<tensorflow::CustomDeviceTensorHandle*>(

It would take the same amount of time to finish with or without turns (concurrency) and you would have done the same amount of work.

But in this case, if you could bring the 8 ex-cashier/cooks/now-cleaners, and each one of them (plus you) could take a zone of the house to clean it, you could do all the work in **parallel**, with the extra help, and finish much sooner.

      "certChain": []
    },
    {
      "identity": "spiffe://cluster.local/ns/istio-system/sa/istiod",
      "state": "Unavailable: signing gRPC error (The service is currently unavailable): error trying to connect: TLS handshake failed: cert verification failed - unable to get local issuer certificate [CERTIFICATE_VERIFY_FAILED]",
      "certChain": []
    }
  ]

So, if your application consumes a lot of memory (for example with machine learning models), and your server has a lot of CPU cores **but little memory**, then your container could end up trying to use more memory than what is available, and degrading performance a lot (or even crashing). 🚨

### Create a `Dockerfile`

Here's how you would create a `Dockerfile` based on this image:

```Dockerfile

Pydantic's `orm_mode` will tell the Pydantic *model* to read the data even if it is not a `dict`, but an ORM model (or any other arbitrary object with attributes).

This way, instead of only trying to get the `id` value from a `dict`, as in:

```Python
id = data["id"]
```

it will also try to get it from an attribute, as in:

```Python
id = data.id
```

			attempts := 1
			for attempts <= 3 {
				aerr := api.AbortMultipartUpload(ctx, tgtBucket, tgtObject, res.UploadID, ObjectOptions{})
				if aerr == nil {
					return
				}
				batchLogIf(ctx,
					fmt.Errorf("trying %s: Unable to cleanup failed multipart replication %s on remote %s/%s: %w - this may consume space on remote cluster",
						humanize.Ordinal(attempts), res.UploadID, tgtBucket, tgtObject, aerr))
				attempts++

		// This may be because of 2 reasons:
		//
		// 1) The folder/object was deleted.
		// 2) We come from another disk and this disk missed the write.
		//
		// We therefore perform a heal check.
		// If that doesn't bring it back we remove the folder and assume it was deleted.
		// This means that the next run will not look for it.
		// How to resolve results.
		resolver := metadataResolutionParams{
			dirQuorum: f.disksQuorum,

- Fixed kubelet startup getting stuck with `NewVolumeManagerReconstruction`...