for _, v := range vectors {
			b.Run(v.label, func(b *testing.B) {
				b.ReportAllocs()
				for i := 0; i < b.N; i++ {
					// Writing to io.Discard because we want to
					// test purely the writer code and not bring in disk performance into this.
					tw := NewWriter(io.Discard)
					for _, file := range v.files {
						if err := tw.WriteHeader(file.hdr); err != nil {
							b.Errorf("unexpected WriteHeader error: %v", err)
						}

	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
```

    ```

!!! info
    FastAPI added support for `Annotated` (and started recommending it) in version 0.95.0.

    If you have an older version, you would get errors when trying to use `Annotated`.

    Make sure you [Upgrade the FastAPI version](../deployment/versions.md#upgrading-the-fastapi-versions){.internal-link target=_blank} to at least 0.95.1 before using `Annotated`.

			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++

    return;
  } else if (context->GetContextId() ==
             tensorflow::EagerContext::kInvalidContextId) {
    status->status = tensorflow::errors::InvalidArgument(
        "Trying to update a context with invalid context id.");
  }
  status->status =
      tensorflow::unwrap(ctx)->GetDistributedManager()->SetOrUpdateServerDef(
          server_def, /*reset_context=*/false, keep_alive_secs,