Sehen wir uns an, was jede dieser Optionen bedeutet:

* `main:app`: Das ist die gleiche Syntax, die auch von Uvicorn verwendet wird. `main` bedeutet das Python-Modul mit dem Namen `main`, also eine Datei `main.py`. Und `app` ist der Name der Variable, welche die **FastAPI**-Anwendung ist.
    * Stellen Sie sich einfach vor, dass `main:app` einer Python-`import`-Anweisung wie der folgenden entspricht:

        ```Python
        from main import app
        ```

	if err != nil {
		writeErrorResponseJSON(ctx, w, toAPIError(ctx, err), r.URL)
		return
	}

	buckets := make([]string, 0, len(bucketInfos))
	for _, bInfo := range bucketInfos {
		buckets = append(buckets, bInfo.Name)
	}

	var id string
	if id, err = pools.initRebalanceMeta(ctx, buckets); err != nil {
		writeErrorResponseJSON(ctx, w, toAPIError(ctx, err), r.URL)
		return
	}

      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions

			t.Fatalf("Unable to create temporary file. %s", err)
		}
		if err = tmpfile.Close(); err != nil {
			t.Fatalf("Unable to create temporary file. %s", err)
		}
		return tmpfile.Name()
	}

	filename := createTempFile(
		`app="virtuous-rat-minio"
chart="minio-0.1.3"
heritage="Tiller"
pod-template-hash="818089471"`)

	defer os.Remove(filename)

	testCases := []struct {

    }

    @Test
    public void mergeSameMailingLists()
    {
        MailingList mailingList = new MailingList();
        mailingList.setName( "name" );

        Model target = new Model();
        target.setMailingLists( Arrays.asList( mailingList ) );

        Model source = new Model();
        source.setMailingLists( Arrays.asList( mailingList ) );

#endif
  } else {
    status->status = tensorflow::errors::InvalidArgument(
        "Unsupported Device Type for dlpack");
  }

  return ctx;
}

// Converts DLDevice to TF device name.
absl::optional<std::string> DeviceNameFromDlContext(const DLDevice& ctx,
                                                    TF_Status* status) {
  switch (ctx.device_type) {
    case DLDeviceType::kDLCPU:

             labels:
               kubernetes.io/cluster-service: "true"
             name: system:azure-cloud-provider
           roleRef:
             apiGroup: rbac.authorization.k8s.io
             kind: ClusterRole
             name: system:azure-cloud-provider
           subjects:
           - kind: ServiceAccount
             name: azure-cloud-provider
             namespace: kube-system
    ```

type Reader struct {
	args         *ReaderArgs
	readCloser   io.ReadCloser    // raw input
	buf          *bufio.Reader    // input to the splitter
	columnNames  []string         // names of columns
	nameIndexMap map[string]int64 // name to column index
	current      [][]string       // current block of results to be returned
	recordsRead  int              // number of records read in current slice
	input        chan *queueItem  // input for workers

		ctx = kms.Context{bucket: path.Join(bucket, object)}
	} else if _, ok := ctx[bucket]; !ok {
		ctx[bucket] = path.Join(bucket, object)
	}
	unsealKey, err := k.Decrypt(context.TODO(), &kms.DecryptRequest{
		Name:           keyID,
		Ciphertext:     kmsKey,
		AssociatedData: ctx,
	})
	if err != nil {
		return key, err
	}
	err = key.Unseal(unsealKey, sealedKey, s3.String(), bucket, object)
	return key, err
}

- Now users can get object info like:

  ```bash
    a. kubectl get pod test-pod -o custom-columns=CONTAINER:.spec.containers[0:3].name
    b. kubectl get pod test-pod -o custom-columns=CONTAINER:.spec.containers[-2:].name
  ```

([#73063](https://github.com/kubernetes/kubernetes/pull/73063), [@WanLinghao](https://github.com/WanLinghao))