async def context_a(state: dict = Depends(get_state)):
    state["context_a"] = "started a"
    try:
        yield state
    finally:
        state["context_a"] = "finished a"


async def context_b(state: dict = Depends(context_a)):
    state["context_b"] = "started b"
    try:
        yield state
    finally:
        state["context_b"] = f"finished b with a: {state['context_a']}"

          ]
        }
      }
    }
  },
  "mappings": {
    "_source": {
      "excludes": [
        "content_ar",
        "content_bg",
        "content_bn",
        "content_ca",
        "content_ckb-iq",
        "content_cs",
        "content_da",
        "content_de",
        "content_el",
        "content_en",
        "content_en-ie",
        "content_es",

        op_outputs_ = std::vector<TensorHandlePtr>();
        Execute(context_, operation_name_, std::move(op_inputs_), attributes_,
                expected_max_outputs_, &op_outputs_, status_.get());
        execution_state_ = ExecutionState::kHasResult;
      }
    }
    finished_execute_.notify_one();
  }
}

void DeviceThread::StartExecute(TFE_Context* context,
                                const char* operation_name,

}

namespace tensorflow {
namespace {
class CustomDeviceAPI : public tensorflow::CustomDevice {
 public:
  CustomDeviceAPI(TFE_Context* context, TFE_CustomDevice device, void* info,
                  string name)
      : context_(context), device_(device), info_(info), name_(name) {}

  ~CustomDeviceAPI() override { device_.delete_device(info_); }

  const string& name() override { return name_; }

## Gerenciadores de contexto

### O que são gerenciadores de contexto

"Gerenciadores de Contexto" são qualquer um dos objetos Python que podem ser utilizados com a declaração `with`.

  name: {cluster}
contexts:
- context:
    cluster: {cluster}
    user: {cluster}
  name: {cluster}
current-context: {cluster}
kind: Config
preferences: {}
users:
- name: {cluster}
  user:
    token: token
`, "{cluster}", fakeClusterName)

	cases := []struct {
		name               string
		clusterName        string
		context            string
		server             string

                                                     bool use_tfrt);

// Returns the context_id from the EagerContext which is used by the
// EagerService to maintain consistency between client and worker. The
// context_id is initialized with a dummy value and is later set when the worker
// is initialized (either locally or remotely). The context_id can change during
// the process lifetime although this should cause the worker to be

		"analyze a specific revision deployed.")
	analysisCmd.PersistentFlags().StringArrayVar(&remoteContexts, "remote-contexts", []string{},
		`Kubernetes configuration contexts for remote clusters to be used in multi-cluster analysis. Not to be confused with '--context'. `+
			"If unspecified, contexts are read from the remote secrets in the cluster.")
	return analysisCmd
}

	return val
}

func setCaller(ctx context.Context, cl *RemoteClient) context.Context {
	//nolint:staticcheck // SA1029 Staticcheck is drunk.
	return context.WithValue(ctx, ctxCallerKey{}, cl)
}

func setSubroute(ctx context.Context, s string) context.Context {
	//nolint:staticcheck // SA1029 Staticcheck is drunk.
	return context.WithValue(ctx, ctxSubrouteKey{}, s)
}

func remoteSecretNameFromClusterName(clusterName string) string {
	return remoteSecretPrefix + clusterName
}

// NewCreateRemoteSecretCommand creates a new command for joining two contexts
// together in a multi-cluster mesh.
func NewCreateRemoteSecretCommand(ctx cli.Context) *cobra.Command {
	opts := RemoteSecretOptions{
		AuthType:         RemoteSecretAuthTypeBearerToken,
		AuthPluginConfig: make(map[string]string),