TFE_OpSetDevice(matmul, cpu_device_name.c_str(), status);
    EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
    auto remote_arg =
        tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h1_task2));
    // The input handles should never change since they have been mirrored.
    ASSERT_FALSE(remote_arg->HasLocalMirror(nullptr));
  }

  TFE_TensorHandle* retvals[1];
  int num_retvals = 1;

 * @author mbechler
 *
 */
public interface Credentials {

    /**
     * 
     * @param type
     * @return instance for type, null if the type cannot be unwrapped
     */
    <T extends Credentials> T unwrap ( Class<T> type );


    /**
     * @return the domain the user account is in
     */
    String getUserDomain ();


    /**
     * @return whether these are anonymous credentials
     */

  TFE_TensorHandle* var_handle0 = TestVariable(ctx, 1.0, dev0_name);
  EXPECT_NE(var_handle0, nullptr);

  absl::Status status2;
  EXPECT_EQ(tensorflow::unwrap(var_handle0)->DeviceName(&status2), dev0_name);

  // Rename local device
  // This server def has the task index set to 0.
  string serialized = server_def.SerializeAsString();

      } else {
        TF_SetStatus(
            status, TF_INVALID_ARGUMENT,
            absl::StrCat(
                "Got a non-parallel tensor ",
                tensorflow::unwrap(absl::get<TFE_TensorHandle*>(input))
                    ->DebugString(),
                " as input to a parallel operation. First pack non-parallel "
                "tensors for each device into a parallel tensor explicitly.")

        this.uncPath = this.pipe.getUncPath();
    }


    /**
     * {@inheritDoc}
     *
     * @see jcifs.SmbPipeHandle#unwrap(java.lang.Class)
     */
    @SuppressWarnings ( "unchecked" )
    @Override
    public <T extends SmbPipeHandle> T unwrap ( Class<T> type ) {
        if ( type.isAssignableFrom(this.getClass()) ) {
            return (T) this;
        }
        throw new ClassCastException();

extern "C" {

TF_CAPI_EXPORT extern TFE_TensorDebugInfo* TFE_TensorHandleTensorDebugInfo(
    TFE_TensorHandle* h, TF_Status* status) {
  tensorflow::TensorHandle* handle =
      TensorHandleFromInterface(tensorflow::unwrap(h));
  const tensorflow::Tensor* tensor;
  status->status = handle->Tensor(&tensor);
  if (!status->status.ok()) {
    return nullptr;
  }

    'email': '******@****.***',
    'full_name': None,
}
```

#### Unwrapping a `dict`

If we take a `dict` like `user_dict` and pass it to a function (or class) with `**user_dict`, Python will "unwrap" it. It will pass the keys and values of the `user_dict` directly as key-value arguments.

So, continuing with the `user_dict` from above, writing:

```Python
UserInDB(**user_dict)
```

  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return nullptr;
  }
  tensorflow::TensorHandle* handle =
      tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h));
  if (handle->Type() != TensorHandle::LOCAL) {
    status->status = tensorflow::errors::InvalidArgument(
        "DLPack doesn't support ", handle->TypeString(), " tensor");
    return nullptr;
  }

        try ( SmbTransportInternal trans = tf.getTransportPool()
                .getSmbTransport(tf, dc, 0, false, tf.hasDefaultCredentials() && tf.getConfig().isIpcSigningEnforced())
                .unwrap(SmbTransportInternal.class) ) {
            if ( !tf.hasDefaultCredentials() ) {
                trans.ensureConnected();
                log.warn(

		APICode: "kms:NotSupported",
		Err:     "requested functionality is not supported",
	}
)

// Error is a KMS error that can be translated into an S3 API error.
//
// It does not implement the standard error Unwrap interface for
// better error log messages.
type Error struct {
	Code    int    // The HTTP status code returned to the client
	APICode string // The API error code identifying the error