// Load input model
  auto input_fbm = tflite::FlatBufferModel::BuildFromFile(
      "tensorflow/lite/testdata/sparse_tensor.bin");
  tflite::ModelT input_model;
  input_fbm->GetModel()->UnPackTo(&input_model);

  // Populate input metadata
  auto model_metadata_buffer = std::make_unique<tflite::BufferT>();
  model_metadata_buffer->data =
      std::vector<uint8_t>(expected_value.begin(), expected_value.end());

\n\nSo what happens? Decode first uses json or yaml to unmarshal the serialized data into your external MyAPIObject. That causes the raw JSON to be stored, but not unpacked. The next step is to copy (using pkg/conversion) into the internal struct. The runtime package's DefaultScheme has conversion functions installed which will unpack the JSON stored in RawExtension, turning it into the correct object type, and storing it in the Object. (TODO: In the case where the object is of an unknown type, a...

\n\nSo what happens? Decode first uses json or yaml to unmarshal the serialized data into your external MyAPIObject. That causes the raw JSON to be stored, but not unpacked. The next step is to copy (using pkg/conversion) into the internal struct. The runtime package's DefaultScheme has conversion functions installed which will unpack the JSON stored in RawExtension, turning it into the correct object type, and storing it in the Object. (TODO: In the case where the object is of an unknown type, a...

\n\nSo what happens? Decode first uses json or yaml to unmarshal the serialized data into your external MyAPIObject. That causes the raw JSON to be stored, but not unpacked. The next step is to copy (using pkg/conversion) into the internal struct. The runtime package's DefaultScheme has conversion functions installed which will unpack the JSON stored in RawExtension, turning it into the correct object type, and storing it in the Object. (TODO: In the case where the object is of an unknown type, a...

  }];
}

def TFL_UnpackOp : TFL_Op<"unpack", [
    Pure,
    QuantizableResult,
    SameOperandsAndResultElementType,
    SameOperandsAndResultsScale,
    DeclareOpInterfaceMethods<InferTypeOpInterface>]> {
  let summary = "Unpacks a tensor along a dimension into multiple tensors";

  let description = [{
    Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors.

\n\nSo what happens? Decode first uses json or yaml to unmarshal the serialized data into your external MyAPIObject. That causes the raw JSON to be stored, but not unpacked. The next step is to copy (using pkg/conversion) into the internal struct. The runtime package's DefaultScheme has conversion functions installed which will unpack the JSON stored in RawExtension, turning it into the correct object type, and storing it in the Object. (TODO: In the case where the object is of an unknown type, a...

\n\nSo what happens? Decode first uses json or yaml to unmarshal the serialized data into your external MyAPIObject. That causes the raw JSON to be stored, but not unpacked. The next step is to copy (using pkg/conversion) into the internal struct. The runtime package's DefaultScheme has conversion functions installed which will unpack the JSON stored in RawExtension, turning it into the correct object type, and storing it in the Object. (TODO: In the case where the object is of an unknown type, a...

		if recover() == nil {
			t.Fatalf("unlock of unlocked RWMutex did not panic")
		}
	}()
	mu := NewDRWMutex(ds, "test")
	mu.Unlock(context.Background())
}

// Borrowed from rwmutex_test.go
func TestUnlockPanic2(t *testing.T) {
	mu := NewDRWMutex(ds, "test-unlock-panic-2")
	defer func() {
		if recover() == nil {
			t.Fatalf("unlock of unlocked RWMutex did not panic")
		}

func TestUnlockPanic(t *testing.T) {
	defer func() {
		if recover() == nil {
			t.Fatalf("unlock of unlocked RWMutex did not panic")
		}
	}()
	mu := NewLRWMutex()
	mu.Unlock()
}

// Borrowed from rwmutex_test.go
func TestUnlockPanic2(t *testing.T) {
	defer func() {
		if recover() == nil {
			t.Fatalf("unlock of unlocked RWMutex did not panic")
		}
	}()
	mu := NewLRWMutex()
	mu.RLock()
	mu.Unlock()
}

		printlock()
		print("runtime: checkmarks found unexpected unmarked object obj=", hex(obj), "\n")
		print("runtime: found obj at *(", hex(base), "+", hex(off), ")\n")

		// Dump the source (base) object
		gcDumpObject("base", base, off)

		// Dump the object
		gcDumpObject("obj", obj, ^uintptr(0))

		getg().m.traceback = 2
		throw("checkmark found unmarked object")
	}

	ai := arenaIndex(obj)