break;
    case TF_DataType::TF_INT8:
    case TF_DataType::TF_INT16:
    case TF_DataType::TF_INT32:
    case TF_DataType::TF_INT64:
      dtype.code = DLDataTypeCode::kDLInt;
      break;
    case TF_DataType::TF_UINT8:
    case TF_DataType::TF_UINT16:
    case TF_DataType::TF_UINT32:
    case TF_DataType::TF_UINT64:
      dtype.code = DLDataTypeCode::kDLUInt;
      break;

  handle_ = other.handle_;
  handle_->Ref();
}
TapeTensor::~TapeTensor() { handle_->Unref(); }

int64_t TapeTensor::GetID() const { return ToId(handle_); }

tensorflow::DataType TapeTensor::GetDType() const {
  return handle_->DataType();
}
AbstractTensorHandle* TapeTensor::GetHandle() const { return handle_; }

AbstractTensorHandle* TapeTensor::ZerosLike() const { return nullptr; }

class TapeVSpace

        ],
    }),
)

cc_library(
    name = "tf_datatype_hdrs",
    hdrs = ["tf_datatype.h"],
    visibility = ["//tensorflow:internal"],
    deps = [
        ":c_api_macros_hdrs",
    ],
)

cc_library(
    name = "tf_datatype",
    srcs = ["tf_datatype.cc"],
    hdrs = ["tf_datatype.h"],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [

        "Number of values did not match number of underlying devices.");
    return nullptr;
  }
  TF_DataType datatype_enum(
      static_cast<TF_DataType>(DataTypeToEnum<DataType>().value));
  for (int device_index = 0; device_index < num_underlying_devices();
       ++device_index) {
    auto device_value = absl::make_unique<DataType>();
    *device_value = values[device_index];

    TF_CheckpointReader* reader, int index);
// Get the number of variable in the checkpoint
TF_CAPI_EXPORT extern int TF_CheckpointReaderSize(TF_CheckpointReader* reader);
// Get the DataType of a variable
TF_CAPI_EXPORT extern TF_DataType TF_CheckpointReaderGetVariableDataType(
    TF_CheckpointReader* reader, const char* name);
// Read the shape of a variable and write to `dims`

                           TF_DataType value) {
  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, static_cast<tensorflow::DataType>(value));
}

void TF_AttrBuilderSetTypeList(TF_AttrBuilder* builder, const char* attr_name,
                               const TF_DataType* values, int num_values) {
  auto iter = builder->attr_names.insert(attr_name).first;

 public:
  explicit GraphTensor(TF_Output output, TF_Graph* graph)
      : TracingTensorHandle(kGraph), output_(output), graph_(graph) {}

  tensorflow::DataType DataType() const override {
    return static_cast<tensorflow::DataType>(TF_OperationOutputType(output_));
  }

  tensorflow::Status Shape(
      tensorflow::PartialTensorShape* shape) const override {
    DCHECK(shape != nullptr);
    TF_Status status;

      "TFE_DeleteTensorHandle", tensorflow::profiler::TraceMeLevel::kInfo);
  if (h) {
    tensorflow::unwrap(h)->Unref();
  }
}

TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h) {
  return static_cast<TF_DataType>(tensorflow::unwrap(h)->DataType());
}

int TFE_TensorHandleNumDims(TFE_TensorHandle* h, TF_Status* status) {
  if (h == nullptr) {

  }

  // Returns whether any tensor in a list of tensors is being watched and has
  // a trainable dtype.
  bool ShouldRecord(absl::Span<const int64_t> tensor_ids,
                    absl::Span<const tensorflow::DataType> dtypes) const;

  // Adds this tensor to the list of watched tensors.
  //
  // This is a no-op if the tensor is already being watched either from an

// Indicates that the caller will not be using `h` any more.
TF_CAPI_EXPORT extern void TFE_DeleteTensorHandle(TFE_TensorHandle* h);
TF_CAPI_EXPORT extern TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h);
// This function will block till the operation that produces `h` has completed.
TF_CAPI_EXPORT extern int TFE_TensorHandleNumDims(TFE_TensorHandle* h,