virtual bool GetBool(absl::string_view attr_name, bool* result) const = 0;
  virtual bool GetType(absl::string_view attr_name, DataType* result) const = 0;
  virtual absl::Status GetTypeList(
      absl::string_view attr_name,
      absl::InlinedVector<DataType, 4>* type_list) const = 0;

 private:
  const AbstractOpAttrsKind kind_;
};

}  // namespace tensorflow

// Return a tensor handle with given type, values and dimensions.
template <class T, TF_DataType datatype>
TFE_TensorHandle* TestTensorHandleWithDims(TFE_Context* ctx, const T* data,
                                           const int64_t* dims, int num_dims) {
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, datatype, dims, num_dims, status);
  memcpy(TF_TensorData(t), data, TF_TensorByteSize(t));

  explicit AbstractTensorHandle(AbstractTensorHandleKind kind) : kind_(kind) {}
  ~AbstractTensorHandle() override {}

 public:
  // Returns tensor dtype.
  virtual tensorflow::DataType DataType() const = 0;

  // Returns the status of the tensor handle. If it is a tfrt::TensorHandle,
  // the tensor handle can be an error and return non-OK status.
  virtual absl::Status TensorHandleStatus() const;

absl::Status SetAttrBool(AbstractOperation*, const char* attr_name, bool value,
                         ForwardOperation*);
absl::Status SetAttrType(AbstractOperation*, const char* attr_name,
                         DataType value, ForwardOperation*);
absl::Status SetAttrShape(AbstractOperation*, const char* attr_name,
                          const int64_t* dims, const int num_dims,
                          ForwardOperation*);

#else
#define TF_CAPI_EXPORT __attribute__((visibility("default")))
#endif  // TF_CAPI_WEAK
#endif  // _WIN32
#endif  // SWIG

// TF_Bool is the C API typedef for unsigned char, while TF_BOOL is
// the datatype for boolean tensors.
#ifndef TF_Bool
#define TF_Bool unsigned char
#endif  // TF_Bool

// Macro used to calculate struct size for maintaining ABI stability across
// different struct implementations.

//
// =============================================================================

using tensorflow::AbstractFunction;
using tensorflow::AbstractTensorHandle;
using tensorflow::DataType;
using tensorflow::dyn_cast;
using tensorflow::OutputList;
using tensorflow::Status;
using tensorflow::unwrap;
using tensorflow::wrap;
using tensorflow::tracing::CreateTracingExecutionContext;

  virtual absl::Status SetAttrFloat(const char* attr_name, float value) = 0;
  virtual absl::Status SetAttrBool(const char* attr_name, bool value) = 0;
  virtual absl::Status SetAttrType(const char* attr_name, DataType value) = 0;
  virtual absl::Status SetAttrShape(const char* attr_name, const int64_t* dims,
                                    const int num_dims) = 0;
  virtual absl::Status SetAttrShape(const char* attr_name,

    TF_Status* out_status) const {
  absl::Status status;
  if (reader_ != nullptr) {
    status = reader_->GetTensor(name, out_tensor);
  } else {
    tensorflow::DataType dtype;
    tensorflow::TensorShape shape;
    status = v2_reader_->LookupDtypeAndShape(name, &dtype, &shape);
    if (status.ok()) {
      out_tensor->reset(new Tensor(dtype, shape));

    if (values.length === 2 && $queryId.length > 0) {
      var docId = values[1];
      var actionUrl = contextPath + "/api/v1/documents/" + docId + "/favorite";

      $.ajax({
        dataType: "json",
        cache: false,
        type: "post",
        timeout: AJAX_TIMEOUT,
        url: actionUrl,
        data: {
          queryId: $queryId.val()
        }
      })

    shape_string = "<error computing shape>";
  } else {
    shape_string = shape.DebugString();
  }
  return absl::StrCat("TensorHandle(shape=", shape_string,
                      ", dtype=", DataType_Name(DataType()),
                      ", type=", FullType().DebugString(), ")");
}

absl::Status AbstractTensorHandle::TensorHandleStatus() const {
  // Tensor handles in current runtime don't carry error info and this method