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;

#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.

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*);

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

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;

    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));

  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,

        actionUrl,
        docId;
    if (values.length === 2 && $queryId.length > 0) {
      docId = values[1];
      actionUrl = contextPath + "/api/v1/documents/" + docId + "/favorite";
      $.ajax({
        dataType: "json",
        cache: false,
        type: "post",
        timeoutNumber: 10000,
        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

  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;
  builder->Set(*iter, tensorflow::gtl::ArraySlice<const tensorflow::DataType>(

  if (!s.ok()) {
    device_name = "<error fetching device name>";
  }
  return absl::StrCat("TensorHandle(", value_string, ", shape=", shape_string,
                      ", dtype=", DataType_Name(DataType()), ", device=\"",
                      device_name, "\")");
}

absl::Status ImmediateExecutionTensorHandle::SummarizeValue(
    std::string& summary) const {
  absl::Status status;
  AbstractTensorPtr resolved(