private:
  friend class GraphContext;  // For access to op_.
  TF_Graph* g_;
  std::unique_ptr<TF_OperationDescription> op_;
  // Hold `op_type` and `op_name` till both are available since we need both
  // to build a graph operation.
  string op_type_;
  const char* op_name_ = nullptr;
  // TODO(srbs): Use this.
  string device_name_;
};

// GraphContext wraps a TF_Graph modeling a single function and manages the

}  // namespace

absl::Status GradientRegistry::Register(
    const string& op_name, GradientFunctionFactory gradient_function_factory) {
  auto iter = registry_.find(op_name);
  if (iter != registry_.end()) {
    const string error_msg = "Gradient already exists for op: " + op_name + ".";
    return errors::AlreadyExists(error_msg);
  }
  registry_.insert({op_name, gradient_function_factory});
  return absl::OkStatus();
}

  // `TF_AttrBuilderCheckCanRunOnDevice()` call(s) on the same `builder`.
  std::set<std::string> attr_names;
};

TF_AttrBuilder* TF_NewAttrBuilder(const char* op_name) {
  return new TF_AttrBuilder(op_name);
}

void TF_DeleteAttrBuilder(TF_AttrBuilder* builder) { delete builder; }

void TF_AttrBuilderSetType(TF_AttrBuilder* builder, const char* attr_name,

  // least one input of the op is watched and has a trainable dtype.
  // op_name is optional and is used for debugging only.
  void RecordOperation(absl::Span<AbstractTensorHandle* const> inputs,
                       absl::Span<AbstractTensorHandle* const> outputs,
                       GradientFunction* gradient_function,
                       const string& op_name = "");
  // Returns whether any tensor in a list of tensors is being watched and has

	_ = x[OpMuxServerMsg-10]
	_ = x[OpUnblockSrvMux-11]
	_ = x[OpUnblockClMux-12]
	_ = x[OpAckMux-13]
	_ = x[OpRequest-14]
	_ = x[OpResponse-15]
	_ = x[OpDisconnect-16]
	_ = x[OpMerged-17]
}

const _Op_name = "ConnectConnectResponsePingPongConnectMuxMuxConnectErrorDisconnectClientMuxDisconnectServerMuxMuxClientMsgMuxServerMsgUnblockSrvMuxUnblockClMuxAckMuxRequestResponseDisconnectMerged"

  return op;
}

TFE_Op* SendOp(TFE_Context* ctx, TFE_TensorHandle* in,
               const std::string& op_name, const std::string& send_device,
               const std::string& recv_device,
               tensorflow::uint64 send_device_incarnation) {
  TF_Status* status = TF_NewStatus();
  TFE_Op* op = TFE_NewOp(ctx, op_name.c_str(), status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddInput(op, in, status);

  tsl::Set_TF_Status_from_Status(
      s, unwrap(op)->Reset(op_type,
                           /*raw_device_name=*/nullptr));
}

void TF_AbstractOpSetOpName(TF_AbstractOp* op, const char* const op_name,
                            TF_Status* s) {
  TracingOperation* tracing_op = dyn_cast<TracingOperation>(unwrap(op));
  if (!tracing_op) {
    tsl::Set_TF_Status_from_Status(

 public:
  // Sets the name of the operation: this is an optional identifier that is
  // not intended to carry semantics and preserved/propagated without
  // guarantees.
  virtual absl::Status SetOpName(const char* op_name) = 0;

  // For LLVM style RTTI.
  static bool classof(const AbstractOperation* ptr) {
    return ptr->getKind() == kGraph || ptr->getKind() == kMlir;
  }
};

namespace internal {

        System.out.println(SystemUtil.FILE_ENCODING);
        System.out.println(SystemUtil.LINE_SEPARATOR);
        System.out.println(SystemUtil.PATH_SEPARATOR);
        System.out.println(SystemUtil.OS_NAME);
        System.out.println(SystemUtil.JAVA_IO_TMPDIR);
        System.out.println(SystemUtil.USER_DIR);
        System.out.println(SystemUtil.USER_HOME);
    }

  TF_OperationDescription* init(string type) {
    // Construct op_name to match the name used by REGISTER_OP in the
    // ATTR_TEST_REGISTER calls above.
    string op_name = "CApiAttributesTestOp";
    if (type.find("list(") == 0) {
      op_name += "List";
      type = type.replace(0, 5, "");
      type = type.replace(type.size() - 1, 1, "");
    }
    op_name += type;
    return TF_NewOperation(