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

Status AbstractTensorHandle::TensorHandleStatus() const {
  // Tensor handles in current runtime don't carry error info and this method
  // should always return OK status.

  // can be used to set a FullTypeDef based on dtype in a derived class if
  // appropriate.
  virtual tensorflow::FullTypeDef FullType() const = 0;

  // The default debug string includes a shape, dtype and FullType.
  // Implementations are free to override it with something more informative.
  virtual std::string DebugString() const;

  AbstractTensorHandleKind getKind() const { return kind_; }

    TF_RETURN_IF_ERROR(StatusFromTF_Status(&status));
    TF_RETURN_IF_ERROR(tensorflow::TensorShapeUtils::MakeShape(dims, shape));

    return absl::OkStatus();
  }

  tensorflow::FullTypeDef FullType() const override {
    const FullTypeDef* ft;
    mutex_lock l(graph_->mu);
    graph_->graph.NodeType(output_.oper->node.name(), &ft);
    if (ft == nullptr) {
      return FullTypeDef();
    } else {

  // Test that full type information can be accessed.
  auto outs = unwrap(add_outputs);
  auto h = outs->outputs[0];
  ASSERT_NE(h, nullptr);
  ASSERT_EQ(h->FullType().type_id(), TFT_UNSET);
  ASSERT_EQ(unwrap(inputs[0])->FullType().type_id(), TFT_UNSET);

  // Clean up operation and inputs.
  TF_DeleteAbstractOp(add_op);

  TF_AbstractFunction* func =

  FullTypeDef full_type;
  full_type.ParseFromArray(full_type_proto->data, full_type_proto->length);
  *op->node.mutable_def()->mutable_experimental_type() = full_type;
  RecordMutation(graph, *op, "setting fulltype");
}

void TF_SetRequestedDevice(TF_Graph* graph, TF_Operation* op,
                           const char* device) {
  using tensorflow::RecordMutation;
  mutex_lock l(graph->mu);