}

namespace {

// Helper method that creates a shape handle for a shape described by dims.
tensorflow::shape_inference::ShapeHandle ShapeHandleFromDims(
    tensorflow::shape_inference::InferenceContext* ic, int num_dims,
    const int64_t* dims) {
  if (num_dims != -1) {
    std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec;
    dim_vec.reserve(num_dims);
    for (int i = 0; i < num_dims; ++i) {

  using tensorflow::NodeDef;
  using tensorflow::OpRegistrationData;
  using tensorflow::Tensor;
  using tensorflow::shape_inference::DimensionHandle;
  using tensorflow::shape_inference::InferenceContext;
  using tensorflow::shape_inference::ShapeAndType;
  using tensorflow::shape_inference::ShapeHandle;

  const int num_inputs = input_shapes->num_items;
  NodeDef node_def;

      graph_properties);
}

void TF_InferStatically(TF_GraphProperties* graph_properties,
                        TF_Bool assume_valid_feeds,
                        TF_Bool aggressive_shape_inference,
                        TF_Bool include_input_tensor_values,
                        TF_Bool include_output_tensor_values,
                        TF_Status* status) {
  TF_SetStatus(status, TF_OK, "");

      .Attr("v: " #type)                                      \
      .SetShapeFn(tensorflow::shape_inference::UnknownShape); \
  REGISTER_OP("CApiAttributesTestOpList" #type)               \
      .Attr("v: list(" #type ")")                             \
      .SetShapeFn(tensorflow::shape_inference::UnknownShape)
ATTR_TEST_REGISTER_OP(string);
ATTR_TEST_REGISTER_OP(int);
ATTR_TEST_REGISTER_OP(float);

namespace tensorflow {
namespace tracing {
namespace graph {

class GraphContext;
class GraphOperation;
class GraphTensor;

auto& kUnknownDim = shape_inference::InferenceContext::kUnknownDim;
auto& kUnknownRank = shape_inference::InferenceContext::kUnknownRank;

// GraphTensor wraps a `TF_Output`, i.e. a pointer to TF_Operation and the index
// into the list of outputs for the operation.

  CHECK(tensorflow::unwrap(concatOp)->OpDef());
  TFE_OpAddInput(concatOp, inputs[0], status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  EXPECT_FALSE(tensorflow::unwrap(concatOp)->OpDef())
      << "Inference context is still present";
  TFE_OpAddInput(concatOp, inputs[1], status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  tensorflow::AttrValueMap attr_values = ExtractAttrs(concatOp);

  }

  ~ShapeInferenceTest() override {
    TFE_DeleteContextOptions(tfe_context_options_);
    TFE_DeleteContext(tfe_context_);
    TF_DeleteStatus(status_);
  }

  // Checks the expected result of shape inference for the given `op`.
  void CheckOutputShapes(
      TFE_Op* op,
      const std::vector<absl::optional<std::vector<int64_t>>>& input_shapes_vec,
      const std::vector<TF_Tensor*>& input_tensors,

    s = fs->CreateDir(root_dir_);
    if (!s.ok()) {
      GTEST_SKIP() << "Cannot create working directory: " << s;
    }
  }

  // Converts path reference to URI reference.
  //
  // If URI scheme is empty, URI reference is `path` relative to current test
  // root directory. Otherwise, we need to add the `<scheme>://` in front of
  // this path.
  //

// original framework of destruction, and this context will be deleted also.
struct TfDlManagedTensorCtx {
  TensorReference reference;
  std::vector<int64_t> shape;
  std::vector<int64_t> strides;
  DLManagedTensor tensor;

  explicit TfDlManagedTensorCtx(const TensorReference& ref) : reference(ref) {}
};

// Gets tensor from eager tensor handle.
const Tensor* GetTensorFromHandle(TFE_TensorHandle* h, TF_Status* status) {

  const DataType& dt = node->output_type(idx);
  return IsRefType(dt)
             ? InvalidArgument("Output ", idx, " of node '", node->name(),
                               "' has a reference type ", DataTypeString(dt))
             : absl::OkStatus();
}

// Converts `ninputs` and `inputs` into `inputs_tensors` and `input_nodes` and
// does various checks while doing so. `input_nodes` will contain the same