// outputs are as expected.
  // 'T' stands for 'tensor' since the outputs are tensors, not scalars.
  void RunT(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,
            std::initializer_list<TF_Output> outputs,
            const std::vector<std::vector<int32_t>>& expected_results) {
    // Create a session for this graph
    CSession csession(host_graph_, s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

    local rowPanels =
      std.filter(
        function(p) p.type == 'row',
        grouped
      );

    local CalculateXforPanel(index, panel) =
      local panelsPerRow = std.floor(gridWidth / panel.gridPos.w);
      local col = std.mod(index, panelsPerRow);
      panel + { gridPos+: { x: panel.gridPos.w * col } };

    local panelsBeforeRowsWithX = std.mapWithIndex(CalculateXforPanel, panelsBeforeRows);

                 std::unique_ptr<tensorflow::Tensor>* out_tensor,
                 TF_Status* out_status) const;

 private:
  // Uses "v2_reader_" to build "var name -> shape" and "var name -> data type"
  // maps; both owned by caller.
  // REQUIRES: "v2_reader_ != nullptr && v2_reader_.status().ok()".
  std::pair<std::unique_ptr<TensorSliceReader::VarToShapeMap>,

    tsl::Set_TF_Status_from_Status(out_status, status);
  }
}

std::pair<std::unique_ptr<TensorSliceReader::VarToShapeMap>,
          std::unique_ptr<TensorSliceReader::VarToDataTypeMap>>
CheckpointReader::BuildV2VarMaps() {
  CHECK(v2_reader_ != nullptr);
  CHECK(v2_reader_->status().ok());

  // First pass: filters out the entries of the slices.
  std::unordered_set<string> filtered_keys;
  BundleEntryProto entry;

                           const string& op_name) {
  std::vector<int64_t> input_ids(inputs.size());
  std::vector<tensorflow::DataType> input_dtypes(inputs.size());
  for (int i = 0; i < inputs.size(); i++) {
    input_ids[i] = ToId(inputs[i]);
    input_dtypes[i] = inputs[i]->DataType();
  }
  std::vector<TapeTensor> tape_tensors;
  tape_tensors.reserve(outputs.size());
  for (auto t : outputs) {

  TFE_OpSetAttrString(op, "final_op", "Id", 2);
  std::vector<int64_t> subdiv_offsets;
  TFE_OpSetAttrIntList(op, "subdiv_offsets", subdiv_offsets.data(),
                       subdiv_offsets.size());

  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,

  }

  // 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,
      const absl::optional<std::vector<int64_t>>& expected_shape) {
    // Create input_shapes.
    TF_ShapeAndTypeList* input_shapes =

               const std::string& op_name, const std::string& send_device,
               const std::string& recv_device,
               tensorflow::uint64 send_device_incarnation);

// Return a RecvOp op `op_name` with the attributes `send_device`,
// `recv_device`, and `send_device_incarnation` set.
TFE_Op* RecvOp(TFE_Context* ctx, const std::string& op_name,

      ImmediateExecutionTensorHandle* handle) = 0;

  virtual std::vector<std::string> GetLoggedOpsTestonly() { return {}; }

  // Get a list of the names of functions that have been registered.
  virtual std::vector<string> ListFunctionNames() = 0;

  struct CacheStats {
    int64_t kernel_cache_size;
    int64_t device_cache_size;
    std::map<std::string, int64_t> func_kernel_cache_entries;

  InferenceContext c(TF_GRAPH_DEF_VERSION, node_def, op_reg_data->op_def,
                     std::vector<ShapeHandle>(num_inputs), input_tensors_vector,
                     {},
                     std::vector<std::unique_ptr<std::vector<ShapeAndType>>>());

  // Set input_shapes.
  for (int i = 0; i < num_inputs; ++i) {
    std::vector<DimensionHandle> dims;
    const TF_ShapeAndType& input_shape = input_shapes->items[i];