// (definition: https://github.com/torvalds/linux/blob/v6.7/include/uapi/linux/netfilter/nfnetlink_acct.h#L24-L35)
const (
	// NFACCT_NAME
	attrName = 1
	// NFACCT_PKTS
	attrPackets = 2
	// NFACCT_BYTES
	attrBytes = 3
)

// runner implements the Interface and depends on the handler for execution.
type runner struct {
	handler handler
}

				// validate attribute(NFACCT_NAME)
				assert.Equal(t, 1, len(tc.handler.requests[0].data))
				assert.Equal(t,
					tc.handler.requests[0].data[0].Serialize(),
					nl.NewRtAttr(attrName, nl.ZeroTerminated(tc.counterName)).Serialize(),
				)
			}
		})
	}
}
func TestRunner_Get(t *testing.T) {
	testCases := []struct {
		name         string
		counterName  string

				return nil
			}
			for _, pcs := range ranges {
				if pcs[0] <= addr && addr < pcs[1] {
					var ok bool
					// TODO: AT_linkage_name, AT_MIPS_linkage_name.
					name, ok = entry.Val(dwarf.AttrName).(string)
					if ok {
						break FindName
					}
				}
			}
		}
	}

	// TODO: Report inlined functions.

	frames := []driver.Frame{
		{
			Func: name,

	// Scan DWARF info for top-level TagVariable entries with AttrName __cgo__i.
	types := make([]dwarf.Type, len(names))
	r := d.Reader()
	for {
		e, err := r.Next()
		if err != nil {
			fatalf("reading DWARF entry: %s", err)
		}
		if e == nil {
			break
		}
		switch e.Tag {
		case dwarf.TagVariable:
			name, _ := e.Val(dwarf.AttrName).(string)
			// As of https://reviews.llvm.org/D123534, clang

// with the key `attr_name` as the type provided by `attr_type`.
class GetCompositeAttributeAs<string attr_name, string attr_type>:
  NativeCodeCall<"$0.get(\"" # attr_name # "\").dyn_cast<" # attr_type # ">()">;

// Receives a composite DictionaryAttr and returns the value of the Attribute
// with the key `attr_name` as a DenseIntElementsAttr.
class GetAsVectorAttr<string attr_name>:

  }
}

void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name, int64_t value) {
  auto s = tensorflow::unwrap(op)->SetAttrInt(attr_name, value);
  if (!s.ok()) {
    LOG(WARNING) << "Unable to set attribute: " << attr_name;
  }
}

void TFE_OpSetAttrFloat(TFE_Op* op, const char* attr_name, float value) {
  auto s = tensorflow::unwrap(op)->SetAttrFloat(attr_name, value);
  if (!s.ok()) {

  Status SetAttrString(const char* attr_name, const char* data,
                       size_t length) override;
  Status SetAttrInt(const char* attr_name, int64_t value) override;
  Status SetAttrFloat(const char* attr_name, float value) override;
  Status SetAttrBool(const char* attr_name, bool value) override;
  Status SetAttrType(const char* attr_name,
                     tensorflow::DataType dtype) override;

namespace mlir {
namespace odml {

// Ensure an attribute named attr_name exists and it is of type AttrType.
// If so, sets the `out_attr` pointer to point to the casted attribute.
template <typename AttrType>
bool EnsureAttribute(const DictionaryAttr& composite_attributes,
                     const std::string& attr_name, AttrType* out_attr) {
  Attribute attr = composite_attributes.get(attr_name);
  if (!mlir::isa_and_nonnull<AttrType>(attr)) {

  case DataTypeToEnum<type>::value: {                            \
    kernel_builder->cc_builder->TypeConstraint<type>(attr_name); \
    break;                                                       \
  }

void AddTypeConstraint(TF_KernelBuilder* kernel_builder, const char* attr_name,
                       const DataType dtype, TF_Status* status) {
  // This needs to be under tensorflow:: namespace so that

    }
    desc->node_builder.Attr(attr_name, v);
  }
}

void TF_SetAttrInt(TF_OperationDescription* desc, const char* attr_name,
                   int64_t value) {
  desc->node_builder.Attr(attr_name, static_cast<int64_t>(value));
}

void TF_SetAttrIntList(TF_OperationDescription* desc, const char* attr_name,
                       const int64_t* values, int num_values) {