string AttrView::DefaultValue() const {
  const AttrValue &attr_value = attr_.default_value();
  switch (attr_value.value_case()) {
    case AttrValue::VALUE_NOT_SET:
      return "";
    case AttrValue::kType:
      return DataType_Name(attr_value.type());
    case AttrValue::kS:
      return "\"" + attr_value.s() + "\"";
    case AttrValue::kI:
      return std::to_string(attr_value.i());
    case AttrValue::kF:

  }
  return ret;
}

string PrintTensor(const TensorProto& tensor_proto);

string PrintTensorProto(const TensorProto& proto);

string PrintAttrValue(StringPiece, const AttrValue& attr_value);

bool IsEmptyList(const AttrValue::ListValue& list);

string ToCamelCase(StringPiece str);

string SeparateNamespaces(StringPiece str);

// Returns a <string, bool> pair. The string is the C++ type name to be used for

absl::StatusOr<mlir::Attribute> ConvertNonFuncAttributeValue(
    const AttrValue& value, mlir::Builder* builder);

// Converts all kinds of AttrValue proto into an MLIR attribute.
absl::StatusOr<mlir::Attribute> ConvertAttributeValue(const AttrValue& value,
                                                      mlir::Builder* builder);

}  // namespace tensorflow

          device.has_job && device.job == jobType) {
        for (const auto& attr : node->attrs()) {
          auto attr_key = attr.first;
          auto attr_value = attr.second;
          if (attr_key == attrKey &&
              attr_value.value_case() == AttrValue::kType &&
              attr_value.type() == DT_RESOURCE) {
            return true;
            break;
          }
        }
      }
    }
  }
  return false;

  const string& base_type() const { return base_type_; }
  const AttrValue& default_value() const { return default_value_; }
  const bool is_list() const { return is_list_; }

 private:
  explicit AttrSpec(const OpDef::AttrDef& attr_def);

  string name_;
  string description_;
  string full_type_;
  string base_type_;
  AttrValue default_value_;
  bool is_list_;
};

}  // namespace generator

typedef struct TFE_Op TFE_Op;

namespace tensorflow {
DEFINE_CONVERSION_FUNCTIONS(tensorflow::AbstractOpAttrs, TFE_OpAttrs);

// Set an AttrValue on the op. Doesn't handle the list types.
void SetOpAttrValueScalar(TFE_Context* ctx, TFE_Op* op,
                          const tensorflow::AttrValue& default_value,
                          const char* attr_name, TF_Status* status);
}  // namespace tensorflow

  (*op)->setAttr("data_format", StringAttr::get(context, target_data_format));

  for (auto pair : shuffle_attrs) {
    StringRef attr_name = pair.first;
    ArrayAttr attr_value = pair.second;
    (*op)->setAttr(attr_name,
                   ShuffleArrayAttr(attr_value, reverse_permutation));
  }

  auto fold = cast<FoldOperandsTransposeInterface>(op->getOperation());
  for (unsigned idx : fold.GetLayoutDependentResults()) {

bool GetFunctionDef(TF_Function* func, tensorflow::FunctionDef* func_def);

bool GetAttrValue(TF_Operation* oper, const char* attr_name,
                  tensorflow::AttrValue* attr_value, TF_Status* s);

// Returns a sorted vector of std::pair<function_name, gradient_func> from
// graph_def.library().gradient()
std::vector<std::pair<string, string>> GetGradDefs(

    }
  }
  return true;
}

bool HasSymbolRefAttr(Operation* op) {
  for (const auto& attr : op->getAttrs()) {
    Attribute attr_value = attr.getValue();
    if (mlir::isa<SymbolRefAttr>(attr_value)) {
      return true;
    } else if (auto array_attr = mlir::dyn_cast<ArrayAttr>(attr_value)) {
      if (!array_attr.empty() &&
          mlir::isa<SymbolRefAttr>(*array_attr.begin())) {
        return true;
      }
    }

#include "llvm/ADT/StringRef.h"
#include "mlir/IR/Operation.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/utils/export_utils.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/op_def_builder.h"
#include "tensorflow/core/platform/status.h"