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;

  (*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()) {

    }
  }
  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"

      mlir::NameLoc::get(builder->getStringAttr("fused_func")), "fused_func",
      func_type, {});

  func.addEntryBlock();
  mlir::StringAttr attr_value = builder->getStringAttr("MaxUnpooling2D");
  func->setAttr("tf._implements", attr_value);
  return func;
}

func::FuncOp createMaxUnpoolingFunc(
    mlir::Builder* builder, const SmallVector<int64_t, 4>& input_shape,

    case AttrValue::kI:
      return builder->getI64IntegerAttr(value.i());
    case AttrValue::kS:
      return builder->getStringAttr(value.s());
    case AttrValue::kF:
      return builder->getFloatAttr(builder->getF32Type(), value.f());
    case AttrValue::kB:
      return builder->getBoolAttr(value.b());
    case AttrValue::kType: {
      mlir::Type type;
      TF_RETURN_IF_ERROR(ConvertDataType(value.type(), *builder, &type));

                   TensorShape expected_shape, error::Code expected_code) {
  Status status;
  NodeDef def;
  def.set_op("Bitcast");
  def.set_device(DEVICE_CPU);

  AttrValue typeAttr;
  SetAttrValue(input_tensor->dtype(), &typeAttr);

  AttrValue outTypeAttr;
  SetAttrValue(out_type, &outTypeAttr);

  (*def.mutable_attr())["T"] = typeAttr;
  (*def.mutable_attr())["type"] = outTypeAttr;

  def.add_input(