} else if (auto int_attr =
                   mlir::dyn_cast_or_null<IntegerAttr>(attr.second)) {
      fbb.Int(attr.first.data(), int_attr.getInt());
    } else if (auto bool_attr = mlir::dyn_cast_or_null<BoolAttr>(attr.second)) {
      fbb.Bool(attr.first.data(), bool_attr.getValue());
    } else if (auto string_attr =
                   mlir::dyn_cast_or_null<StringAttr>(attr.second)) {

  >;

// This decomposition is only correct inside XLA as it ignores use_locking
// attribute.
def DecomposeResourceApplyGradientDescentOp :
  Pat<
    (TF_ResourceApplyGradientDescentOp:$src_op $resource, $alpha, $delta, BoolAttr:$_),
    (TF_AssignVariableOp
      $resource,
      (TF_SubOp
        (CreateTFReadVariableOp $src_op, $alpha, $resource),
        (TF_MulOp $alpha, $delta)
      ),
      (CreateConstBoolAttrFalse)
    )

  std::optional<QuantizationUnitLoc::QuantizationUnit> quant_unit =
      FindQuantizationUnitFromLoc(call_op->getLoc());
  return std::make_pair(quant_unit->func_name(), quant_unit->node_name());
}

std::pair<std::string, std::string> GetFuncNameAndNodeName(
    TF::XlaCallModuleOp call_op, const FlatSymbolRefAttr &f_attr) {
  return std::make_pair(f_attr.getValue().str(), kEmptyNodeName.str());
}

def LegalizeMin : Pat<
  (TF_MinOp $arg0, $axes, BoolAttr:$arg2),
  (TFL_ReduceMinOp $arg0, (CreateTFCastToInt32Op $axes), $arg2)>;

def LegalizeMax : Pat<
  (TF_MaxOp $arg0, $axes, BoolAttr:$arg2),
  (TFL_ReduceMaxOp $arg0, (CreateTFCastToInt32Op $axes), $arg2)>;

def LegalizeProd : Pat<
  (TF_ProdOp $arg0, $axes, BoolAttr:$arg2),
  (TFL_ReduceProdOp $arg0, (CreateTFCastToInt32Op $axes), $arg2)>;

static bool FloatValueEquals(const Attribute &attr, double value) {
  auto fp_attr = mlir::dyn_cast_or_null<DenseFPElementsAttr>(attr);
  if (fp_attr == nullptr) return false;

  if (fp_attr.isSplat()) {
    return fp_attr.getSplatValue<APFloat>().isExactlyValue(value);
  }
  return llvm::all_of(fp_attr.getValues<APFloat>(), [value](const APFloat &f) {
    return f.isExactlyValue(value);
  });
}

    I64Attr:$num_bits,
    // Quantization range starts from 0 or 1; starts from 1 if true.
    DefaultValuedOptionalAttr<BoolAttr, "false">:$narrow_range,
    // The sign of the quantization.
    DefaultValuedOptionalAttr<BoolAttr, "false">:$is_signed
  );

  let results = (outs
    F32Tensor:$outputs
  );
}

    if (callable->hasAttr("tf._XlaMustCompile")) return true;
    auto noinline_attr_name = absl::StrCat("tf.", tensorflow::kNoInlineAttr);
    if (auto noinline_attr =
            callable->getAttrOfType<BoolAttr>(noinline_attr_name))
      return !noinline_attr.getValue();
    return true;
  }

  // Returns if its legal to inline 'src' region into the 'dest' region
  // attached to a TF operation.

    auto c_false =
        rewriter.create<arith::ConstantOp>(loc, rewriter.getBoolAttr(false));
    TypeAttr f32_attr = TypeAttr::get(rewriter.getF32Type());
    TFRAttrType output_type = TFRAttrType::get(rewriter.getContext());
    auto constant_f32_op = rewriter.create<ConstOp>(loc, output_type, f32_attr);
    TypeAttr i32_attr = TypeAttr::get(rewriter.getI32Type());

    // Note: this information is duplicated and can be removed from the proto
    // and here once MLIR bridge phase 2 doesn't fallback to the old bridge.
    auto attr = op.getFuncOp().getArgAttrOfType<mlir::BoolAttr>(
        index, replication_attr_name);
    arg->set_is_same_data_across_replicas(attr != nullptr && attr.getValue());

    // Currently only support first dimension to be bounded dynamic.

      TF_RETURN_IF_ERROR(ConvertLocation(locations[i], node_name, debug_info));
    }
  }
  return absl::OkStatus();
}

Status ConvertAttribute(const mlir::BoolAttr& attr, AttrValue* value) {
  value->set_b(attr.getValue());
  return absl::OkStatus();
}

Status ConvertAttribute(const mlir::IntegerAttr& attr, AttrValue* value) {
  value->set_i(attr.getInt());