op = strings.Replace(op, "(*stsAPIHandlers)", "sts", 1)
	op = strings.Replace(op, "(*peerS3Server)", "s3", 1)
	op = strings.Replace(op, "ClusterCheckHandler", "health.Cluster", 1)
	op = strings.Replace(op, "ClusterReadCheckHandler", "health.ClusterRead", 1)
	op = strings.Replace(op, "LivenessCheckHandler", "health.Liveness", 1)
	op = strings.Replace(op, "ReadinessCheckHandler", "health.Readiness", 1)
	op = strings.Replace(op, "-fm", "", 1)
	return op
}

  SmallVector<NamedAttribute> attrs{op->getAttrs()};
  attrs.push_back(call_target);
  auto custom_call = rewriter.create<mlir::stablehlo::CustomCallOp>(
      op->getLoc(), op->getResultTypes(), op->getOperands(), attrs);
  rewriter.replaceOp(op, custom_call.getResults());
  return success();
}

}  // namespace

template <typename OpTy>

    for (auto& op : graph->operators) {
      for (int idx = 0; idx < op->inputs.size(); idx++) {
        if (op->inputs[idx] < 0) {
          continue;
        }
        const auto& tensor = graph->tensors[op->inputs[idx]];
        auto* expected_tensor = FindMatchingExpectedTensor(
            *expected_graph, expected_model, model, *op, idx);
        if (!expected_tensor) {
          continue;

// these predicates.

// Returns true if the given TensorFlow op does not have a `data_format`
// attribute (then default to "NHWC"), or its `data_format` attribute is "NHWC".
inline bool TFDataFormatIsNHWC(Operation *op) {
  auto attr = op->getAttrOfType<StringAttr>("data_format");
  return !attr || attr.getValue() == "NHWC";
}

// Returns true if the given TensorFlow op does not have a `data_format`

  llvm::DenseMap<SymbolRefAttr, int> called_indexes;

  WalkResult result = module.walk([&](stablehlo::CustomCallOp op) {
    if (!IsTfFuncCustomCall(op)) {
      return WalkResult::advance();
    }

    auto backend_config =
        op->getAttrOfType<DictionaryAttr>(kTfBackendConfigAttrName);
    if (!backend_config) {
      op->emitOpError() << "is missing attribute '" << kTfBackendConfigAttrName
                        << "'";

Operation* GetUserIfOnlyOne(Operation* op) {
  if (op->getNumResults() != 1) return nullptr;
  auto result = op->getResult(0);
  if (!result.hasOneUse()) return nullptr;
  return (*result.getUses().begin()).getOwner();
}

// Gets operation providing value for the given operand of given operation
// if the given operation is the only user.
Operation* GetInputOpWithOneUse(Operation* op, int opr_num) {

def IsPrecisionEmpty :
  Constraint<CPred<"IsPrecisionEmpty($0)">>;

// Creates Einsum Op from XlaDotV2 Op by generating equation.
def CreateEinsumOpFromXlaDotV2Op : NativeCodeCall<
  "CreateEinsumOpFromXlaDotV2Op($_builder, $_loc, $0...)">;

// Convert XlaDotV2 Op to Einsum Op with above two functions.
def ConvertXlaDotV2OpToEinsumOp : Pat<

}

// Returns true if this is a basic LSTM op.
bool IsBasicLSTMOp(tflite::BuiltinOptionsUnion op_union) {
  if (const auto* op = op_union.AsLSTMOptions()) {
    return op->kernel_type == tflite::LSTMKernelType_BASIC;
  } else {
    return false;
  }
}

// Gets the MLIR op name with the dialect name for the flatbuffer operator.
std::string GetMlirOpName(const tflite::OperatorT& op,

@tf.RegisterGradient('NewMaxPool')
def _max_pool_grad(op: ops.Operation, grad):
  filter_width = op.get_attr('filter_width')
  filter_height = op.get_attr('filter_height')
  stride_w = op.get_attr('stride_w')
  stride_h = op.get_attr('stride_h')
  padding = op.get_attr('padding')
  return tf.raw_ops.MaxPoolGrad(
      orig_input=op.inputs[0],
      orig_output=op.outputs[0],
      grad=grad,

	case OPLUS, ONEG, OBITNOT, ONOT, ORECV:
		// Unary
		n := n.(*UnaryExpr)
		fmt.Fprintf(s, "%v", n.Op())
		if n.X != nil && n.X.Op() == n.Op() {
			fmt.Fprint(s, " ")
		}
		exprFmt(n.X, s, nprec+1)

	case OADDR:
		n := n.(*AddrExpr)
		fmt.Fprintf(s, "%v", n.Op())
		if n.X != nil && n.X.Op() == n.Op() {
			fmt.Fprint(s, " ")
		}
		exprFmt(n.X, s, nprec+1)

	case ODEREF:
		n := n.(*StarExpr)