if (mirrored_variable_indices_attr) {
      for (const auto& mirrored_index : mirrored_variable_indices_attr) {
        mirrored_replicate_args.insert(
            mlir::cast<IntegerAttr>(mirrored_index).getInt());
      }
    }
    auto func =
        llvm::cast<func::FuncOp>(m.lookupSymbol(cluster_func.getFunc()));
    for (auto entry : llvm::enumerate(cluster_func.getOperands())) {

        RankedTensorType::get({2}, rewriter.getF32Type()),
        {static_cast<float>(min.getValueAsDouble()),
         static_cast<float>(max.getValueAsDouble())});
    ElementsAttr axis_stats;
    IntegerAttr axis;

    quantfork::StatisticsOp stats_op = rewriter.create<quantfork::StatisticsOp>(
        op->getLoc(), op.getInput(), layer_stats, axis_stats, axis);
    op.getOutput().replaceAllUsesWith(stats_op.getResult());

  // The StringRef cast is necessary before cxx14.
  mlir::IntegerAttr group_key_attr =
      module_op->getAttrOfType<mlir::IntegerAttr>(
          mlir::StringRef(kGroupKeyAttrName.data(), kGroupKeyAttrName.size()));
  mlir::IntegerAttr group_size_attr =
      module_op->getAttrOfType<mlir::IntegerAttr>(mlir::StringRef(
          kGroupSizeAttrName.data(), kGroupSizeAttrName.size()));

// is an IntegerAttr, containing axis in the TensorFlow format. TensorFlow
// format supports negative indexing unlike HLO.
static IntegerAttr GetHLOAxisFromTFAxis(Attribute attr, int64_t rank,
                                        Builder *b) {
  IntegerAttr intAttr = mlir::dyn_cast_or_null<IntegerAttr>(attr);
  if (auto elementAttr = mlir::dyn_cast_or_null<ElementsAttr>(attr)) {

    int64_t arg_index = mlir::cast<IntegerAttr>(entry.value()).getInt();
    auto arg_metadata = metadata.mutable_args(arg_index);
    if (arg_metadata->enable_xla_sharding() ==
        ::tensorflow::tpu::TPUCompileMetadataProto_Arg::ALLOWED) {
      int64_t ret_index =
          mlir::cast<IntegerAttr>(
              execute.getDeviceVarUpdatesIndices().getValue()[entry.index()])

  if (failed(tensorflow::GetDevicesFromOp(module, &devices))) return failure();

  uint32_t num_replicas = replicate.getN();

  auto num_cores_per_replica_attr = cluster->getAttrOfType<mlir::IntegerAttr>(
      tensorflow::kNumCoresPerReplicaAttr);
  if (!num_cores_per_replica_attr)
    return cluster.emitOpError(
        CreateMissingAttributeMsg(tensorflow::kNumCoresPerReplicaAttr));

def GetOffSet : NativeCodeCall<"TFL::GetOffSet($0, $1)">;

// Attribute Constraint that checks if the attribute value is zero.
def ZeroIntAttr
  : AttrConstraint<CPred<"$_self.cast<::mlir::IntegerAttr>().getInt() == 0">>;

// Checks if the value has rank at most 'n'.
class HasRankAtLeast<int n> : Constraint<
    CPred<"$0.getType().cast<ShapedType>().hasRank() && "

// tf_executor.SwitchN
//===----------------------------------------------------------------------===//

LogicalResult SwitchNOp::verify() {
  SwitchNOp switchn = *this;
  IntegerAttr num_outs = switchn->getAttrOfType<IntegerAttr>("num_outs");
  if (!num_outs)
    return switchn.emitOpError() << "expects a `num_outs` integer attribute";

  // Expect num_outs results + 1 control output.

    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());
    Type res_type = tf_op.getType();
    TypeAttr qtype = quant::GetQuantizedTypeAttr(

    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());
    Type res_type = tf_op.getType();
    TypeAttr qtype = quant::GetQuantizedTypeAttr(