int64_t GetTransferredTensorBytes(func::CallOp from_graph,
                                  func::CallOp to_graph) {
  int64_t total_size_transferred = 0;
  for (auto input : to_graph.getOperands()) {
    Operation* input_op = input.getDefiningOp();
    if (input_op && input_op == from_graph.getOperation()) {
      auto input_type =
          mlir::dyn_cast_or_null<RankedTensorType>(input.getType());

    DenseFPElementsAttr min_value, max_value;
    if (auto id1 = dyn_cast_or_null<TF::IdentityOp>(min.getDefiningOp())) {
      id1.replaceAllUsesWith(id1.getInput());
      min = tf_op.getMin();
      rewriter.eraseOp(id1);
    }
    if (auto id2 = dyn_cast_or_null<TF::IdentityOp>(max.getDefiningOp())) {
      id2.replaceAllUsesWith(id2.getInput());
      max = tf_op.getMax();
      rewriter.eraseOp(id2);
    }

    // case is useful for float16 quantization. Since all ops have been
    // legalized to tflite ops, so we only care about ConstOp or QConstOp or
    // mlir constant op.
    Operation* input_op = operand.getDefiningOp();
    if (input_op == nullptr) return failure();

    Attribute attr;
    if (matchPattern(input_op, m_Constant(&attr))) {
      // Constant case.
      builder->setInsertionPoint(op);

def EmptyCustomOptions : NativeCodeCall<
  "TFL::ConstBytesAttr::get($_builder.getContext(), \"\")">;

def CustomOptions : NativeCodeCall<
  "CustomOptions($_builder.getContext(), $0.getDefiningOp()).value()">;

class CreateStringAttr<string values> : NativeCodeCall<
  "$_builder.getStringAttr("# values #")">;

def LegalizeTensorListReserve : Pat<(TF_TensorListReserveOp:$tf_op $shape, $num_elements),

  auto bias_op = op->getOperand(bias_index).getDefiningOp<arith::ConstantOp>();
  if (!affine_op || !bias_op || input_indices.size() != 2) return false;
  if (!mlir::isa<DenseFPElementsAttr>(bias_op.getValue())) return false;
  filter_index = affine_op.GetAffineOperandIndex();
  if (!op->getOperand(filter_index).getDefiningOp<arith::ConstantOp>()) {
    return false;
  }

                  AttrType &max_value) const {
    Value min = tf_op.getMin(), max = tf_op.getMax();
    if (auto min_id = min.getDefiningOp<TF::IdentityOp>()) {
      min = min_id.getInput();
    }
    if (auto max_id = max.getDefiningOp<TF::IdentityOp>()) {
      max = max_id.getInput();
    }

    if (!matchPattern(min, m_Constant(&min_value))) {
      return false;
    }

#define COMPOSITE_UTILS_TD

include "mlir/IR/PatternBase.td"

// See the function doc in the header file.
def GetNhwcReturnTypeFromNchw: NativeCodeCall<
  "GetNhwcReturnTypeFromNchw((*$0.begin()).getDefiningOp())">;

// When given a DenseIntElementsAttr containing I64 elements, this extracts
// one I32IntegerAttr from the given index.
class GetI32At<int index>: NativeCodeCall<

    auto assign_variable_op =
        dyn_cast_or_null<AssignVariableOp>(var_handle_user);
    if (!assign_variable_op) continue;
    auto value_op = assign_variable_op.getValue().getDefiningOp();
    auto dq_op = dyn_cast_or_null<DequantizeOp>(value_op);
    if (!dq_op || ref_qtype) continue;
    ref_qtype = dq_op.getInput().getType();
  }
  return ref_qtype;
}

class QuantizeVariablesPass

    auto batchInputDataset = op.getInputDataset();

    ParallelMapDatasetOp batchInputOp = dyn_cast_or_null<ParallelMapDatasetOp>(
        batchInputDataset.getDefiningOp());
    if (!batchInputOp) return failure();

    // The type of the `num_parallel_calls` argument in ParallelMapDataset
    // and MapAndBatchDataset is different (int32 and int64 respectively)

      return;
    }

    // Invariant:
    // isa<quantfork::QuantizeCastOp>(dq_arg.getDefiningOp()) -->
    // getdq_arg.getType() != q_op.getResult().getType()
    //
    // as otherwise qdq pair would have been optimized away.
    auto qd_arg_def_q_op =
        dyn_cast_or_null<quantfork::QuantizeCastOp>(dq_arg.getDefiningOp());
    if (!qd_arg_def_q_op) {
      return;
    }

    qd_arg_def_q_op.emitWarning()