resource_infos_[resource].potentially_written = true;
  auto* operation = resource.getDefiningOp();
  if (operation && isa<TF::VarHandleOp>(operation)) {
    mutable_variables_.insert(GetResourceKey(operation));
  }
}

bool ResourceAnalyzer::IsPotentiallyWritten(Value resource) const {
  assert(IsResource(resource));
  auto* operation = resource.getDefiningOp();
  if (operation && isa<TF::VarHandleOp>(operation))

    SmallVector<Value, 1> new_values;
    bool should_rewrite = false;
    for (auto operand : op.getArgs()) {
      if (auto corert_const_dense_tensor_op =
              operand.getDefiningOp<corert::ConstDenseTensorOp>()) {
        new_values.push_back(
            rewriter.create<fallback_async::ConstDenseTensorOp>(
                op.getLoc(), rewriter.getType<fallback::TFTensorType>(),

    Value assigned_value_operand = assign_variable_op.getOperand(1);

    if (auto var_handle_op =
            dyn_cast<TF::VarHandleOp>(resource_operand.getDefiningOp());
        var_handle_op &&
        isa<TF::ConstOp>(assigned_value_operand.getDefiningOp())) {
      var_handle_ops.emplace_back(var_handle_op);
    }
  }

  return var_handle_ops;
}

    Value pad_input = pad_op.getOperand();
    RankedTensorType pad_type = pad_op.getType().cast<RankedTensorType>();

    auto transpose_op = pad_input.getDefiningOp<stablehlo::TransposeOp>();
    if (!transpose_op || !transpose_op->hasOneUse()) return failure();
    Value transpose_input = transpose_op.getOperand();

    ArrayRef<int64_t> transpose_perm = transpose_op.getPermutation();

  LogicalResult match(mlir::stablehlo::TransposeOp op) const override {
    Value operand = op.getOperand();
    auto const_op =
        dyn_cast_or_null<mlir::stablehlo::ConstantOp>(operand.getDefiningOp());
    if (!const_op) return failure();

    // Only support float tensors.
    auto tensor_type = mlir::dyn_cast_or_null<TensorType>(const_op.getType());

          if (mlir::isa<BlockArgument>(operand)) continue;

          Operation* predecessor = operand.getDefiningOp();
          if (!operations->contains(predecessor) &&
              !ops_to_avoid.contains(predecessor)) {
            new_ops.insert(operand.getDefiningOp());
            operations->insert(operand.getDefiningOp());
          }
        }
      }
      if (successors) {

def FuseAffineOpAndMul : Pat<
  (TF_MulOp
    (SupportedAffineOpMatcher $conv_out, $input, $weight),
    (TF_ConstOp:$mul_rhs IsFloatElementsAttr:$mul_rhs_value)),
  (CloneOpWithReplacedOperands
        (GetDefiningOp $conv_out),
        $input,
        (MultiplyFakeQuantValue $weight,
          (MakeOneDimValueBroadcastable $mul_rhs, $weight))),
  [(HasOneUse $conv_out),
   (HasRankOf<1> $mul_rhs_value),

    auto var_handle_op =
        dyn_cast<TF::VarHandleOp>(resource_operand.getDefiningOp());
    if (!var_handle_op) continue;

    Value assigned_value_operand = assign_variable_op.getOperand(1);
    auto const_op =
        dyn_cast<TF::ConstOp>(assigned_value_operand.getDefiningOp());
    if (!const_op) continue;

    var_handle_ops.emplace_back(var_handle_op);
  }

  }

  // Check if the ConvOp's input is defined by `Expand` op, and the output used
  // by `Squeeze` op.
  Operation* producer_op = op.getOperand(0).getDefiningOp();
  if (!producer_op || producer_op->getNumResults() != 1) {
    return rewriter.notifyMatchFailure(
        op, "op doesn't have a producer node that has a single result");
  }
  if (!producer_op->hasOneUse() ||

              preceding_op)) {
        if (auto q_op = llvm::dyn_cast_or_null<quantfork::QuantizeCastOp>(
                dq_op.getArg().getDefiningOp())) {
          Operation* q_op_input = q_op.getArg().getDefiningOp();
          is_weight_constant =
              q_op_input && q_op_input->hasTrait<OpTrait::ConstantLike>();
        }
      }

      if (!is_weight_constant) {