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()

  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))

    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) {

  quant_spec.set_tensor_type(QuantizationComponentSpec::TENSORTYPE_INT_8);

  std::optional<TF::PartitionedCallOp> dequantize_op = ApplyUniformQuantization(
      pattern_rewriter, cast<TF::ConstOp>(value.getDefiningOp()), quant_spec);
  EXPECT_TRUE(dequantize_op.has_value());
  EXPECT_EQ(dequantize_op.value().func().getName().str(),
            "composite_dequantize_uniform");
}

}  // namespace