// Locate the argument position of the use.
        int argument_index = -1;
        for (int i = 0; i < consumer_call.getNumOperands(); ++i) {
          if (consumer_call.getOperand(i) == op->getResult(0)) {
            argument_index = i;
            break;
          }
        }

        // Copy the const into the consumer func and replace their usages.

    multiplier = isa<mhlo::ConstantOp>(bcast_or_const_op)
                     ? dyn_cast_or_null<mhlo::ConstantOp>(bcast_or_const_op)
                     : bcast_or_const_op->getOperand(0)
                           .getDefiningOp<mhlo::ConstantOp>();
    if (multiplier == nullptr) {
      return failure();
    }

    auto result_type = OpTrait::util::getBroadcastedType(filter.getType(),

      // This op is guaranteed to be a constant as ODS checks IsConstTensor.
      // Check if the number of elements meets the requirement.
      int current_num_elements =
          mlir::cast<ShapedType>(call_op.getOperand(idx).getType())
              .getNumElements();
      if (current_num_elements < min_num_elements_for_weights_) {
        call_op.emitRemark("Quantization is skipped for ")

  if (v.getDefiningOp()->getNumOperands() == 1) {
    // If the value is originating from an unary op, assume it's something
    // simple like "cast" and keep tracing.
    return traceUpwardsToArgument(v.getDefiningOp()->getOperand(0), seen, out);
  } else {
    // Typically a tf.VarHandle op.
    return v.getDefiningOp()->emitOpError("Non constant predecessor");
  }
}

      // Use rhs operand to detect types for dynamic range quantizable ops.
      Value value_for_deducing_op_type =
          (dyn_cast_or_null<DynamicRangeQuantizedOpInterface>(op))
              ? op->getOperand(1)
              : op->getResult(0);
      ShapedType value_shaped_type = mlir::dyn_cast_or_null<ShapedType>(
          value_for_deducing_op_type.getType());
      if (value_shaped_type != nullptr) {

  }

  int num_return_operands = terminator->getNumOperands();
  new_output_types.reserve(num_return_operands);
  for (int i = 0; i != num_return_operands; ++i) {
    auto returned_value = terminator->getOperand(i);
    Type returned_type = returned_value.getType();
    Operation* returned_op = returned_value.getDefiningOp();
    if (returned_op && llvm::isa<DequantizeOp>(returned_op)) {

bool GetValueAsConstant(Value val, AttrT &attr) {
  while (auto result = mlir::dyn_cast<OpResult>(val)) {
    Operation *op = result.getOwner();
    if (!isa<IdentityOp>(op) && !isa<IdentityNOp>(op)) break;
    val = op->getOperand(result.getResultNumber());
  }
  return matchPattern(val, m_Constant(&attr));
}

// Checks if both compilation and replication attributes are present in the
// operation, and if their values are valid.

    quant::AccumulatorScaleFunc func) {
  std::vector<quant::QuantizedType> non_bias_types;
  non_bias_types.reserve(non_biases.size());
  for (int non_bias : non_biases) {
    Operation *non_bias_define = op->getOperand(non_bias).getDefiningOp();
    if (auto dequant = llvm::dyn_cast<TFL::DequantizeOp>(non_bias_define)) {
      auto non_bias_type = mlir::cast<TensorType>(dequant.getInput().getType());
      auto non_bias_ele_type =

TorchAvgPoolData GetTorchAvgPoolData(CompositeOp op) {
  auto composite_attrs = op.getCompositeAttributes();
  TorchAvgPoolData data;

  auto op_type = mlir::cast<RankedTensorType>(op.getOperand(0).getType());

  data.n = op_type.getShape()[0];
  data.c = op_type.getShape()[1];
  data.h_in = op_type.getShape()[2];
  data.w_in = op_type.getShape()[3];

  std::vector<int32_t> kernel_size;