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()
        << " quantizer's output has another quantizer (" << q_op.getLoc()
        << ") as consumer - intentional?";
  });
}

// Merges consecutive QuantizeCast ops. For example, the following case:

        if (mlir::dyn_cast<DenseFPElementsAttr>(attr).size() >=
            quant_specs_.minimum_elements_for_weights) {
          continue;
        }

        visited_nonquantizable_ops_->insert(op);
        op->emitWarning(
            "Skipped quantization for UnidirectionalSequenceLSTMOp. Partial "
            "quantization of inputs for UnidirectionalSequenceLSTMOp is not "
            "supported. The operand ")

        // and once existing cases have been fixed.
        mlir::InFlightDiagnostic error =
            strict_clusters ? mlir::emitError(op.getLoc(), "")
                            : mlir::emitWarning(op.getLoc(), "");
        error << "Op has cyclic dependency with a compilation cluster:\n";
        error << "The cluster depends on\n";
        error << op.getName() << "\n"

                                              target_opset_);
    patterns.add<QuantizeAvgPoolOpPattern>(ctx);
  }
  if (failed(applyPatternsAndFoldGreedily(func, std::move(patterns)))) {
    func.emitWarning("Failed to converge pattern at QuantizePass.");
  }

  if (!shouldKeepUnusedQdqPattern()) {
    RewritePatternSet patterns_2(&getContext());
    patterns_2.add<RemoveUnusedQdqPattern>(ctx);