<< mlir::dyn_cast<DenseFPElementsAttr>(attr).size()
            << " elements which is fewer than the "
               "`minimum_elements_for_weights` threshold of "
            << quant_specs_.minimum_elements_for_weights;
        return false;
      }

      return true;
    }
    return false;
  }

    if (attr.size() < quant_specs_.minimum_elements_for_weights) {
      op->emitRemark("Quantization is skipped for ")
          << quantized_op->getName().getStringRef().str() << " because it has "
          << mlir::dyn_cast<DenseFPElementsAttr>(attr).size()
          << " elements which is fewer than the threshold("
          << quant_specs_.minimum_elements_for_weights << " elements).";
      return false;
    }

    const tflite::TensorType& inference_type,
    const absl::flat_hash_set<std::string>& denylisted_ops,
    const CustomOpMap& custom_op_map,
    int64_t minimum_elements_for_weights = 1024,
    bool disable_per_channel = false, bool weight_only_quantization = false,
    bool legacy_float_scale = false);

// Overloading methods to support old quantizer versions API

  // it is hard to get performance benefits from them with quantization. Used
  // in MLIR dynamic range quantizer with int8 weight data type.
  int64_t minimum_elements_for_weights = 1024;

  // Whether to calculate scales in float to keep quantized values the same with
  // old TOCO quantizer.
  bool legacy_float_scale = false;