PatternRewriter &rewriter) const override {
    auto output_type = GetUniformQuantizedType(
        op, op.getOutput().getType(), op.getScales(), op.getZeroPoints(),
        /*expressed_type=*/rewriter.getF32Type(), op.getQuantizationMinVal(),
        op.getQuantizationMaxVal(), op.getQuantizationAxis(), rewriter);
    if (failed(output_type)) {
      return failure();
    }

    return uniform_type.getZeroPoint() == 0;
  } else if (auto per_axis_type =
                 llvm::dyn_cast_or_null<UniformQuantizedPerAxisType>(qtype)) {
    return absl::c_all_of(per_axis_type.getZeroPoints(),
                          [](int64_t x) { return x == 0; });
  }
  return false;
}

// Multiplies two 1D arrays with broadcasting support.
template <typename T>

          new_qtype = mlir::quant::UniformQuantizedPerAxisType::get(
              per_axis.getFlags(), per_axis.getStorageType(),
              per_axis.getExpressedType(), per_axis.getScales(),
              per_axis.getZeroPoints(), per_axis.getQuantizedDimension(),
              per_axis.getStorageTypeMin() - 1, per_axis.getStorageTypeMax());
        } else if (auto per_tensor =

    std::vector<float> scales(qtype.getScales().begin(),
                              qtype.getScales().end());
    std::vector<int64_t> zero_points(qtype.getZeroPoints().begin(),
                                     qtype.getZeroPoints().end());
    q_params = tflite::CreateQuantizationParameters(
        builder_, /*min=*/0, /*max=*/0, builder_.CreateVector<float>(scales),