//===----------------------------------------------------------------------===//
//

LogicalResult UniformQuantizeOp::verify() {
  UniformQuantizeOp op = *this;
  return VerifyScalesAndZeroPoints(op, op.getScales(), op.getZeroPoints(),
                                   op.getQuantizationAxis());
}

//===----------------------------------------------------------------------===//
// UniformRequantizeOp

  // so not passing it during conversion. This design might change if needed.
  ArrayRef<double> scales = qtype.getScales();
  ArrayRef<int64_t> zero_points = qtype.getZeroPoints();
  const int num_channels = scales.size();
  TensorType scale_type = RankedTensorType::get(
      {static_cast<int64_t>(num_channels)}, rewriter.getF32Type());

          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),

    element_type = quant::UniformQuantizedPerAxisType::get(
        per_axis_qtype.getFlags(), per_axis_qtype.getStorageType(),
        per_axis_qtype.getExpressedType(), per_axis_qtype.getScales(),
        per_axis_qtype.getZeroPoints(), quantized_dimension,
        per_axis_qtype.getStorageTypeMin(), per_axis_qtype.getStorageTypeMax());
  }

  TFL::TransposeOp::build(
      *builder, result,