// TODO(fengliuai): moving the following to an utility method.
  const llvm::fltSemantics& semantics = op.getMin().getSemantics();
  float rmin, rmax;
  if (&semantics == &APFloat::IEEEsingle()) {
    rmin = op.getMin().convertToFloat();
    rmax = op.getMax().convertToFloat();
  } else {
    rmin = op.getMin().convertToDouble();
    rmax = op.getMax().convertToDouble();
  }
  // Range boundaries must be valid.

  const int num_inputs_expected = is_packed ? 1 : num_partitions;

  if (!((replicated && !is_packed) || (op.getN() == num_inputs_expected))) {
    return op.emitOpError() << "expected " << num_inputs_expected
                            << " inputs, got " << op.getN();
  }

  return success();
}

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

        builder_.CreateVector<int64_t>(zero_points));
  } else if (auto qtype = mlir::dyn_cast<mlir::quant::CalibratedQuantizedType>(
                 element_type)) {
    std::vector<float> mins = {static_cast<float>(qtype.getMin())};
    std::vector<float> maxs = {static_cast<float>(qtype.getMax())};
    q_params = tflite::CreateQuantizationParameters(
        builder_, builder_.CreateVector<float>(mins),

    // The unpack & pack should have the same axis & num inputs/outputs.
    if (pack_op.getAxis() != input_unpack_op.getAxis() ||
        pack_op.getValuesCount() != input_unpack_op.getNum())
      return failure();

    const int total_pack_inputs = pack_op.getNumOperands();
    const int num_results = input_unpack_op.getNumResults();
    if (total_pack_inputs != num_results) return failure();