// special case: just copy
  if (input_height == output_height && input_width == output_width) {
    std::memcpy(
        o_p,
        i_p,
        channels * input_height * input_width *
            sizeof(typename scalar_t::underlying));
    return;

            

Reported by FlawFinder.

                return qy;
}

TORCH_LIBRARY_IMPL(quantized, QuantizedCPU, m) {
  m.impl(TORCH_SELECTIVE_NAME("quantized::threshold"), TORCH_FN(threshold_quantized_cpu));
}

} // namespace native
} // namespace at

            

Reported by Cppcheck.

                }
};

TORCH_LIBRARY_IMPL(quantized, QuantizedCPU, m) {
  m.impl(TORCH_SELECTIVE_NAME("quantized::sigmoid"), TORCH_FN(QSigmoid::run));
}
} // namespace

}}  // namespace at::native

            

Reported by Cppcheck.

                }
};

TORCH_LIBRARY_IMPL(quantized, QuantizedCPU, m) {
  m.impl(TORCH_SELECTIVE_NAME("quantized::relu6"), TORCH_FN(QRelu6::run));
  m.impl(TORCH_SELECTIVE_NAME("quantized::leaky_relu"), TORCH_FN(QLeakyRelu::run));
}

} // namespace

            

Reported by Cppcheck.

                }
};

TORCH_LIBRARY_IMPL(quantized, QuantizedCPU, m) {
  m.impl(TORCH_SELECTIVE_NAME("quantized::max_pool1d"), TORCH_FN(QMaxPool_arr_args<1>::run));
  m.impl(TORCH_SELECTIVE_NAME("quantized::max_pool2d"), TORCH_FN(QMaxPool_arr_args<2>::run));
}

} // namespace

            

Reported by Cppcheck.

              }


TORCH_LIBRARY_IMPL(quantized, QuantizedCPU, m) {
  // TODO: this is kind of... blegh
  m.impl(TORCH_SELECTIVE_NAME("quantized::layer_norm"), [](
    Tensor input,
    std::vector<int64_t> normalized_shape,  // because IntArrayRef doesn't work
    c10::optional<Tensor> weight,

            

Reported by Cppcheck.

              uint8_t pytorch_u8rmax_ukernel__sse2(size_t n, const uint8_t* x) {
  assert(n != 0);

  if
    PYTORCH_QNNP_LIKELY(n >= 16) {
      __m128i vmax = _mm_setzero_si128();
      do {
        const __m128i vx = _mm_loadu_si128((const __m128i*)x);
        x += 16;

            

Reported by Cppcheck.

              uint8_t pytorch_u8rmax_ukernel__neon(size_t n, const uint8_t* x) {
  assert(n != 0);

  if
    PYTORCH_QNNP_LIKELY(n >= 16) {
      uint8x16_t vmax = vmovq_n_u8(0);
      do {
        const uint8x16_t vx = vld1q_u8(x);
        x += 16;

            

Reported by Cppcheck.

                  const union pytorch_qnnp_u8_clamping_params params[RESTRICT_STATIC 1]) {
  assert(n != 0);

  if
    PYTORCH_QNNP_LIKELY(n >= 8) {
      const __m128i voutput_max =
          _mm_load_si128((const __m128i*)&params->sse2.output_max);
      const __m128i voutput_min =
          _mm_load_si128((const __m128i*)&params->sse2.output_min);

            

Reported by Cppcheck.

                const uint8x16_t voutput_max = vld1q_dup_u8(&params->neon.output_max);
  const uint8x16_t voutput_min = vld1q_dup_u8(&params->neon.output_min);

  if
    PYTORCH_QNNP_LIKELY(n >= 8) {
      for (; n >= 64; n -= 64) {
        const uint8x16_t vx0 = vld1q_u8(x);
        x += 16;
        const uint8x16_t vx1 = vld1q_u8(x);

            

Reported by Cppcheck.