TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
                           TfLiteDepthwiseConvParams* params, OpData* data,
                           const TfLiteTensor* input,
                           const TfLiteTensor* filter, const TfLiteTensor* bias,
                           TfLiteTensor* output) {
  auto input_offset = -input->params.zero_point;
  auto filter_offset = -filter->params.zero_point;
  auto output_offset = output->params.zero_point;

  DepthwiseParams op_params;
  op_params.padding_type = PaddingType::kSame;
  op_params.padding_values.width = data->padding.width;
  op_params.padding_values.height = data->padding.height;
  op_params.stride_width = params->stride_width;
  op_params.stride_height = params->stride_height;
  op_params.dilation_width_factor = params->dilation_width_factor;
  op_params.dilation_height_factor = params->dilation_height_factor;
  op_params.input_offset = input_offset;
  op_params.weights_offset = filter_offset;
  op_params.output_offset = output_offset;
  op_params.output_multiplier = data->output_multiplier;
  op_params.output_shift = -data->output_shift;
  op_params.quantized_activation_min = data->output_activation_min;
  op_params.quantized_activation_max = data->output_activation_max;
  TF_LITE_ENSURE_STATUS(ComputeDepthMultiplier(context, input, filter,
                                               &op_params.depth_multiplier));
  if (kernel_type == kReference) {
    reference_ops::DepthwiseConv(
        op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
        GetTensorShape(filter), GetTensorData<uint8_t>(filter),
        GetTensorShape(bias), GetTensorData<int32_t>(bias),
        GetTensorShape(output), GetTensorData<uint8_t>(output));
  } else {
    optimized_ops::DepthwiseConv<uint8, int32>(
        op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
        GetTensorShape(filter), GetTensorData<uint8_t>(filter),
        GetTensorShape(bias), GetTensorData<int32_t>(bias),
        GetTensorShape(output), GetTensorData<uint8_t>(output),
        CpuBackendContext::GetFromContext(context));
  }
  return kTfLiteOk;
}
TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
                           TfLiteDepthwiseConvParams* params, OpData* data,
                           const TfLiteTensor* input,
                           const TfLiteTensor* filter, const TfLiteTensor* bias,
                           TfLiteTensor* output) {
  auto input_offset = -input->params.zero_point;
  auto filter_offset = -filter->params.zero_point;
  auto output_offset = output->params.zero_point;

  DepthwiseParams op_params;
  op_params.padding_type = PaddingType::kSame;
  op_params.padding_values.width = data->padding.width;
  op_params.padding_values.height = data->padding.height;
  op_params.stride_width = params->stride_width;
  op_params.stride_height = params->stride_height;
  op_params.dilation_width_factor = params->dilation_width_factor;
  op_params.dilation_height_factor = params->dilation_height_factor;
  op_params.input_offset = input_offset;
  op_params.weights_offset = filter_offset;
  op_params.output_offset = output_offset;
  op_params.output_multiplier = data->output_multiplier;
  op_params.output_shift = -data->output_shift;
  op_params.quantized_activation_min = data->output_activation_min;
  op_params.quantized_activation_max = data->output_activation_max;
  TF_LITE_ENSURE_STATUS(ComputeDepthMultiplier(context, input, filter,
                                               &op_params.depth_multiplier));
  if (kernel_type == kReference) {
    reference_ops::DepthwiseConv(
        op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
        GetTensorShape(filter), GetTensorData<uint8_t>(filter),
        GetTensorShape(bias), GetTensorData<int32_t>(bias),
        GetTensorShape(output), GetTensorData<uint8_t>(output));
  } else {
    optimized_ops::DepthwiseConv<uint8, int32>(
        op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
        GetTensorShape(filter), GetTensorData<uint8_t>(filter),
        GetTensorShape(bias), GetTensorData<int32_t>(bias),
        GetTensorShape(output), GetTensorData<uint8_t>(output),
        CpuBackendContext::GetFromContext(context));
  }
  return kTfLiteOk;
}