TfLiteStatus EvalHybridPerChannel(TfLiteContext* context, TfLiteNode* node,
                                  TfLiteDepthwiseConvParams* params,
                                  OpData* data, const TfLiteTensor* input,
                                  const TfLiteTensor* filter,
                                  const TfLiteTensor* bias,
                                  TfLiteTensor* output) {
  float output_activation_min, output_activation_max;
  CalculateActivationRange(params->activation, &output_activation_min,
                           &output_activation_max);
  const int batch_size = SizeOfDimension(input, 0);
  TF_LITE_ENSURE(context, batch_size != 0);
  const int input_size = NumElements(input) / batch_size;
  TfLiteTensor* input_quantized;
  TF_LITE_ENSURE_OK(context,
                    GetTemporarySafe(context, node, data->input_quantized_index,
                                     &input_quantized));
  int8_t* quantized_input_ptr_batch = input_quantized->data.int8;
  TfLiteTensor* scaling_factors_tensor;
  TF_LITE_ENSURE_OK(context,
                    GetTemporarySafe(context, node, data->scaling_factors_index,
                                     &scaling_factors_tensor));
  float* scaling_factors_ptr = GetTensorData<float>(scaling_factors_tensor);
  TfLiteTensor* input_offset_tensor;
  TF_LITE_ENSURE_OK(context,
                    GetTemporarySafe(context, node, data->input_offset_index,
                                     &input_offset_tensor));
  int32_t* input_offset_ptr = GetTensorData<int32_t>(input_offset_tensor);

  for (int b = 0; b < batch_size; ++b) {
    const int offset = b * input_size;
    tensor_utils::AsymmetricQuantizeFloats(
        GetTensorData<float>(input) + offset, input_size,
        quantized_input_ptr_batch + offset, &scaling_factors_ptr[b],
        &input_offset_ptr[b]);
  }

  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.depth_multiplier = params->depth_multiplier;

  op_params.weights_offset = 0;
  op_params.float_activation_min = output_activation_min;
  op_params.float_activation_max = output_activation_max;
  TF_LITE_ENSURE(context, filter->quantization.type != kTfLiteNoQuantization);
  const auto* affine_quantization =
      reinterpret_cast<TfLiteAffineQuantization*>(filter->quantization.params);
  if (kernel_type == kReference) {
    reference_integer_ops::DepthwiseConvHybridPerChannel(
        op_params, scaling_factors_ptr, GetTensorShape(input),
        quantized_input_ptr_batch, GetTensorShape(filter),
        GetTensorData<int8>(filter), GetTensorShape(bias),
        GetTensorData<float>(bias), GetTensorShape(output),
        GetTensorData<float>(output), affine_quantization->scale->data,
        input_offset_ptr);
  } else {
    optimized_integer_ops::DepthwiseConvHybridPerChannel(
        op_params, scaling_factors_ptr, GetTensorShape(input),
        quantized_input_ptr_batch, GetTensorShape(filter),
        GetTensorData<int8>(filter), GetTensorShape(bias),
        GetTensorData<float>(bias), GetTensorShape(output),
        GetTensorData<float>(output), affine_quantization->scale->data,
        input_offset_ptr, CpuBackendContext::GetFromContext(context));
  }

  return kTfLiteOk;
}
TfLiteStatus EvalHybridPerChannel(TfLiteContext* context, TfLiteNode* node,
                                  TfLiteDepthwiseConvParams* params,
                                  OpData* data, const TfLiteTensor* input,
                                  const TfLiteTensor* filter,
                                  const TfLiteTensor* bias,
                                  TfLiteTensor* output) {
  float output_activation_min, output_activation_max;
  CalculateActivationRange(params->activation, &output_activation_min,
                           &output_activation_max);
  const int batch_size = SizeOfDimension(input, 0);
  TF_LITE_ENSURE(context, batch_size != 0);
  const int input_size = NumElements(input) / batch_size;
  TfLiteTensor* input_quantized;
  TF_LITE_ENSURE_OK(context,
                    GetTemporarySafe(context, node, data->input_quantized_index,
                                     &input_quantized));
  int8_t* quantized_input_ptr_batch = input_quantized->data.int8;
  TfLiteTensor* scaling_factors_tensor;
  TF_LITE_ENSURE_OK(context,
                    GetTemporarySafe(context, node, data->scaling_factors_index,
                                     &scaling_factors_tensor));
  float* scaling_factors_ptr = GetTensorData<float>(scaling_factors_tensor);
  TfLiteTensor* input_offset_tensor;
  TF_LITE_ENSURE_OK(context,
                    GetTemporarySafe(context, node, data->input_offset_index,
                                     &input_offset_tensor));
  int32_t* input_offset_ptr = GetTensorData<int32_t>(input_offset_tensor);

  for (int b = 0; b < batch_size; ++b) {
    const int offset = b * input_size;
    tensor_utils::AsymmetricQuantizeFloats(
        GetTensorData<float>(input) + offset, input_size,
        quantized_input_ptr_batch + offset, &scaling_factors_ptr[b],
        &input_offset_ptr[b]);
  }

  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.depth_multiplier = params->depth_multiplier;

  op_params.weights_offset = 0;
  op_params.float_activation_min = output_activation_min;
  op_params.float_activation_max = output_activation_max;
  TF_LITE_ENSURE(context, filter->quantization.type != kTfLiteNoQuantization);
  const auto* affine_quantization =
      reinterpret_cast<TfLiteAffineQuantization*>(filter->quantization.params);
  if (kernel_type == kReference) {
    reference_integer_ops::DepthwiseConvHybridPerChannel(
        op_params, scaling_factors_ptr, GetTensorShape(input),
        quantized_input_ptr_batch, GetTensorShape(filter),
        GetTensorData<int8>(filter), GetTensorShape(bias),
        GetTensorData<float>(bias), GetTensorShape(output),
        GetTensorData<float>(output), affine_quantization->scale->data,
        input_offset_ptr);
  } else {
    optimized_integer_ops::DepthwiseConvHybridPerChannel(
        op_params, scaling_factors_ptr, GetTensorShape(input),
        quantized_input_ptr_batch, GetTensorShape(filter),
        GetTensorData<int8>(filter), GetTensorShape(bias),
        GetTensorData<float>(bias), GetTensorShape(output),
        GetTensorData<float>(output), affine_quantization->scale->data,
        input_offset_ptr, CpuBackendContext::GetFromContext(context));
  }

  return kTfLiteOk;
}