TfLiteStatus EvalFloat(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);

  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.float_activation_min = output_activation_min;
  op_params.float_activation_max = 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<float>(input),
        GetTensorShape(filter), GetTensorData<float>(filter),
        GetTensorShape(bias), GetTensorData<float>(bias),
        GetTensorShape(output), GetTensorData<float>(output));
  } else {
    optimized_ops::DepthwiseConv<float, float>(
        op_params, GetTensorShape(input), GetTensorData<float>(input),
        GetTensorShape(filter), GetTensorData<float>(filter),
        GetTensorShape(bias), GetTensorData<float>(bias),
        GetTensorShape(output), GetTensorData<float>(output),
        CpuBackendContext::GetFromContext(context));
  }
  return kTfLiteOk;
}
TfLiteStatus EvalFloat(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);

  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.float_activation_min = output_activation_min;
  op_params.float_activation_max = 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<float>(input),
        GetTensorShape(filter), GetTensorData<float>(filter),
        GetTensorShape(bias), GetTensorData<float>(bias),
        GetTensorShape(output), GetTensorData<float>(output));
  } else {
    optimized_ops::DepthwiseConv<float, float>(
        op_params, GetTensorShape(input), GetTensorData<float>(input),
        GetTensorShape(filter), GetTensorData<float>(filter),
        GetTensorShape(bias), GetTensorData<float>(bias),
        GetTensorShape(output), GetTensorData<float>(output),
        CpuBackendContext::GetFromContext(context));
  }
  return kTfLiteOk;
}