inline void DepthwiseConv(const uint8* input_data, const Dims<4>& input_dims,
                          int32 input_offset, const uint8* filter_data,
                          const Dims<4>& filter_dims, int32 filter_offset,
                          const int32* bias_data, const Dims<4>& bias_dims,
                          int stride_width, int stride_height,
                          int dilation_width_factor, int dilation_height_factor,
                          int pad_width, int pad_height, int depth_multiplier,
                          int32 output_offset, int32 output_multiplier,
                          int output_shift, int32 output_activation_min,
                          int32 output_activation_max, uint8* output_data,
                          const Dims<4>& output_dims) {
  tflite::DepthwiseParams op_params;
  // Padding type is ignored, but still set.
  op_params.padding_type = PaddingType::kSame;
  op_params.padding_values.width = pad_width;
  op_params.padding_values.height = pad_height;
  op_params.stride_width = stride_width;
  op_params.stride_height = stride_height;
  op_params.dilation_width_factor = dilation_width_factor;
  op_params.dilation_height_factor = dilation_height_factor;
  op_params.depth_multiplier = depth_multiplier;
  op_params.quantized_activation_min = output_activation_min;
  op_params.quantized_activation_max = output_activation_max;
  op_params.input_offset = input_offset;
  op_params.weights_offset = filter_offset;
  op_params.output_offset = output_offset;
  op_params.output_multiplier = output_multiplier;
  // Legacy ops used mixed left and right shifts. Now all are +ve-means-left.
  op_params.output_shift = kDepthwiseReverseShift * output_shift;

  DepthwiseConv(op_params, DimsToShape(input_dims), input_data,
                DimsToShape(filter_dims), filter_data, DimsToShape(bias_dims),
                bias_data, DimsToShape(output_dims), output_data);
}
inline void DepthwiseConv(const uint8* input_data, const Dims<4>& input_dims,
                          int32 input_offset, const uint8* filter_data,
                          const Dims<4>& filter_dims, int32 filter_offset,
                          const int32* bias_data, const Dims<4>& bias_dims,
                          int stride_width, int stride_height,
                          int dilation_width_factor, int dilation_height_factor,
                          int pad_width, int pad_height, int depth_multiplier,
                          int32 output_offset, int32 output_multiplier,
                          int output_shift, int32 output_activation_min,
                          int32 output_activation_max, uint8* output_data,
                          const Dims<4>& output_dims) {
  tflite::DepthwiseParams op_params;
   
  op_params.padding_type = PaddingType::kSame;
  op_params.padding_values.width = pad_width;
  op_params.padding_values.height = pad_height;
  op_params.stride_width = stride_width;
  op_params.stride_height = stride_height;
  op_params.dilation_width_factor = dilation_width_factor;
  op_params.dilation_height_factor = dilation_height_factor;
  op_params.depth_multiplier = depth_multiplier;
  op_params.quantized_activation_min = output_activation_min;
  op_params.quantized_activation_max = output_activation_max;
  op_params.input_offset = input_offset;
  op_params.weights_offset = filter_offset;
  op_params.output_offset = output_offset;
  op_params.output_multiplier = output_multiplier;
   
  op_params.output_shift = kDepthwiseReverseShift * output_shift;

  DepthwiseConv(op_params, DimsToShape(input_dims), input_data,
                DimsToShape(filter_dims), filter_data, DimsToShape(bias_dims),
                bias_data, DimsToShape(output_dims), output_data);
}