inline void HybridConv(const ConvParams& params, float* scaling_factors_ptr,
                       const RuntimeShape& input_shape,
                       const int8_t* input_data,
                       const RuntimeShape& filter_shape,
                       const int8_t* filter_data,
                       const RuntimeShape& bias_shape, const float* bias_data,
                       const RuntimeShape& accum_scratch_shape,
                       int32_t* accum_scratch, const RuntimeShape& output_shape,
                       float* output_data, const RuntimeShape& im2col_shape,
                       int8_t* im2col_data, CpuBackendContext* context) {
  const int stride_width = params.stride_width;
  const int stride_height = params.stride_height;
  const int dilation_width_factor = params.dilation_width_factor;
  const int dilation_height_factor = params.dilation_height_factor;
  const float output_activation_min = params.float_activation_min;
  const float output_activation_max = params.float_activation_max;
  TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
  TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
  TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);

  const int batch_size = input_shape.Dims(0);
  const int filter_width = filter_shape.Dims(2);
  const int filter_height = filter_shape.Dims(1);

  const int input_zero_point = 0;
  const int8_t* gemm_input_data = nullptr;
  int num_input;
  const bool need_dilated_im2col =
      dilation_width_factor != 1 || dilation_height_factor != 1;
  const bool need_im2col = stride_width != 1 || stride_height != 1 ||
                           filter_width != 1 || filter_height != 1;

  if (need_dilated_im2col) {
    DilatedIm2col(params, input_zero_point, input_shape, input_data,
                  filter_shape, output_shape, im2col_data);
    gemm_input_data = im2col_data;
    num_input = im2col_shape.FlatSize();
  } else if (need_im2col) {
    TFLITE_DCHECK(im2col_data);
    // symmetric quantization assumes zero point of 0.

    Im2col(params, filter_height, filter_width, input_zero_point, input_shape,
           input_data, im2col_shape, im2col_data);
    gemm_input_data = im2col_data;
    num_input = im2col_shape.FlatSize();
  } else {
    TFLITE_DCHECK(!im2col_data);
    gemm_input_data = input_data;
    num_input = input_shape.FlatSize();
  }

  // Flatten 4D matrices into 2D matrices for matrix multiplication.

  // Flatten so that each filter has its own row.
  const int filter_rows = filter_shape.Dims(0);
  const int filter_cols = FlatSizeSkipDim(filter_shape, 0);

  // In MatrixBatchVectorMultiplyAccumulate, each output value is the
  // dot product of one row of the first matrix with one row of the second
  // matrix. Therefore, the number of cols in each matrix are equivalent.
  //
  // After Im2Col, each input patch becomes a row.
  const int gemm_input_cols = filter_cols;
  const int gemm_input_rows = num_input / gemm_input_cols;

  const int output_cols = output_shape.Dims(3);
  const int output_rows = FlatSizeSkipDim(output_shape, 3);
  TFLITE_DCHECK_EQ(output_cols, filter_rows);
  TFLITE_DCHECK_EQ(output_rows, gemm_input_rows);
  TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_cols);

  // MatrixBatchVectorMultiplyAccumulate assumes that each row of the second
  // input matrix has its own scale factor. This code duplicates the scale
  // factors for each row in the same batch.
  const int rows_per_batch = gemm_input_rows / batch_size;
  for (int i = gemm_input_rows - 1; i >= 0; --i) {
    scaling_factors_ptr[i] = scaling_factors_ptr[i / rows_per_batch];
  }

  std::fill_n(output_data, output_rows * output_cols, 0.0f);

  // The scratch buffer must have the same size as the output.
  TFLITE_DCHECK_EQ(accum_scratch_shape.FlatSize(), output_shape.FlatSize());
  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
      filter_data, filter_rows, filter_cols, gemm_input_data,
      scaling_factors_ptr, /*n_batch=*/gemm_input_rows, accum_scratch,
      output_data, context);
  AddBiasAndEvalActivationFunction(output_activation_min, output_activation_max,
                                   bias_shape, bias_data, output_shape,
                                   output_data);
}
inline void HybridConv(const ConvParams& params, float* scaling_factors_ptr,
                       const RuntimeShape& input_shape,
                       const int8_t* input_data,
                       const RuntimeShape& filter_shape,
                       const int8_t* filter_data,
                       const RuntimeShape& bias_shape, const float* bias_data,
                       const RuntimeShape& accum_scratch_shape,
                       int32_t* accum_scratch, const RuntimeShape& output_shape,
                       float* output_data, const RuntimeShape& im2col_shape,
                       int8_t* im2col_data, CpuBackendContext* context) {
  const int stride_width = params.stride_width;
  const int stride_height = params.stride_height;
  const int dilation_width_factor = params.dilation_width_factor;
  const int dilation_height_factor = params.dilation_height_factor;
  const float output_activation_min = params.float_activation_min;
  const float output_activation_max = params.float_activation_max;
  TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
  TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
  TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);

  const int batch_size = input_shape.Dims(0);
  const int filter_width = filter_shape.Dims(2);
  const int filter_height = filter_shape.Dims(1);

  const int input_zero_point = 0;
  const int8_t* gemm_input_data = nullptr;
  int num_input;
  const bool need_dilated_im2col =
      dilation_width_factor != 1 || dilation_height_factor != 1;
  const bool need_im2col = stride_width != 1 || stride_height != 1 ||
                           filter_width != 1 || filter_height != 1;

  if (need_dilated_im2col) {
    DilatedIm2col(params, input_zero_point, input_shape, input_data,
                  filter_shape, output_shape, im2col_data);
    gemm_input_data = im2col_data;
    num_input = im2col_shape.FlatSize();
  } else if (need_im2col) {
    TFLITE_DCHECK(im2col_data);
     

    Im2col(params, filter_height, filter_width, input_zero_point, input_shape,
           input_data, im2col_shape, im2col_data);
    gemm_input_data = im2col_data;
    num_input = im2col_shape.FlatSize();
  } else {
    TFLITE_DCHECK(!im2col_data);
    gemm_input_data = input_data;
    num_input = input_shape.FlatSize();
  }

   

   
  const int filter_rows = filter_shape.Dims(0);
  const int filter_cols = FlatSizeSkipDim(filter_shape, 0);

   
   
   
   
   
  const int gemm_input_cols = filter_cols;
  const int gemm_input_rows = num_input / gemm_input_cols;

  const int output_cols = output_shape.Dims(3);
  const int output_rows = FlatSizeSkipDim(output_shape, 3);
  TFLITE_DCHECK_EQ(output_cols, filter_rows);
  TFLITE_DCHECK_EQ(output_rows, gemm_input_rows);
  TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_cols);

   
   
   
  const int rows_per_batch = gemm_input_rows / batch_size;
  for (int i = gemm_input_rows - 1; i >= 0; --i) {
    scaling_factors_ptr[i] = scaling_factors_ptr[i / rows_per_batch];
  }

  std::fill_n(output_data, output_rows * output_cols, 0.0f);

   
  TFLITE_DCHECK_EQ(accum_scratch_shape.FlatSize(), output_shape.FlatSize());
  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
      filter_data, filter_rows, filter_cols, gemm_input_data,
      scaling_factors_ptr,  gemm_input_rows, accum_scratch,
      output_data, context);
  AddBiasAndEvalActivationFunction(output_activation_min, output_activation_max,
                                   bias_shape, bias_data, output_shape,
                                   output_data);
}