  * matrix vector product like: y <- Ax + beta*y
  */
 TH_API void THCudaTensor_conv2Dmap(THCudaTensor *output, THCudaTensor *input,
-                                  THCudaTensor *kernel, long stride_x, long stride_y
-                                   , THCudaTensor *table, long fanin)
+                                   THCudaTensor *kernel, long stride_x, long stride_y,
+                                   THCudaTensor *table, long fanin)
 {
   long nInputPlane, nInputRows, nInputCols;
   long nKernelRows, nKernelCols;

              float *input_p = input + ((long)table[ii]-1)*input_h*input_w 
                + yy*stride_h*input_w + xx*stride_w;
              float *output_p = output + oo*output_h*output_w + yy*output_w + xx;
              float *kernel_p = kernel + ((long)table[ii + 1]-1) *kernel_w*kernel_h + koffset;
              float sum = 0;
              if (swapkernel) {
                for(ky = 0; ky < kernel_h; ky++) {

        }
      }
    }

  } else { // not enough shared mem for kernels, simply stream them

    // convolution loop
    for(oo = oo_start; oo < oo_end; oo++) {
      for (ii = table_start; ii < table_end; ii = ii + 2) {
        for(yy = yy_start; yy < yy_end; yy+=yy_step) {
          for(xx = xx_start; xx < xx_end; xx+=xx_step) {
            // Dot product in two dimensions... (between input image and the mask)
            float *input_p = input + ((long)table[ii]-1)*input_h*input_w 
              + yy*stride_h*input_w + xx*stride_w;
            float *output_p = output + oo*output_h*output_w + yy*output_w + xx;
            float *kernel_p = kernel + ((long)table[ii + 1]-1) *kernel_w*kernel_h + koffset;
            float sum = 0;
            if (swapkernel) {
              for(ky = 0; ky < kernel_h; ky++) {
#pragma unroll 5
                for(kx = 0; kx < kernel_w; kx++) {
                  sum += input_p[kx]*(*kernel_p--);
                }
                input_p += input_w;
              }
            } else {
              for(ky = 0; ky < kernel_h; ky++) {
#pragma unroll 5
                for(kx = 0; kx < kernel_w; kx++) {
                  sum += input_p[kx]*(*kernel_p++);
                }
                input_p += input_w;
              }
            }
            *output_p += sum;
          }
        }
      }
    }
  }
}


/*
