 
 template <typename Dtype>
 __global__ void StoPoolBackward(const int nthreads,
-    const Dtype* rand_idx, const Dtype* top_diff,
+    const Dtype* const rand_idx, const Dtype* const top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, Dtype* bottom_diff) {
+    const int stride_w, Dtype* const bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
-    int w = index % width;
-    int h = (index / width) % height;
-    int c = (index / width / height) % channels;
-    int n = index / width / height / channels;
-    int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
-    int phend = min(h / stride_h + 1, pooled_height);
-    int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
-    int pwend = min(w / stride_w + 1, pooled_width);
+    const int w = index % width;
+    const int h = (index / width) % height;
+    const int c = (index / width / height) % channels;
+    const int n = index / width / height / channels;
+    const int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
+    const int phend = min(h / stride_h + 1, pooled_height);
+    const int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
+    const int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
     const Dtype* const rand_idx_slice =
         rand_idx + (n * channels + c) * pooled_height * pooled_width;

}

template <typename Dtype>
__global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
    const int num, const int channels, const int height,
    const int width, const int pooled_height, const int pooled_width,
    const int kernel_h, const int kernel_w, const int stride_h,
    const int stride_w, const int pad_h, const int pad_w,
    Dtype* bottom_diff) {
  CUDA_KERNEL_LOOP(index, nthreads) {
    // find out the local index
    // find out the local offset
    int w = index % width + pad_w;
    int h = (index / width) % height + pad_h;
    int c = (index / width / height) % channels;
    int n = index / width / height / channels;
    int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
    int phend = min(h / stride_h + 1, pooled_height);
    int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
    int pwend = min(w / stride_w + 1, pooled_width);
    Dtype gradient = 0;
    const Dtype* const top_diff_slice =
        top_diff + (n * channels + c) * pooled_height * pooled_width;

}

template <typename Dtype>
__global__ void AvePoolForward(const int nthreads,
    const Dtype* const bottom_data, const int num, const int channels,
    const int height, const int width, const int pooled_height,
    const int pooled_width, const int kernel_h, const int kernel_w,
    const int stride_h, const int stride_w, const int pad_h, const int pad_w,
    Dtype* const top_data) {
  CUDA_KERNEL_LOOP(index, nthreads) {
    const int pw = index % pooled_width;
    const int ph = (index / pooled_width) % pooled_height;
    const int c = (index / pooled_width / pooled_height) % channels;
    const int n = index / pooled_width / pooled_height / channels;
    int hstart = ph * stride_h - pad_h;
    int wstart = pw * stride_w - pad_w;
    int hend = min(hstart + kernel_h, height + pad_h);
    int wend = min(wstart + kernel_w, width + pad_w);
    const int pool_size = (hend - hstart) * (wend - wstart);
    hstart = max(hstart, 0);
    wstart = max(wstart, 0);
    hend = min(hend, height);
