code: def sliced_forward(single_forward):
      def _pad(x, crop_size):
          h, w = x.size()[2:]
          pad_h = max(crop_size - h, 0)
          pad_w = max(crop_size - w, 0)
          x = F.pad(x, (0, pad_w, 0, pad_h))
          return x, pad_h, pad_w
  
      def wrapper(self, x):
          batch_size, _, ori_h, ori_w = x.size()
          if self.training and self.use_aux:
              outputs_all_scales = torch.zeros(
                  (batch_size, self.num_classes, ori_h, ori_w)
              ).cuda()
              aux_all_scales = torch.zeros(
                  (batch_size, self.num_classes, ori_h, ori_w)
              ).cuda()
              for s in self.scales:
                  new_size = (int(ori_h * s), int(ori_w * s))
                  scaled_x = F.interpolate(
                      x, size=new_size, mode="bilinear"
                  )
                  scaled_x = scaled_x.cuda()
                  scaled_h, scaled_w = scaled_x.size()[2:]
                  long_size = max(scaled_h, scaled_w)
                  print(scaled_x.size())
  
                  if long_size > self.crop_size:
                      count = torch.zeros((scaled_h, scaled_w))
                      outputs = torch.zeros(
                          (
                              batch_size,
                              self.num_classes,
                              scaled_h,
                              scaled_w,
                          )
                      ).cuda()
                      aux_outputs = torch.zeros(
                          (
                              batch_size,
                              self.num_classes,
                              scaled_h,
                              scaled_w,
                          )
                      ).cuda()
                      stride = int(
                          ceil(self.crop_size * self.stride_rate)
                      )
                      h_step_num = (
                          int(
                              ceil((scaled_h - self.crop_size) / stride)
                          )
                          + 1
                      )
                      w_step_num = (
                          int(
                              ceil((scaled_w - self.crop_size) / stride)
                          )
                          + 1
                      )
                      for yy in range(h_step_num):
                          for xx in range(w_step_num):
                              sy, sx = yy * stride, xx * stride
                              ey, ex = (
                                  sy + self.crop_size,
                                  sx + self.crop_size,
                              )
                              x_sub = scaled_x[:, :, sy:ey, sx:ex]
                              x_sub, pad_h, pad_w = _pad(
                                  x_sub, self.crop_size
                              )
                              print(x_sub.size())
                              outputs_sub, aux_sub = single_forward(
                                  self, x_sub
                              )
  
                              if sy + self.crop_size > scaled_h:
                                  outputs_sub = outputs_sub[
                                      :, :, :-pad_h, :
                                  ]
                                  aux_sub = aux_sub[:, :, :-pad_h, :]
  
                              if sx + self.crop_size > scaled_w:
                                  outputs_sub = outputs_sub[
                                      :, :, :, :-pad_w
                                  ]
                                  aux_sub = aux_sub[:, :, :, :-pad_w]
  
                              outputs[:, :, sy:ey, sx:ex] = outputs_sub
                              aux_outputs[:, :, sy:ey, sx:ex] = aux_sub
  
                              count[sy:ey, sx:ex] += 1
                      count = count.cuda()
                      outputs = outputs / count
                      aux_outputs = outputs / count
                  else:
                      scaled_x, pad_h, pad_w = _pad(
                          scaled_x, self.crop_size
                      )
                      outputs, aux_outputs = single_forward(
                          self, scaled_x
                      )
                      outputs = outputs[:, :, :-pad_h, :-pad_w]
                      aux_outputs = aux_outputs[:, :, :-pad_h, :-pad_w]
                  outputs_all_scales += outputs
                  aux_all_scales += aux_outputs
              return (
                  outputs_all_scales / len(self.scales),
                  aux_all_scales,
              )
          else:
              outputs_all_scales = torch.zeros(
                  (batch_size, self.num_classes, ori_h, ori_w)
              ).cuda()
              for s in self.scales:
                  new_size = (int(ori_h * s), int(ori_w * s))
                  scaled_x = F.interpolate(
                      x, size=new_size, mode="bilinear"
                  )
                  scaled_h, scaled_w = scaled_x.size()[2:]
                  long_size = max(scaled_h, scaled_w)
  
                  if long_size > self.crop_size:
                      count = torch.zeros((scaled_h, scaled_w))
                      outputs = torch.zeros(
                          (
                              batch_size,
                              self.num_classes,
                              scaled_h,
                              scaled_w,
                          )
                      ).cuda()
                      stride = int(
                          ceil(self.crop_size * self.stride_rate)
                      )
                      h_step_num = (
                          int(
                              ceil((scaled_h - self.crop_size) / stride)
                          )
                          + 1
                      )
                      w_step_num = (
                          int(
                              ceil((scaled_w - self.crop_size) / stride)
                          )
                          + 1
                      )
                      for yy in range(h_step_num):
                          for xx in range(w_step_num):
                              sy, sx = yy * stride, xx * stride
                              ey, ex = (
                                  sy + self.crop_size,
                                  sx + self.crop_size,
                              )
                              x_sub = scaled_x[:, :, sy:ey, sx:ex]
                              x_sub, pad_h, pad_w = _pad(
                                  x_sub, self.crop_size
                              )
  
                              outputs_sub = single_forward(self, x_sub)
  
                              if sy + self.crop_size > scaled_h:
                                  outputs_sub = outputs_sub[
                                      :, :, :-pad_h, :
                                  ]
  
                              if sx + self.crop_size > scaled_w:
                                  outputs_sub = outputs_sub[
                                      :, :, :, :-pad_w
                                  ]
  
                              outputs[:, :, sy:ey, sx:ex] = outputs_sub
  
                              count[sy:ey, sx:ex] += 1
                      count = count.cuda()
                      outputs = outputs / count
                  else:
                      scaled_x, pad_h, pad_w = _pad(
                          scaled_x, self.crop_size
                      )
                      outputs = single_forward(self, scaled_x)
                      outputs = outputs[:, :, :-pad_h, :-pad_w]
                  outputs_all_scales += outputs
              return outputs_all_scales
  
      return wrapper
  
imports: import os
  from math import ceil
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 6 for calling a global function with example usage: sliced_forward(single_forward) and returns: wrapper, x,  pad_h,  pad_w, unknown,  aux_all_scales, outputs_all_scales