code: def forward(self, image, boxes, box_ind):
      crops = torch.zeros_like(image)
      if image.is_cuda:
          _backend.crop_and_resize_gpu_forward(
              image,
              boxes,
              box_ind,
              self.extrapolation_value,
              self.crop_height,
              self.crop_width,
              crops,
          )
      else:
          _backend.crop_and_resize_forward(
              image,
              boxes,
              box_ind,
              self.extrapolation_value,
              self.crop_height,
              self.crop_width,
              crops,
          )
  
      # save for backward
      self.im_size = image.size()
      self.save_for_backward(boxes, box_ind)
  
      return crops
  
file_path: medicaldetectiontoolkit/cuda_functions/roi_align_2D/roi_align/crop_and_resize.py
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Function
  from ._ext import crop_and_resize
label_desc: Use image node_id 2 for calling the CropAndResizeFunction obj's underlying member method code with example usage: obj.forward(image, boxes, box_ind) and returns: crops