code: def forward(self, x: torch.Tensor):
      self.layer_height, self.layer_width = x.shape[2], x.shape[3]
      self.stride = self.height // self.layer_height
      if self.training:
          batch_size = x.shape[0]
          grid_size = x.shape[2]
          bbox_attrs = 5 + self.num_classes
          num_anchors = len(self.anchors)
  
          # transform input shape
          self.layer_output = x.detach()
          self.pred = (
              x.view(
                  batch_size,
                  num_anchors,
                  bbox_attrs,
                  grid_size,
                  grid_size,
              )
              .permute(0, 1, 3, 4, 2)
              .contiguous()
          )
  
          self.layer_output = self.layer_output.view(
              batch_size,
              bbox_attrs * num_anchors,
              grid_size * grid_size,
          )
          self.layer_output = self.layer_output.transpose(
              1, 2
          ).contiguous()
          self.layer_output = self.layer_output.view(
              batch_size,
              grid_size * grid_size * num_anchors,
              bbox_attrs,
          )
  
      else:
          # transform the output of the network and scale it to match the
          # network dimension : 416x416
          self.layer_output = transform_prediction(
              x.data,
              self.width,
              self.anchors,
              self.num_classes,
              self.device,
          )
      return self.layer_output
  
imports: from typing import Union, List, Tuple, Optional
  import torch
  import torch.nn
  import numpy
  from .utils import transform_prediction
label_desc: Use image node_id 3 for calling the DetectionLayer obj's underlying member method code with example usage: obj.forward(x) and returns: self