code: def compute_loss(loss_layers, targets, device="cpu"):
      nc = loss_layers[0].num_classes
      nl = len(loss_layers)
      # output at each layer
      predictions = [layer.pred for layer in loss_layers]
  
      # placeholders for the losses.
      lcls, lbox, lobj = (
          torch.zeros(1, device=device),
          torch.zeros(1, device=device),
          torch.zeros(1, device=device),
      )
  
      # Build yolo targets
      tcls, tbox, indices, anchors = build_targets(
          predictions, targets, loss_layers, device
      )  # targets
  
      BCEcls = nn.BCEWithLogitsLoss(
          pos_weight=torch.tensor([1.0], device=device)
      )
      BCEobj = nn.BCEWithLogitsLoss(
          pos_weight=torch.tensor([1.0], device=device)
      )
  
      balance = [4.0, 1.0, 0.4]
  
      # Calculate losses for each yolo layer
      for layer_index, layer_predictions in enumerate(predictions):
          # Get image ids, anchors, grid index i and j for each target in the current yolo layer
          b, anchor, grid_j, grid_i = indices[layer_index]
          # Build empty object target tensor with the same shape as the object prediction
          tobj = torch.zeros_like(
              layer_predictions[..., 0], device=device
          )  # target obj
          # Get the number of targets for this layer.
          # Each target is a label box with some scaling and the association of an anchor box.
          # Label boxes may be associated to 0 or multiple anchors. So they are multiple times or not at all in the targets.
          num_targets = b.shape[0]
          # Check if there are targets for this batch
          if num_targets:
              # Load the corresponding values from the predictions for each of the targets
              ps = layer_predictions[b, anchor, grid_j, grid_i]
  
              # Regression of the box
              # Apply sigmoid to xy offset predictions in each cell that has a target
              pxy = ps[:, :2].sigmoid() * 2 - 0.5
              # Apply exponent to wh predictions and multiply with the anchor box that matched best with the label for each cell that has a target
              pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[
                  layer_index
              ]
              # Build box out of xy and wh
              pbox = torch.cat((pxy, pwh), 1)
              # Calculate CIoU or GIoU for each target with the predicted box for its cell + anchor
              iou = bbox_iou(
                  pbox.T, tbox[layer_index], x1y1x2y2=False, CIoU=True
              )
              # We want to minimize our loss so we and the best possible IoU is 1 so we take 1 - IoU and reduce it with a mean
              lbox += (1.0 - iou).mean()  # iou loss
  
              # Classification of the objectness
              # Fill our empty object target tensor with the IoU we just calculated for each target at the targets position
              tobj[b, anchor, grid_j, grid_i] = (
                  iou.detach().clamp(0).type(tobj.dtype)
              )  # Use cells with iou > 0 as object targets
  
              # Classification of the class
              # Check if we need to do a classification (number of classes > 1)
              if nc > 1:
                  # Hot one class encoding
                  t = torch.full_like(
                      ps[:, 5:], 0.0, device=device
                  )  # targets
                  t[range(num_targets), tcls[layer_index]] = 1
                  # Use the tensor to calculate the BCE loss
                  lcls += BCEcls(ps[:, 5:], t)  # BCE
  
          # Classification of the objectness the sequel
          # Calculate the BCE loss between the on the fly generated target and the network prediction
          obji = BCEobj(layer_predictions[..., 4], tobj)  # obj loss
          lobj += obji * balance[layer_index]
  
      lbox *= 0.05
      lobj *= 1.0 * ((416 / 640) ** 2)  # scale to image size
      lcls *= 0.5 * (nc / 80)  # scale to classes
  
      # Merge losses
      loss = (lbox + lobj + lcls) * tobj.shape[0]
  
      return loss, (torch.cat((lbox, lobj, lcls))).detach()
  
imports: import math
  import torch
  import torch.nn
label_desc: Use image node_id 2 for calling a global function with example usage: compute_loss(loss_layers, targets, device) and returns: loss,  torch