code: def dice_loss(inputs, targets, num_boxes):
      """
      Compute the DICE loss, similar to generalized IOU for masks
      Args:
          inputs: A float tensor of arbitrary shape.
                  The predictions for each example.
          targets: A float tensor with the same shape as inputs. Stores the binary
                   classification label for each element in inputs
                  (0 for the negative class and 1 for the positive class).
      """
      inputs = inputs.sigmoid()
      inputs = inputs.flatten(1)
      numerator = 2 * (inputs * targets).sum(1)
      denominator = inputs.sum(-1) + targets.sum(-1)
      loss = 1 - (numerator + 1) / (denominator + 1)
      return loss.sum() / num_boxes
  
imports: import io
  from collections import defaultdict
  from typing import List, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from PIL import Image
  import util.box_ops
  from util.misc import NestedTensor, interpolate, nested_tensor_from_tensor_list
label_desc: Use image node_id 2 for calling a global function with example usage: dice_loss(inputs, targets, num_boxes) and returns: unknown