code: def dice_loss(inputs, targets, num_masks):
      """
      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 = F.sigmoid(inputs)
      inputs = paddle.flatten(inputs, 1)
      numerator = 2 * (inputs * targets).sum(-1)
      denominator = inputs.sum(-1) + targets.sum(-1)
      loss = 1 - (numerator + 1) / (denominator + 1)
      return loss.sum() / num_masks
  
imports: import copy
  import numpy
  from scipy.optimize import linear_sum_assignment
  import paddle
  import paddle.nn
  import paddle.distributed
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 1 for calling a global function with example usage: dice_loss(inputs, targets, num_masks) and returns: unknown