code: def sigmoid_focal_loss(
      inputs, targets, alpha, gamma, reduction="sum"
  ):
      assert reduction in [
          "sum",
          "mean",
      ], f"do not support this {reduction} reduction?"
  
      p = F.sigmoid(inputs)
      ce_loss = F.binary_cross_entropy_with_logits(
          inputs, targets, reduction="none"
      )
      p_t = p * targets + (1 - p) * (1 - targets)
      loss = ce_loss * ((1 - p_t) ** gamma)
  
      if alpha >= 0:
          alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
          loss = alpha_t * loss
  
      if reduction == "mean":
          loss = loss.mean()
      elif reduction == "sum":
          loss = loss.sum()
  
      return loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from scipy.optimize import linear_sum_assignment
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.metric import accuracy
  from ppdet.core.workspace import register
  from ppdet.modeling.losses.iou_loss import GIoULoss
label_desc: Use image node_id 4 for calling a global function with example usage: sigmoid_focal_loss(inputs, targets, alpha, gamma, reduction) and returns: loss