code: def batch_sigmoid_focal_loss(inputs, targets, alpha=0.25, gamma=2):
      """
      Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
      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).
          alpha: (optional) Weighting factor in range (0,1) to balance
                  positive vs negative examples. Default = -1 (no weighting).
          gamma: Exponent of the modulating factor (1 - p_t) to
                 balance easy vs hard examples.
      Returns:
          Loss tensor
      """
      hw = inputs.shape[1]
  
      prob = F.sigmoid(inputs)
      focal_pos = (
          (1 - prob) ** gamma
      ) * F.binary_cross_entropy_with_logits(
          inputs, paddle.ones_like(inputs), reduction="none"
      )
      focal_neg = (prob**gamma) * F.binary_cross_entropy_with_logits(
          inputs, paddle.zeros_like(inputs), reduction="none"
      )
      if alpha >= 0:
          focal_pos = focal_pos * alpha
          focal_neg = focal_neg * (1 - alpha)
  
      loss = paddle.einsum(
          "nc,mc->nm", focal_pos, targets
      ) + paddle.einsum("nc,mc->nm", focal_neg, (1 - targets))
  
      return loss / hw
  
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 4 for calling a global function with example usage: batch_sigmoid_focal_loss(inputs, targets, alpha, gamma) and returns: unknown