code: def sigmoid_focal_loss(
      inputs: torch.Tensor,
      targets: torch.Tensor,
      alpha: float = -1,
      gamma: float = 2,
      reduction: str = "none",
  ) -> torch.Tensor:
      """
      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.
          reduction: 'none' | 'mean' | 'sum'
                   'none': No reduction will be applied to the output.
                   'mean': The output will be averaged.
                   'sum': The output will be summed.
      Returns:
          Loss tensor with the reduction option applied.
      """
      p = torch.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
  
label_desc: Use image node_id 1 for calling a global function with example usage: sigmoid_focal_loss(inputs, targets, alpha, gamma, reduction) and returns: loss