code: def forward(
      ctx,
      cls_output,
      cls_targets_at_level,
      num_positives_sum,
      num_real_classes,
      alpha,
      gamma,
      label_smoothing=0.0,
  ):
      loss, partial_grad = focal_loss_cuda.forward(
          cls_output,
          cls_targets_at_level,
          num_positives_sum,
          num_real_classes,
          alpha,
          gamma,
          label_smoothing,
      )
  
      ctx.save_for_backward(partial_grad, num_positives_sum)
      return loss
  
imports: import torch
  import focal_loss_cuda
label_desc: Use image node_id 1 for calling the FocalLoss obj's underlying member method code with example usage: obj.forward(ctx, cls_output, cls_targets_at_level, num_positives_sum, num_real_classes, alpha, gamma, label_smoothing) and returns: loss