code: def backward(ctx, grad_loss):
      partial_grad, num_positives_sum = ctx.saved_tensors
  
      # The backward kernel is actually in-place to save memory space,
      # partial_grad and grad_input are the same tensor.
      grad_input = focal_loss_cuda.backward(
          grad_loss, partial_grad, num_positives_sum
      )
  
      return grad_input, None, None, None, None, None, None
  
imports: import torch
  import focal_loss_cuda
label_desc: Use image node_id 2 for calling the FocalLoss obj's underlying member method code with example usage: obj.backward(ctx, grad_loss) and returns: grad_input,  None,  None,  None,  None,  None,  None