code: def backward(cls, ctx, grad_output):
      (p_star,) = ctx.saved_tensors
      grad = grad_output.unsqueeze(1) * p_star
      ret = (grad,)
  
      # pad with as many Nones as needed
      return ret + (None,) * (1 + cls.n_fwd_args)
  
imports: import torch
  import torch.nn
  from torch.autograd import Function
  from ludwig.constants import IGNORE_INDEX_TOKEN_ID
  from ludwig.utils.entmax.activations import entmax15, sparsemax
  from ludwig.utils.entmax.root_finding import entmax_bisect, sparsemax_bisect
label_desc: Use image node_id 2 for calling the _GenericLossFunction obj's underlying member method code with example usage: obj.backward(cls, ctx, grad_output) and returns: unknown