code: def __init__(
      self,
      k=None,
      ignore_index=IGNORE_INDEX_TOKEN_ID,
      reduction="elementwise_mean",
  ):
      self.k = k
      super().__init__(ignore_index, reduction)
  
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 1 to create a new SparsemaxLoss object from inherited base classes: _GenericLoss with example: obj = SparsemaxLoss(k, ignore_index, reduction)