class_docstr: Loss that is used for ListMLE.
  
  For each feature, ranking is performed independently. A lower score
  indicates a lower rank (i.e. "better" value).
class_docstr_tok: ["Loss","that","is","used","for","ListMLE",".","For","each","feature",",","ranking","is","performed","independently",".","A","lower","score","indicates","a","lower","rank","(","i.e",".","``","better","''","value",")","."]
docstr: Args:
      discount: The type of discount to apply. If discounting, higher-ranked values are more
          important.
docstr_tok: ["Args",":","discount",":","The","type","of","discount","to","apply",".","If","discounting",",","higher-ranked","values","are","more","important","."]
code: def __init__(
      self,
      discount: Optional[
          Literal["logarithmic", "linear", "quadratic"]
      ] = None,
  ):
      """
      Args:
          discount: The type of discount to apply. If discounting, higher-ranked values are more
              important.
      """
      super().__init__()
      self.discount = discount
  
imports: from typing import Literal, Optional
  import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new ListMLELoss object from inherited base classes: nn with example: obj = ListMLELoss(discount)