class_docstr: Interpretation base class, can be inherited for task specific Interpretation classes
docstr: `k` largest(/smallest) losses and indexes, defaulting to all losses.
docstr_tok: ["`","k","`","largest","(","\/smallest",")","losses","and","indexes",",","defaulting","to","all","losses","."]
code: def top_losses(
      self,
      k: int | None = None,  # Return `k` losses, defaults to all
      largest: bool = True,  # Sort losses by largest or smallest
      items: bool = False,  # Whether to return input items
  ):
      "`k` largest(/smallest) losses and indexes, defaulting to all losses."
      losses, idx = self.losses.topk(
          ifnone(k, len(self.losses)), largest=largest
      )
      if items:
          return (
              losses,
              idx,
              getattr(self.dl.items, "iloc", L(self.dl.items))[idx],
          )
      else:
          return losses, idx
  
imports: from __future__ import annotations
  from .data.all import *
  from .optimizer import *
  from .learner import *
  from .tabular.core import *
  import sklearn.metrics
label_desc: Use image node_id 4 for calling the Interpretation obj's underlying member method code with example usage: obj.top_losses(k, largest, items) and returns: losses,  idx,  getattr, losses,  idx