class_docstr: Interpretation methods for classification models.
code: def __init__(
      self,
      learn: Learner,
      dl: DataLoader,  # `DataLoader` to run inference over
      losses: TensorBase,  # Losses calculated from `dl`
      act=None,  # Activation function for prediction
  ):
      super().__init__(learn, dl, losses, act)
      self.vocab = self.dl.vocab
      if is_listy(self.vocab):
          self.vocab = self.vocab[-1]
  
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 1 to create a new ClassificationInterpretation object from inherited base classes: Interpretation with example: obj = ClassificationInterpretation(learn, dl, losses, act)