class_docstr: Classifies a single document into 1 of N categories.
  :param config: A :py:class:`finetune.config.Settings` object or None (for default config).
  :param \**kwargs: key-value pairs of config items to override.
code: def explain(self, Xs, context=None):
      zipped_data = self.input_pipeline.zip_list_to_dict(
          X=Xs, context=context
      )
      explanation = self._inference(
          zipped_data,
          predict_keys=[PredictMode.EXPLAIN, PredictMode.NORMAL],
      )
      classes = self.input_pipeline.label_encoder.target_labels
      out = []
      bases = []
      preds = []
      for values in explanation:
          explain_sample = values[PredictMode.EXPLAIN]
          preds.append(values[PredictMode.NORMAL])
          out.append(explain_sample[1:])
          bases.append(explain_sample[0])
      processed = finetune_to_indico_explain(
          Xs, out, self.input_pipeline.text_encoder, attention=False
      )
  
      for base, sample, cls in zip(bases, processed, preds):
          weights = sample["explanation"]
          weights = np.array([base] + weights[:-1]) - weights
          n_classes = weights.shape[-1]
          norm = (
              np.max(
                  [np.abs(np.max(weights, 0)), abs(np.min(weights, 0))],
                  0,
              )
              * n_classes
          )
          explanation = weights / norm + 1 / n_classes
  
          sample["explanation"] = {
              c: explanation[:, i] for i, c in enumerate(classes)
          }
          sample[
              "prediction"
          ] = self.input_pipeline.label_encoder.inverse_transform(
              [cls]
          )[
              0
          ]
  
      return processed
  
imports: from warnings import warn
  import itertools
  import copy
  import tensorflow
  import tensorflow_addons
  import numpy
  from sklearn.utils import shuffle
  from finetune.base import BaseModel
  from finetune.encoding.target_encoders import OneHotLabelEncoder, NoisyLabelEncoder
  from finetune.nn.target_blocks import classifier
  from finetune.input_pipeline import BasePipeline
  from finetune.model import PredictMode
  from finetune.base_models.gpt.encoder import finetune_to_indico_explain
label_desc: Use image node_id 10 for calling the Classifier obj's underlying member method code with example usage: obj.explain(Xs, context) and returns: processed