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 _target_model(
      self,
      *,
      config,
      featurizer_state,
      targets,
      n_outputs,
      train=False,
      reuse=None,
      **kwargs,
  ):
      if "explain_out" in featurizer_state:
          shape = tf.shape(
              input=featurizer_state["explain_out"]
          )  # batch, seq, hidden
          flat_explain = tf.reshape(
              featurizer_state["explain_out"],
              [shape[0] * shape[1], shape[2]],
          )
          hidden = tf.concat(
              (featurizer_state["features"], flat_explain), 0
          )
      else:
          hidden = featurizer_state["features"]
  
      clf_out = classifier(
          hidden=hidden,
          targets=targets,
          n_targets=n_outputs,
          config=config,
          train=train,
          reuse=reuse,
          **kwargs,
      )
      if "explain_out" in featurizer_state:
          logits = clf_out["logits"]
          clf_out["logits"] = logits[: shape[0]]
          clf_out["explanation"] = tf.nn.softmax(
              tf.reshape(
                  logits[shape[0] :],
                  tf.concat(
                      (shape[:2], [tf.shape(input=logits)[-1]]), 0
                  ),
              ),
              -1,
          )
      return clf_out
  
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 9 for calling the Classifier obj's underlying member method code with example usage: obj._target_model() and returns: clf_out