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.
docstr: Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
  :param X: list or array of text to embed.
  :returns: np.array of features of shape (n_examples, embedding_size).
docstr_tok: ["Embeds","inputs","in","learned","feature","space",".","Can","be","called","before","or","after","calling",":","meth",":","`","finetune","`",".",":","param","X",":","list","or","array","of","text","to","embed",".",":","returns",":","np.array","of","features","of","shape","(","n_examples",",","embedding_size",")","."]
code: def featurize(self, X, **kwargs):
      """
      Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
      :param X: list or array of text to embed.
      :returns: np.array of features of shape (n_examples, embedding_size).
      """
      return super().featurize(X, **kwargs)
  
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 3 for calling the Classifier obj's underlying member method code with example usage: obj.featurize(X) and returns: super