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: :param X: list or array of text.
  :param Y: integer or string-valued class labels.
  :param batch_size: integer number of examples per batch. When N_GPUS > 1, this number
                     corresponds to the number of training examples provided to each GPU.
docstr_tok: [":","param","X",":","list","or","array","of","text",".",":","param","Y",":","integer","or","string-valued","class","labels",".",":","param","batch_size",":","integer","number","of","examples","per","batch",".","When","N_GPUS",">","1",",","this","number","corresponds","to","the","number","of","training","examples","provided","to","each","GPU","."]
code: def finetune(self, X, Y=None, context=None, **kwargs):
      """
      :param X: list or array of text.
      :param Y: integer or string-valued class labels.
      :param batch_size: integer number of examples per batch. When N_GPUS > 1, this number
                         corresponds to the number of training examples provided to each GPU.
      """
      return super().finetune(X, Y=Y, context=context, **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 7 for calling the Classifier obj's underlying member method code with example usage: obj.finetune(X, Y, context) and returns: super