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: Produces a list of most likely class labels as determined by the fine-tuned model.
  :param X: list or array of text to embed.
  :returns: list of class labels.
  
  Chunk idx for prediction.  Dividers at `step_size` increments.
  [  1  |  1  |  2  |  3  |  3  ]
docstr_tok: ["Produces","a","list","of","most","likely","class","labels","as","determined","by","the","fine-tuned","model",".",":","param","X",":","list","or","array","of","text","to","embed",".",":","returns",":","list","of","class","labels",".","Chunk","idx","for","prediction",".","Dividers","at","`","step_size","`","increments",".","[","1","|","1","|","2","|","3","|","3","]"]
code: def predict(self, X, context=None, **kwargs):
      """
      Produces a list of most likely class labels as determined by the fine-tuned model.
      :param X: list or array of text to embed.
      :returns: list of class labels.
  
      Chunk idx for prediction.  Dividers at `step_size` increments.
      [  1  |  1  |  2  |  3  |  3  ]
      """
      return super().predict(X, 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 4 for calling the Classifier obj's underlying member method code with example usage: obj.predict(X, context) and returns: super