class_docstr: Classifies a single document into up to N of N categories.
  
  Implemented via a sum of N sigmoid losses applied a linear projection of the base model's output representation.
  
  :param config: A :py:class:`finetune.config.Settings` object or None (for default config).
  :param \**kwargs: key-value pairs of config items to override.
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docstr: :param X: list or array of text.
  :param Y: A list of lists containing labels for the corresponding X
  :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",":","A","list","of","lists","containing","labels","for","the","corresponding","X",":","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: A list of lists containing labels for the corresponding X
      :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: import tensorflow
  import numpy
  from finetune.base import BaseModel
  from finetune.encoding.target_encoders import MultilabelClassificationEncoder
  from finetune.nn.target_blocks import multi_classifier
  from finetune.util.imbalance import compute_class_weights
  from finetune.input_pipeline import BasePipeline
label_desc: Use image node_id 8 for calling the MultiLabelClassifier obj's underlying member method code with example usage: obj.finetune(X, Y, context) and returns: super