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: 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.
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","."]
code: def predict(self, X, threshold=None, 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.
      """
      return super().predict(
          X, threshold=threshold, 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 5 for calling the MultiLabelClassifier obj's underlying member method code with example usage: obj.predict(X, threshold, context) and returns: super