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.
class_docstr_tok: ["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","."]
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: 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 4 for calling the MultiLabelClassifier obj's underlying member method code with example usage: obj.featurize(X) and returns: super