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","."]
code: def _predict_proba(self, zipped_data, **kwargs):
      return self._predict(
          zipped_data, threshold=None, probas=True, **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 7 for calling the MultiLabelClassifier obj's underlying member method code with example usage: obj._predict_proba(zipped_data) and returns: self