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 probability distribution over classes for each example in X.
  :param X: list or array of text to embed.
  :returns: list of dictionaries.  Each dictionary maps from a class label to its assigned class probability.
docstr_tok: ["Produces","a","probability","distribution","over","classes","for","each","example","in","X.",":","param","X",":","list","or","array","of","text","to","embed",".",":","returns",":","list","of","dictionaries",".","Each","dictionary","maps","from","a","class","label","to","its","assigned","class","probability","."]
code: def _predict_proba(self, zipped_data, **kwargs):
      """
      Produces a probability distribution over classes for each example in X.
      :param X: list or array of text to embed.
      :returns: list of dictionaries.  Each dictionary maps from a class label to its assigned class probability.
      """
      # this is simply a vector of probabilities, not a dict from classes to class probs
      return self._predict(zipped_data, probas=True, **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 6 for calling the Classifier obj's underlying member method code with example usage: obj._predict_proba(zipped_data) and returns: self