class_docstr: Converts pairwise simmilarity scores into class label
  
  Args:
      confidence_threshold: used to determine whether example belongs to one 
                            of the classes in 'y_support' or not
      pooling: strategy for averaging similarity scores for each label
      is_binary: determines whether the similarity is a number or a probability vector
code: def __init__(
      self,
      confidence_threshold: float = 0.0,
      pooling: str = "max",
      is_binary: bool = True,
      **kwargs,
  ) -> None:
      self.confidence_threshold = confidence_threshold
      self.pooling = pooling
      self.is_binary = is_binary
  
file_path: DeepPavlov/deeppavlov/models/classifiers/dnnc_proba2labels.py
imports: from logging import getLogger
  from typing import List
  import numpy
  from deeppavlov.core.common.registry import register
  from deeppavlov.core.models.component import Component
label_desc: Use image node_id 1 to create a new Proba2Labels object from inherited base classes: Component with example: obj = Proba2Labels(confidence_threshold, pooling, is_binary)