class_docstr: Superclass for models used for the classification or agreement model.
  
  Attributes:
    aggregation: A string representing the way to aggregate two inputs provided
      in the function `_aggregate`. This is designed for the agreement model,
      where the source and target inputs need to be integrated. The allowed
      options are the following:
        None: no aggregation is performed.
        - `add`: the two inputs are added
        - `dist`: squared distance between the two inputs, elementwise.
        - `concat`: the two inputs are concatenated along axis=1.
        - `project_add`: the two inputs are first projected to a different space
          using a fully connected network with the hidden units provided in
          hidden_aggregation, and followed by the activation function in
          activation. Then the projected inputs are added, like in `add`.
        - `project_dist`: The two inputs are projected as in `project_add`,
          followed by a distance calculation like in `dist`.
        - `project_concat`: The two inputs are projected as in `project_add`,
          followed by a concatenation like in `concat`.
    hidden_aggregation: A tuple or list of integers representing the number of
      hidden units in each layer of the projection network described above.
    activation: An activation function to be applied to the outputs of each
      fully connected layer of the aggregation multilayer perceptron.
docstr: Convert predictions to probabilities.
docstr_tok: ["Convert","predictions","to","probabilities","."]
code: def normalize_predictions(self, predictions):
      """Convert predictions to probabilities."""
      pass
  
file_path: neural-structured-learning/research/gam/gam/models/models_base.py
imports: import abc
  import logging
  import numpy
  import tensorflow
label_desc: Use image node_id 4 for calling the Model obj's underlying member method code with example usage: obj.normalize_predictions(predictions) without return types