docstr: Predicts the probability of the positive class.
  
  Parameters
  ----------
  X : pd.DataFrame
      The data to make predictions on.
  s : pd.Series, optional
      The sensitive attribute.
  
  Returns
  -------
  pd.Series
      The predicted probabilities.
docstr_tok: ["Predicts","the","probability","of","the","positive","class",".","Parameters","--","--","--","--","--","X",":","pd.DataFrame","The","data","to","make","predictions","on",".","s",":","pd.Series",",","optional","The","sensitive","attribute",".","Returns","--","--","--","-","pd.Series","The","predicted","probabilities","."]
code: def predict_proba(
      self, X: pd.DataFrame, s: Optional[pd.Series] = None
  ) -> pd.Series:
      """Predicts the probability of the positive class.
  
      Parameters
      ----------
      X : pd.DataFrame
          The data to make predictions on.
      s : pd.Series, optional
          The sensitive attribute.
  
      Returns
      -------
      pd.Series
          The predicted probabilities.
      """
      super.predict_proba(X=X, s=s)
      self.model.eval()
      tensor_x = torch.tensor(X.values).float()
      preds = self.model(tensor_x)
      return pd.Series(
          preds.detach().to(torch.device("cpu")).numpy().flatten(),
          name="predictions",
          index=X.index,
      )
  
file_path: aequitas/src/aequitas/fairflow/methods/base_estimator/neural_network.py
imports: from typing import Optional
  import pandas
  import torch
  from torch import nn, optim
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from ...utils import create_logger, import_object
  from .base_estimator import BaseEstimator
label_desc: Use image node_id 3 for calling the NeuralNetwork obj's underlying member method code with example usage: obj.predict_proba(X, s) and returns: pd