class_docstr: ```
  predicts graph node's classes
  ```
docstr: ```
  Performs inductive inference.
  If return_proba is True, returns probabilities of each class.
  ```
docstr_tok: ["``","`","Performs","inductive","inference",".","If","return_proba","is","True",",","returns","probabilities","of","each","class",".","``","`"]
code: def predict_inductive(self, df, G, return_proba=False, verbose=0):
      """
      ```
      Performs inductive inference.
      If return_proba is True, returns probabilities of each class.
      ```
      """
  
      gen = self.preproc.preprocess(df, G)
      gen.batch_size = self.batch_size
      # *_generator methods are deprecated from TF 2.1.0
      # preds = self.model.predict_generator(gen)
      preds = self.model.predict(gen, verbose=verbose)
      result = (
          preds
          if return_proba
          else [self.c[np.argmax(pred)] for pred in preds]
      )
      return result
  
file_path: ktrain/ktrain/graph/predictor.py
imports: from ..None import utils
  from ..imports import *
  from ..predictor import Predictor
  from .preprocessor import LinkPreprocessor, NodePreprocessor
label_desc: Use image node_id 5 for calling the NodePredictor obj's underlying member method code with example usage: obj.predict_inductive(df, G, return_proba, verbose) and returns: result