code: def test_predict_on_dataset_generator(self):
      for replicate in [True, False]:
          wrapper = BaalTransformersTrainer(
              model=self.model,
              args=self.args,
              replicate_in_memory=replicate,
              train_dataset=self.dataset,
              eval_dataset=self.dataset,
              tokenizer=None,
          )
          # iteration == 1
          pred = wrapper.predict_on_dataset_generator(
              self.dataset, 1, False
          )
          assert next(pred).shape == (5, 100, 10, 1)
  
          # iterations > 1
          pred = wrapper.predict_on_dataset_generator(
              self.dataset, 10, False
          )
          assert next(pred).shape == (5, 100, 10, 10)
  
          # Test generators
          l_gen = wrapper.predict_on_dataset_generator(
              self.dataset, 10, False
          )
          l = wrapper.predict_on_dataset(self.dataset, 10, False)
          assert np.allclose(next(l_gen)[0], l[0])
  
          # Test Half
          l_gen = wrapper.predict_on_dataset_generator(
              self.dataset, 10, half=True
          )
          l = wrapper.predict_on_dataset(self.dataset, 10, half=True)
          assert next(l_gen).dtype == np.float16
          assert l.dtype == np.float16
  
file_path: baal/tests/transformers_trainer_wrapper_test.py
imports: import unittest
  from copy import deepcopy
  import numpy
  import pytest
  import torch
  from transformers import GPT2LMHeadModel, GPT2Config
  from transformers import TrainingArguments
  from baal.transformers_trainer_wrapper import BaalTransformersTrainer
label_desc: Use image node_id 2 for calling the BaalTransformerTrainer obj's underlying member method code with example usage: obj.test_predict_on_dataset_generator() without return types