code: def test_predict_on_batch(self):
      self.wrapper.eval()
      input = torch.randn([2, 3, 10, 10])
  
      # iteration == 1
      pred = self.wrapper.predict_on_batch(input, 1, False)
      assert pred.size() == (2, 1, 1)
  
      # iterations > 1
      pred = self.wrapper.predict_on_batch(input, 10, False)
      assert pred.size() == (2, 1, 10)
  
      # iteration == 1
      self.wrapper = ModelWrapper(
          self.model, self.criterion, replicate_in_memory=False
      )
      pred = self.wrapper.predict_on_batch(input, 1, False)
      assert pred.size() == (2, 1, 1)
  
      # iterations > 1
      pred = self.wrapper.predict_on_batch(input, 10, False)
      assert pred.size() == (2, 1, 10)
  
imports: import math
  import unittest
  from unittest.mock import Mock
  import numpy
  import pytest
  import torch
  import torchmetrics
  from torch import nn
  from torch.utils.data import Dataset, DataLoader
  from baal.modelwrapper import ModelWrapper, mc_inference
  from baal.utils.metrics import ClassificationReport
label_desc: Use image node_id 4 for calling the ModelWrapperTest obj's underlying member method code with example usage: obj.test_predict_on_batch() without return types