code: def test_states(self):
      input = torch.randn([1, 3, 10, 10])
  
      def pred_with_dropout(replicate_in_memory):
          self.wrapper = ModelWrapper(
              self.model,
              self.criterion,
              replicate_in_memory=replicate_in_memory,
          )
          self.wrapper.train()
          # Dropout make the pred changes
          preds = torch.stack(
              [
                  self.wrapper.predict_on_batch(
                      input, iterations=1, cuda=False
                  )
                  for _ in range(10)
              ]
          ).view(10, -1)
          assert not torch.allclose(torch.mean(preds, 0), preds[0])
  
      pred_with_dropout(replicate_in_memory=True)
      pred_with_dropout(replicate_in_memory=False)
  
      def pred_without_dropout(replicate_in_memory):
          self.wrapper = ModelWrapper(
              self.model,
              self.criterion,
              replicate_in_memory=replicate_in_memory,
          )
          # Dropout is not active in eval
          self.wrapper.eval()
          preds = torch.stack(
              [
                  self.wrapper.predict_on_batch(
                      input, iterations=1, cuda=False
                  )
                  for _ in range(10)
              ]
          ).view(10, -1)
          assert torch.allclose(torch.mean(preds, 0), preds[0])
  
      pred_without_dropout(replicate_in_memory=True)
      pred_without_dropout(replicate_in_memory=False)
  
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 8 for calling the ModelWrapperTest obj's underlying member method code with example usage: obj.test_states() without return types