code: def test_predict(self):
      l = self.wrapper.predict_on_dataset(
          self.dataset, 10, 20, use_cuda=False, workers=0
      )
      self.wrapper.eval()
      assert np.allclose(
          self.wrapper.predict_on_batch(
              self.dataset[0][0].unsqueeze(0), 20
          )[0]
          .detach()
          .numpy(),
          l[0],
      )
      assert np.allclose(
          self.wrapper.predict_on_batch(
              self.dataset[19][0].unsqueeze(0), 20
          )[0]
          .detach()
          .numpy(),
          l[19],
      )
      assert l.shape == (len(self.dataset), 1, 20)
  
      # Test generators
      l_gen = self.wrapper.predict_on_dataset_generator(
          self.dataset, 10, 20, use_cuda=False, workers=0
      )
      assert np.allclose(next(l_gen)[0], l[0])
      for last in l_gen:
          pass  # Get last item
      assert np.allclose(last[-1], l[-1])
  
      # Test Half
      l_gen = self.wrapper.predict_on_dataset_generator(
          self.dataset, 10, 20, use_cuda=False, workers=0, half=True
      )
      l = self.wrapper.predict_on_dataset(
          self.dataset, 10, 20, use_cuda=False, workers=0, half=True
      )
      assert next(l_gen).dtype == np.float16
      assert l.dtype == np.float16
  
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 7 for calling the ModelWrapperTest obj's underlying member method code with example usage: obj.test_predict() without return types