code: def test_e2e_prediction(self, shutdown_only):
      import torch
      from torchvision import transforms
      from torchvision.models import resnet18
  
      ray.shutdown()
      ray.init(num_cpus=2)
  
      dataset = ray.data.read_images("example://image-datasets/simple")
      transform = transforms.ToTensor()
  
      def preprocess(batch: Dict[str, np.ndarray]):
          return {
              "out": np.stack(
                  [transform(image) for image in batch["image"]]
              )
          }
  
      dataset = dataset.map_batches(preprocess, batch_format="numpy")
  
      class Predictor:
          def __init__(self):
              self.model = resnet18(pretrained=True)
  
          def __call__(self, batch: Dict[str, np.ndarray]):
              with torch.inference_mode():
                  torch_tensor = torch.as_tensor(batch["out"])
                  return {"prediction": self.model(torch_tensor)}
  
      predictions = dataset.map_batches(
          Predictor,
          compute=ray.data.ActorPoolStrategy(min_size=1),
          batch_size=4096,
      )
  
      for _ in predictions.iter_batches():
          pass
  
imports: import os
  import tempfile
  from typing import Dict
  import numpy
  import pyarrow
  import pytest
  from fsspec.implementations.local import LocalFileSystem
  from PIL import Image
  import ray
  from ray.data.datasource import Partitioning
  from ray.data.datasource.file_meta_provider import FastFileMetadataProvider
  from ray.data.datasource.image_datasource import ImageDatasource, _ImageFileMetadataProvider
  from ray.data.extensions import ArrowTensorType
  from ray.data.tests.conftest import *
  from ray.data.tests.mock_http_server import *
  from ray.tests.conftest import *
label_desc: Use image node_id 13 for calling the TestReadImages obj's underlying member method code with example usage: obj.test_e2e_prediction(shutdown_only) and returns: dict, dict