code: def prepare_read(self, parallelism: int, **read_args):
      import pyarrow as pa
  
      def load_data():
          data_raw = load_breast_cancer(as_frame=True)
          dataset_df = data_raw["data"]
          dataset_df["target"] = data_raw["target"]
          if self._shuffle:
              dataset_df = shuffle(dataset_df)
          return [pa.Table.from_pandas(dataset_df)]
  
      meta = BlockMetadata(
          num_rows=None,
          size_bytes=None,
          schema=None,
          input_files=None,
          exec_stats=None,
      )
      return [ReadTask(load_data, meta)]
  
imports: import os
  from pathlib import Path
  from unittest.mock import patch
  import pytest
  import unittest
  from typing import Optional
  from sklearn.datasets import load_breast_cancer
  from sklearn.utils import shuffle
  import ray
  from ray import train, tune
  from ray.train import CheckpointConfig, RunConfig, ScalingConfig
  from ray.train.constants import RAY_CHDIR_TO_TRIAL_DIR
  from ray.train.examples.pytorch.torch_linear_example import train_func
  from ray.data import Dataset, Datasource, ReadTask, from_pandas, read_datasource
  from ray.data.block import BlockMetadata
  from ray.train.data_parallel_trainer import DataParallelTrainer
  from ray.train.torch import TorchTrainer
  from ray.train.trainer import BaseTrainer
  from ray.train.xgboost import XGBoostTrainer
  from ray.tune import Callback, CLIReporter
  from ray.tune.tune_config import TuneConfig
  from ray.tune.tuner import Tuner
label_desc: Use image node_id 2 for calling the TestDatasource obj's underlying member method code with example usage: obj.prepare_read(parallelism) and returns: list, list