code: def test_read_large_data(ray_start_cluster):
      # Test 20G input with single task
      num_blocks_per_task = 20
      block_size = 1024 * 1024 * 1024
  
      cluster = ray_start_cluster
      cluster.add_node(num_cpus=1)
  
      ray.init(cluster.address)
  
      def foo(batch):
          return pd.DataFrame({"one": [1]})
  
      ds = ray.data.read_datasource(
          RandomBytesDatasource(),
          parallelism=1,
          num_batches_per_task=num_blocks_per_task,
          row_size=block_size,
      )
  
      ds = ds.map_batches(foo, num_rows_per_batch=None)
      assert ds.count() == num_blocks_per_task
  
imports: import os
  import time
  from dataclasses import astuple, dataclass
  import numpy
  import pandas
  import pyarrow
  import pytest
  import ray
  from ray.data import Dataset
  from ray.data._internal.arrow_block import ArrowBlockAccessor
  from ray.data._internal.lazy_block_list import LazyBlockList
  from ray.data.block import BlockMetadata
  from ray.data.datasource import Datasource
  from ray.data.datasource.csv_datasource import CSVDatasource
  from ray.data.datasource.datasource import Reader, ReadTask
  from ray.data.tests.conftest import CoreExecutionMetrics, assert_blocks_expected_in_plasma, assert_core_execution_metrics_equals, get_initial_core_execution_metrics_snapshot
  from ray.tests.conftest import *
label_desc: Use image node_id 5 for calling a global function with example usage: test_read_large_data(ray_start_cluster) and returns: pd