code: def test_csv_read_partitioned_with_filter_multikey(
      ray_start_regular_shared,
      fs,
      data_path,
      endpoint_url,
      write_base_partitioned_df,
      assert_base_partitioned_ds,
  ):
      storage_options = (
          {}
          if endpoint_url is None
          else dict(client_kwargs=dict(endpoint_url=endpoint_url))
      )
      partition_keys = ["one", "two"]
      file_writer_fn = partial(
          df_to_csv, storage_options=storage_options, index=False
      )
      kept_file_counter = Counter.remote()
      skipped_file_counter = Counter.remote()
  
      def keep_expected_partitions(kv_dict):
          keep = bool(kv_dict) and (
              (
                  kv_dict["one"] == "1"
                  and kv_dict["two"] in {"a", "b", "c"}
              )
              or (
                  kv_dict["one"] == "3"
                  and kv_dict["two"] in {"e", "f", "g"}
              )
          )
          counter = kept_file_counter if keep else skipped_file_counter
          ray.get(counter.increment.remote())
          return keep
  
      for i, style in enumerate(
          [PartitionStyle.HIVE, PartitionStyle.DIRECTORY]
      ):
          base_dir = os.path.join(data_path, style.value)
          partition_path_encoder = PathPartitionEncoder.of(
              style=style,
              base_dir=base_dir,
              field_names=partition_keys,
              filesystem=fs,
          )
          write_base_partitioned_df(
              partition_keys,
              partition_path_encoder,
              file_writer_fn,
          )
          df = pd.DataFrame({"1": [1]})
          file_writer_fn(df, os.path.join(data_path, f"test{i}.csv"))
          partition_path_filter = PathPartitionFilter.of(
              style=style,
              base_dir=base_dir,
              field_names=partition_keys,
              filesystem=fs,
              filter_fn=keep_expected_partitions,
          )
          ds = ray.data.read_csv(
              data_path,
              partition_filter=partition_path_filter,
              filesystem=fs,
              parallelism=6,
          )
          assert_base_partitioned_ds(
              ds, num_input_files=6, num_computed=6
          )
          assert ray.get(kept_file_counter.get.remote()) == 6
          if i == 0:
              # expect to skip 1 unpartitioned files in the parent of the base directory
              assert ray.get(skipped_file_counter.get.remote()) == 1
          else:
              # expect to skip 2 unpartitioned files in the parent of the base directory
              # plus 6 unpartitioned files in the base directory's sibling directories
              assert ray.get(skipped_file_counter.get.remote()) == 8
          ray.get(kept_file_counter.reset.remote())
          ray.get(skipped_file_counter.reset.remote())
  
imports: import itertools
  import os
  import shutil
  from functools import partial
  import pandas
  import pyarrow
  import pyarrow.parquet
  import pytest
  from packaging.version import Version
  from pytest_lazyfixture import lazy_fixture
  import ray
  from ray.data.block import BlockAccessor
  from ray.data.datasource import BaseFileMetadataProvider, FastFileMetadataProvider, PartitionStyle, PathPartitionFilter
  from ray.data.datasource.file_based_datasource import FILE_SIZE_FETCH_PARALLELIZATION_THRESHOLD
  from ray.data.datasource.path_util import _unwrap_protocol
  from ray.data.tests.conftest import *
  from ray.data.tests.mock_http_server import *
  from ray.data.tests.test_partitioning import PathPartitionEncoder
  from ray.data.tests.util import Counter
  from ray.tests.conftest import *
label_desc: Use image node_id 12 for calling a global function with example usage: test_csv_read_partitioned_with_filter_multikey(ray_start_regular_shared, fs, data_path, endpoint_url, write_base_partitioned_df, assert_base_partitioned_ds) and returns: keep