code: def load_one_fragment(
      fragment: pa.dataset.Fragment, columns: Optional[List[str]] = None
  ) -> pa.Table:
      fragment_columns = columns
      if fragment_columns is not None:
          fragment_columns = [
              col
              for col in fragment_columns
              if col in fragment.physical_schema.names
          ]
      fragment_table = fragment.to_table(
          columns=fragment_columns, use_threads=False
      )
      fragment_table = add_partitioning_values(
          fragment_table, fragment, columns
      )
      return fragment_table
  
imports: from contextlib import contextmanager
  from typing import Dict, Generator, List, Optional, Union
  import numpy
  import pandas
  import pyarrow
  import pyarrow.parquet
  import torch
  from numpy.typing import NDArray
  from pyarrow.dataset import get_partition_keys
  from fairseq2.data import CString
  from fairseq2.data.data_pipeline import DataPipeline, DataPipelineBuilder, read_sequence
label_desc: Use image node_id 9 for calling a global function with example usage: load_one_fragment(fragment, columns) and returns: fragment_table