code: def _parquet_column_iterator(
      paths: Iterable[str], column_name: str
  ) -> Iterator[pa.Buffer]:
      """Yields values from a bytes column in a set of parquet file partitions."""
      dataset = pa.parquet.ParquetDataset(paths)
      table = dataset.read(columns=[column_name])
      for record_batch in table.to_batches():
          # always read index 0 because we filter to one column
          value_array = record_batch.column(0)
          for value in value_array:
              yield value.as_buffer()
  
imports: import itertools
  import os
  from typing import Any, Dict, Iterable, Iterator, List, Optional, Tuple, Union
  from absl import logging
  import apache_beam
  import numpy
  import pyarrow
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.evaluators import evaluator
  from tensorflow_model_analysis.evaluators import metrics_validator
  from tensorflow_model_analysis.metrics import calibration_plot
  from tensorflow_model_analysis.metrics import confusion_matrix_plot
  from tensorflow_model_analysis.metrics import metric_specs
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.post_export_metrics import metric_keys
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.proto import metrics_for_slice_pb2
  from tensorflow_model_analysis.proto import validation_result_pb2
  from tensorflow_model_analysis.slicer import slicer_lib
  from tensorflow_model_analysis.utils import math_util
  from tensorflow_model_analysis.writers import writer
  from google.protobuf import wrappers_pb2
label_desc: Use image node_id 2 for calling a global function with example usage: _parquet_column_iterator(paths, column_name) without return types