code: def _PartitionTransform(
      pcol,
      row_partitions: int,
      column_partitions: int,
      label_feature: types.FeaturePath,
      seed: int,
  ):
      """Ptransform wrapping _default_assign_to_partition."""
      # We need to find the column name associated with the label path.
      steps = label_feature.steps()
      if not steps:
          raise ValueError("Empty label feature")
      label = steps[0]
      return pcol | "PartitionRowsCols" >> beam.ParDo(
          _PartitionFn(row_partitions, column_partitions, label, seed)
      )
  
imports: import collections
  from typing import Any, Dict, Iterable, List, Optional, Set, Tuple
  from absl import logging
  import apache_beam
  import numpy
  import pandas
  import pyarrow
  from tensorflow_data_validation import types
  from tensorflow_data_validation.statistics.generators import partitioned_stats_generator
  from tensorflow_data_validation.utils import feature_partition_util
  from tensorflow_data_validation.utils import mutual_information_util
  from tensorflow_data_validation.utils import schema_util
  from tensorflow_data_validation.utils import stats_util
  from tfx_bsl.arrow import array_util
  from tensorflow_metadata.proto.v0 import schema_pb2
  from tensorflow_metadata.proto.v0 import statistics_pb2
label_desc: Use image node_id 9 for calling a global function with example usage: _PartitionTransform(pcol, row_partitions, column_partitions, label_feature, seed) and returns: unknown