class_docstr: A beam.PTransform that implements NonStreamingCustomStatsGenerator.
class_docstr_tok: ["A","beam.PTransform","that","implements","NonStreamingCustomStatsGenerator","."]
docstr: Initializes _GenerateNonStreamingCustomStats.
docstr_tok: ["Initializes","_GenerateNonStreamingCustomStats","."]
code: def __init__(
      self,
      stats_fn: PartitionedStatsFn,
      num_partitions: int,
      min_partitions_stat_presence: int,
      seed: int,
      max_examples_per_partition: int,
      batch_size: int,
      name: Text,
  ) -> None:
      """Initializes _GenerateNonStreamingCustomStats."""
  
      self._stats_fn = stats_fn
      self._num_partitions = num_partitions
      self._min_partitions_stat_presence = min_partitions_stat_presence
      self._name = name
      self._seed = seed
      self._max_examples_per_partition = max_examples_per_partition
  
      # Seeds the random number generator used in the partitioner.
      np.random.seed(self._seed)
  
imports: import collections
  import functools
  from typing import Dict, Iterable, Text, Tuple
  import apache_beam
  import numpy
  import pyarrow
  from tensorflow_data_validation import constants
  from tensorflow_data_validation import types
  from tensorflow_data_validation.statistics.generators import stats_generator
  from tensorflow_data_validation.utils import stats_util
  from tfx_bsl.arrow import table_util
  from tensorflow_metadata.proto.v0 import statistics_pb2
label_desc: Use image node_id 1 to create a new _GenerateNonStreamingCustomStats object from inherited base classes: beam with example: obj = _GenerateNonStreamingCustomStats(stats_fn, num_partitions, min_partitions_stat_presence, seed, max_examples_per_partition, batch_size, name)