class_docstr: Chicago taxi dataset with a TFT preprocessing_fn containing specified number of analyzers.
  
  Note that the analyzers are called within the corresponding mappers. Half of
  the mappers will be `tft.compute_and_apply_vocabulary`. Another half is split
  between `tft.bucketize` and `tft.scale_to_z_score`.
class_docstr_tok: ["Chicago","taxi","dataset","with","a","TFT","preprocessing_fn","containing","specified","number","of","analyzers",".","Note","that","the","analyzers","are","called","within","the","corresponding","mappers",".","Half","of","the","mappers","will","be","`","tft.compute_and_apply_vocabulary","`",".","Another","half","is","split","between","`","tft.bucketize","`","and","`","tft.scale_to_z_score","`","."]
code: def __init__(
      self, base_dir: Optional[str] = None, num_analyzers: int = 10
  ):
      super().__init__(base_dir)
      self._num_vocabs = max(
          len(self._VOCABULARY_KEYS),
          math.ceil(num_analyzers * self._VOCABS_SHARE),
      )
      self._num_bucketize = max(
          len(self._BUCKETIZE_KEYS),
          math.ceil(num_analyzers * self._BUCKETIZE_SHARE),
      )
      self._num_scale = max(
          len(self._SCALE_KEYS),
          num_analyzers - self._num_vocabs - self._num_bucketize,
      )
  
imports: import itertools
  import math
  import os
  import shutil
  import tempfile
  from typing import Optional
  from absl import logging
  import apache_beam
  import tensorflow_transform
  from tfx import components
  from tfx.benchmarks import benchmark_dataset
  from tfx.components.example_gen.csv_example_gen import executor
  from tfx.examples.chicago_taxi_pipeline import taxi_utils
  from tfx.orchestration import metadata
  from tfx.orchestration import pipeline
  from tfx.orchestration.beam.beam_dag_runner import BeamDagRunner
  from tfx.proto import trainer_pb2
  from tfx_bsl.coders import csv_decoder
label_desc: Use image node_id 1 to create a new WideChicagoTaxiDataset object from inherited base classes: ChicagoTaxiDataset with example: obj = WideChicagoTaxiDataset(base_dir, num_analyzers)