class_docstr: PTransform to run AnalyzeAndTransformDataset.
class_docstr_tok: ["PTransform","to","run","AnalyzeAndTransformDataset","."]
docstr: Constructor.
  
  Args:
    dataset: BenchmarkDataset object.
    tfxio: A `tfx_bsl.TFXIO` instance.
    preprocessing_fn: preprocessing_fn.
    transform_input_dataset_metadata: dataset_metadata.DatasetMetadata.
    force_tf_compat_v1: If False then Transform will use its native TF2
      version, if True then Transform will use its TF1 version.
    max_num_examples: Max number of examples to read from the dataset.
    generate_dataset: If True, generates the raw dataset and appropriate
      intermediate outputs (just the TFT SavedModel for now) necessary for
      other benchmarks.
docstr_tok: ["Constructor",".","Args",":","dataset",":","BenchmarkDataset","object",".","tfxio",":","A","`","tfx_bsl.TFXIO","`","instance",".","preprocessing_fn",":","preprocessing_fn",".","transform_input_dataset_metadata",":","dataset_metadata.DatasetMetadata",".","force_tf_compat_v1",":","If","False","then","Transform","will","use","its","native","TF2","version",",","if","True","then","Transform","will","use","its","TF1","version",".","max_num_examples",":","Max","number","of","examples","to","read","from","the","dataset",".","generate_dataset",":","If","True",",","generates","the","raw","dataset","and","appropriate","intermediate","outputs","(","just","the","TFT","SavedModel","for","now",")","necessary","for","other","benchmarks","."]
code: def __init__(
      self,
      dataset,
      tfxio,
      preprocessing_fn,
      transform_input_dataset_metadata,
      force_tf_compat_v1=True,
      max_num_examples=None,
      generate_dataset=False,
  ):
      """Constructor.
  
      Args:
        dataset: BenchmarkDataset object.
        tfxio: A `tfx_bsl.TFXIO` instance.
        preprocessing_fn: preprocessing_fn.
        transform_input_dataset_metadata: dataset_metadata.DatasetMetadata.
        force_tf_compat_v1: If False then Transform will use its native TF2
          version, if True then Transform will use its TF1 version.
        max_num_examples: Max number of examples to read from the dataset.
        generate_dataset: If True, generates the raw dataset and appropriate
          intermediate outputs (just the TFT SavedModel for now) necessary for
          other benchmarks.
      """
      self._dataset = dataset
      self._tfxio = tfxio
      self._preprocessing_fn = preprocessing_fn
      self._transform_input_dataset_metadata = (
          transform_input_dataset_metadata
      )
      self._force_tf_compat_v1 = force_tf_compat_v1
      self._max_num_examples = max_num_examples
      self._generate_dataset = generate_dataset
  
imports: import collections
  import shutil
  import tempfile
  import time
  from absl import logging
  import apache_beam
  from apache_beam.utils import shared
  import tensorflow
  import tensorflow_transform
  from tensorflow_transform import graph_tools
  from tensorflow_transform import impl_helper
  import tensorflow_transform.beam
  from tensorflow_transform.beam import impl
  from tensorflow_transform.saved import saved_transform_io
  from tensorflow_transform.saved import saved_transform_io_v2
  from tensorflow_transform.tf_metadata import dataset_metadata
  from tensorflow_transform.tf_metadata import schema_utils
  import tfx
  from tfx.benchmarks import benchmark_utils
  from tfx.benchmarks import benchmark_base
  from tfx_bsl.coders import example_coder
  from tfx_bsl.tfxio import tensor_adapter
  from tfx_bsl.tfxio import tf_example_record
label_desc: Use image node_id 1 to create a new _AnalyzeAndTransformDataset object from inherited base classes: beam with example: obj = _AnalyzeAndTransformDataset(dataset, tfxio, preprocessing_fn, transform_input_dataset_metadata, force_tf_compat_v1, max_num_examples, generate_dataset)