class_docstr: Builder config for a view transforming another dataset.
class_docstr_tok: ["Builder","config","for","a","view","transforming","another","dataset","."]
docstr: Initialize a ViewConfig.
  
  Arguments:
    name: the name of this config.
    input_dataset: the dataset on which this view is being applied. If `None`,
      the input dataset needs to be specified in the `ViewBuilder`.
    ex_transformations: transformations that need to be applied to individual
      examples / rows. The transformations are normal Python functions. See
      the documentation for more information. Note that you cannot specify
      both example and dataset transformations.
    ds_transformations: `tf.data` transformations that are applied on the
      whole `tf.data.Dataset`. Note that you cannot specify both example and
      dataset transformations.
    **kwargs: additional arguments passed to the `BuilderConfig` such as
      `version`, `release_notes`, `supported_versions`, and `description`.
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code: def __init__(
      self,
      *,
      name: str,
      input_dataset: Union[None, str, naming.DatasetReference] = None,
      ex_transformations: Optional[
          List[transform_lib.ExampleTransformFn]
      ] = None,
      ds_transformations: Optional[
          List[Callable[[tf.data.Dataset], tf.data.Dataset]]
      ] = None,
      **kwargs,
  ):
      """Initialize a ViewConfig.
  
      Arguments:
        name: the name of this config.
        input_dataset: the dataset on which this view is being applied. If `None`,
          the input dataset needs to be specified in the `ViewBuilder`.
        ex_transformations: transformations that need to be applied to individual
          examples / rows. The transformations are normal Python functions. See
          the documentation for more information. Note that you cannot specify
          both example and dataset transformations.
        ds_transformations: `tf.data` transformations that are applied on the
          whole `tf.data.Dataset`. Note that you cannot specify both example and
          dataset transformations.
        **kwargs: additional arguments passed to the `BuilderConfig` such as
          `version`, `release_notes`, `supported_versions`, and `description`.
      """
      if isinstance(input_dataset, str):
          input_dataset = naming.DatasetReference.from_tfds_name(
              input_dataset
          )
      self.input_dataset = input_dataset
      if ex_transformations and ds_transformations:
          raise ValueError(
              "It is not supported to use both TF data and example transformations!"
          )
      self.ex_transformations = ex_transformations
      self.ds_transformations = ds_transformations
      super().__init__(name=name, **kwargs)
  
imports: from __future__ import annotations
  import functools
  from typing import Callable, Dict, Iterator, List, Optional, Union
  from tensorflow_datasets.core import beam_utils
  from tensorflow_datasets.core import dataset_builder
  from tensorflow_datasets.core import dataset_utils
  from tensorflow_datasets.core import load
  from tensorflow_datasets.core import naming
  from tensorflow_datasets.core import registered
  from tensorflow_datasets.core import split_builder
  from tensorflow_datasets.core import splits
  from tensorflow_datasets.core import transform
  from tensorflow_datasets.core.download import download_manager
  from tensorflow_datasets.core.utils import read_config
  from tensorflow_datasets.core.utils.lazy_imports_utils import apache_beam
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
label_desc: Use image node_id 1 to create a new ViewConfig object from inherited base classes: dataset_builder with example: obj = ViewConfig()