class_docstr: Implements a pipeline of Datasets.
  
  DatasetPipelines implement pipelined execution. This allows for the
  overlapped execution of data input (e.g., reading files), computation
  (e.g. feature preprocessing), and output (e.g., distributed ML training).
  
  A DatasetPipeline can be created by either repeating a Dataset
  (``ds.repeat(times=None)``), by turning a single Dataset into a pipeline
  (``ds.window(blocks_per_window=10)``), or defined explicitly using
  ``DatasetPipeline.from_iterable()``.
  
  DatasetPipeline supports the all the per-record transforms of Datasets
  (e.g., map, flat_map, filter), holistic transforms (e.g., repartition),
  and output methods (e.g., iter_rows, to_tf, to_torch, write_datasource).
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docstr: Create a pipeline from an sequence of Dataset producing functions.
  
  Args:
      iterable: A finite or infinite-length sequence of functions that
          each produce a Dataset when called.
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code: def from_iterable(
      iterable: Iterable[Callable[[], Dataset]],
  ) -> "DatasetPipeline":
      """Create a pipeline from an sequence of Dataset producing functions.
  
      Args:
          iterable: A finite or infinite-length sequence of functions that
              each produce a Dataset when called.
      """
      _raise_dataset_pipeline_deprecation_warning()
  
imports: from typing import Callable, Iterable
  from ray.data.dataset import Dataset, _raise_dataset_pipeline_deprecation_warning
  from ray.util.annotations import Deprecated
label_desc: Use image node_id 1 for calling the DatasetPipeline obj's underlying member method code with example usage: obj.from_iterable(iterable) without return types