code: def read_images(
      paths: Union[str, List[str]],
      *,
      filesystem: Optional["pyarrow.fs.FileSystem"] = None,
      parallelism: int = -1,
      meta_provider: Optional[BaseFileMetadataProvider] = None,
      ray_remote_args: Dict[str, Any] = None,
      arrow_open_file_args: Optional[Dict[str, Any]] = None,
      partition_filter: Optional[PathPartitionFilter] = None,
      partitioning: Partitioning = None,
      size: Optional[Tuple[int, int]] = None,
      mode: Optional[str] = None,
      include_paths: bool = False,
      ignore_missing_paths: bool = False,
      shuffle: Union[Literal["files"], None] = None,
      file_extensions: Optional[
          List[str]
      ] = ImageDatasource._FILE_EXTENSIONS,
  ) -> Dataset:
      """Creates a :class:`~ray.data.Dataset` from image files.
  
      Examples:
          >>> import ray
          >>> path = "s3://anonymous@ray-example-data/batoidea/JPEGImages/"
          >>> ds = ray.data.read_images(path)
          >>> ds.schema()
          Column  Type
          ------  ----
          image   numpy.ndarray(shape=(32, 32, 3), dtype=uint8)
  
          If you need image file paths, set ``include_paths=True``.
  
          >>> ds = ray.data.read_images(path, include_paths=True)
          >>> ds.schema()
          Column  Type
          ------  ----
          image   numpy.ndarray(shape=(32, 32, 3), dtype=uint8)
          path    string
          >>> ds.take(1)[0]["path"]
          'ray-example-data/batoidea/JPEGImages/1.jpeg'
  
          If your images are arranged like:
  
          .. code::
  
              root/dog/xxx.png
              root/dog/xxy.png
  
              root/cat/123.png
              root/cat/nsdf3.png
  
          Then you can include the labels by specifying a
          :class:`~ray.data.datasource.partitioning.Partitioning`.
  
          >>> import ray
          >>> from ray.data.datasource.partitioning import Partitioning
          >>> root = "s3://anonymous@ray-example-data/image-datasets/dir-partitioned"
          >>> partitioning = Partitioning("dir", field_names=["class"], base_dir=root)
          >>> ds = ray.data.read_images(root, size=(224, 224), partitioning=partitioning)
          >>> ds.schema()
          Column  Type
          ------  ----
          image   numpy.ndarray(shape=(224, 224, 3), dtype=uint8)
          class   string
  
      Args:
          paths: A single file or directory, or a list of file or directory paths.
              A list of paths can contain both files and directories.
          filesystem: The pyarrow filesystem
              implementation to read from. These filesystems are specified in the
              `pyarrow docs <https://arrow.apache.org/docs/python/api/\
              filesystems.html#filesystem-implementations>`_. Specify this parameter if
              you need to provide specific configurations to the filesystem. By default,
              the filesystem is automatically selected based on the scheme of the paths.
              For example, if the path begins with ``s3://``, the `S3FileSystem` is used.
          parallelism: The amount of parallelism to use for the dataset. Defaults to -1,
              which automatically determines the optimal parallelism for your
              configuration. You should not need to manually set this value in most cases.
              For details on how the parallelism is automatically determined and guidance
              on how to tune it, see :ref:`Tuning read parallelism
              <read_parallelism>`. Parallelism is upper bounded by the total number of
              records in all the CSV files.
          meta_provider: A :ref:`file metadata provider <metadata_provider>`. Custom
              metadata providers may be able to resolve file metadata more quickly and/or
              accurately. In most cases, you do not need to set this. If ``None``, this
              function uses a system-chosen implementation.
          ray_remote_args: kwargs passed to :meth:`~ray.remote` in the read tasks.
          arrow_open_file_args: kwargs passed to
              `pyarrow.fs.FileSystem.open_input_file <https://arrow.apache.org/docs/\
                  python/generated/pyarrow.fs.FileSystem.html\
                      #pyarrow.fs.FileSystem.open_input_file>`_.
              when opening input files to read.
          partition_filter:  A
              :class:`~ray.data.datasource.partitioning.PathPartitionFilter`. Use
              with a custom callback to read only selected partitions of a dataset.
              By default, this filters out any file paths whose file extension does not
              match ``*.png``, ``*.jpg``, ``*.jpeg``, ``*.tiff``, ``*.bmp``, or ``*.gif``.
          partitioning: A :class:`~ray.data.datasource.partitioning.Partitioning` object
              that describes how paths are organized. Defaults to ``None``.
          size: The desired height and width of loaded images. If unspecified, images
              retain their original shape.
          mode: A `Pillow mode <https://pillow.readthedocs.io/en/stable/handbook/concepts\
              .html#modes>`_
              describing the desired type and depth of pixels. If unspecified, image
              modes are inferred by
              `Pillow <https://pillow.readthedocs.io/en/stable/index.html>`_.
          include_paths: If ``True``, include the path to each image. File paths are
              stored in the ``'path'`` column.
          ignore_missing_paths: If True, ignores any file/directory paths in ``paths``
              that are not found. Defaults to False.
          shuffle: If setting to "files", randomly shuffle input files order before read.
              Defaults to not shuffle with ``None``.
          file_extensions: A list of file extensions to filter files by.
  
      Returns:
          A :class:`~ray.data.Dataset` producing tensors that represent the images at
          the specified paths. For information on working with tensors, read the
          :ref:`tensor data guide <working_with_tensors>`.
  
      Raises:
          ValueError: if ``size`` contains non-positive numbers.
          ValueError: if ``mode`` is unsupported.
      """
      if meta_provider is None:
          meta_provider = get_image_metadata_provider()
  
      datasource = ImageDatasource(
          paths,
          size=size,
          mode=mode,
          include_paths=include_paths,
          filesystem=filesystem,
          meta_provider=meta_provider,
          open_stream_args=arrow_open_file_args,
          partition_filter=partition_filter,
          partitioning=partitioning,
          ignore_missing_paths=ignore_missing_paths,
          shuffle=shuffle,
          file_extensions=file_extensions,
      )
      return read_datasource(
          datasource,
          parallelism=parallelism,
          ray_remote_args=ray_remote_args,
      )
  
imports: import collections
  import logging
  import os
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Tuple, TypeVar, Union
  import numpy
  import ray
  from ray._private.auto_init_hook import wrap_auto_init
  from ray.air.util.tensor_extensions.utils import _create_possibly_ragged_ndarray
  from ray.data._internal.block_list import BlockList
  from ray.data._internal.delegating_block_builder import DelegatingBlockBuilder
  from ray.data._internal.lazy_block_list import LazyBlockList
  from ray.data._internal.logical.operators.from_operators import FromArrow, FromItems, FromNumpy, FromPandas
  from ray.data._internal.logical.operators.read_operator import Read
  from ray.data._internal.logical.optimizers import LogicalPlan
  from ray.data._internal.plan import ExecutionPlan
  from ray.data._internal.remote_fn import cached_remote_fn
  from ray.data._internal.stats import DatasetStats
  from ray.data._internal.util import _autodetect_parallelism, _lazy_import_pyarrow_dataset, _warn_on_high_parallelism, get_table_block_metadata, ndarray_to_block, pandas_df_to_arrow_block
  from ray.data.block import Block, BlockAccessor, BlockExecStats, BlockMetadata
  from ray.data.context import DataContext
  from ray.data.dataset import Dataset, MaterializedDataset
  from ray.data.datasource import BaseFileMetadataProvider, BigQueryDatasource, BinaryDatasource, Connection, CSVDatasource, Datasource, ImageDatasource, JSONDatasource, MongoDatasource, NumpyDatasource, ParquetBaseDatasource, ParquetDatasource, ParquetMetadataProvider, PathPartitionFilter, RangeDatasource, SQLDatasource, TextDatasource, TFRecordDatasource, TorchDatasource, WebDatasetDatasource
  from ray.data.datasource._default_metadata_providers import get_generic_metadata_provider, get_image_metadata_provider, get_parquet_bulk_metadata_provider, get_parquet_metadata_provider
  from ray.data.datasource.datasource import Reader
  from ray.data.datasource.file_based_datasource import _unwrap_arrow_serialization_workaround, _wrap_arrow_serialization_workaround
  from ray.data.datasource.partitioning import Partitioning
  from ray.types import ObjectRef
  from ray.util.annotations import DeveloperAPI, PublicAPI
  from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy
label_desc: Use image node_id 8 for calling a global function with example usage: read_images(paths) and returns: read_datasource