class_docstr: Parquet datasource, for reading and writing Parquet files.
  
  The primary difference from ParquetBaseDatasource is that this uses
  PyArrow's `ParquetDataset` abstraction for dataset reads, and thus offers
  automatic Arrow dataset schema inference and row count collection at the
  cost of some potential performance and/or compatibility penalties.
class_docstr_tok: ["Parquet","datasource",",","for","reading","and","writing","Parquet","files",".","The","primary","difference","from","ParquetBaseDatasource","is","that","this","uses","PyArrow","'s","`","ParquetDataset","`","abstraction","for","dataset","reads",",","and","thus","offers","automatic","Arrow","dataset","schema","inference","and","row","count","collection","at","the","cost","of","some","potential","performance","and\/or","compatibility","penalties","."]
code: def supports_distributed_reads(self) -> bool:
      return self._supports_distributed_reads
  
imports: import logging
  from typing import TYPE_CHECKING, Any, Callable, Dict, Iterator, List, Literal, Optional, Union
  import numpy
  import ray
  import ray.cloudpickle
  from ray.data._internal.progress_bar import ProgressBar
  from ray.data._internal.remote_fn import cached_remote_fn
  from ray.data._internal.util import _check_pyarrow_version, _is_local_scheme
  from ray.data.block import Block
  from ray.data.context import DataContext
  from ray.data.datasource import Datasource
  from ray.data.datasource._default_metadata_providers import get_generic_metadata_provider
  from ray.data.datasource.datasource import ReadTask
  from ray.data.datasource.file_meta_provider import DefaultParquetMetadataProvider, ParquetMetadataProvider, _handle_read_os_error
  from ray.data.datasource.partitioning import PathPartitionFilter
  from ray.data.datasource.path_util import _has_file_extension, _resolve_paths_and_filesystem
  from ray.util.annotations import PublicAPI
label_desc: Use image node_id 6 for calling the ParquetDatasource obj's underlying member method code with example usage: obj.supports_distributed_reads() and returns: self