code: def read_parquet(
      path: FilePath | ReadBuffer[bytes],
      engine: str = "auto",
      columns: list[str] | None = None,
      storage_options: StorageOptions = None,
      use_nullable_dtypes: bool | lib.NoDefault = lib.no_default,
      dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
      **kwargs,
  ) -> DataFrame:
      """
      Load a parquet object from the file path, returning a DataFrame.
  
      Parameters
      ----------
      path : str, path object or file-like object
          String, path object (implementing ``os.PathLike[str]``), or file-like
          object implementing a binary ``read()`` function.
          The string could be a URL. Valid URL schemes include http, ftp, s3,
          gs, and file. For file URLs, a host is expected. A local file could be:
          ``file://localhost/path/to/table.parquet``.
          A file URL can also be a path to a directory that contains multiple
          partitioned parquet files. Both pyarrow and fastparquet support
          paths to directories as well as file URLs. A directory path could be:
          ``file://localhost/path/to/tables`` or ``s3://bucket/partition_dir``.
      engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
          Parquet library to use. If 'auto', then the option
          ``io.parquet.engine`` is used. The default ``io.parquet.engine``
          behavior is to try 'pyarrow', falling back to 'fastparquet' if
          'pyarrow' is unavailable.
      columns : list, default=None
          If not None, only these columns will be read from the file.
  
      {storage_options}
  
          .. versionadded:: 1.3.0
  
      use_nullable_dtypes : bool, default False
          If True, use dtypes that use ``pd.NA`` as missing value indicator
          for the resulting DataFrame. (only applicable for the ``pyarrow``
          engine)
          As new dtypes are added that support ``pd.NA`` in the future, the
          output with this option will change to use those dtypes.
          Note: this is an experimental option, and behaviour (e.g. additional
          support dtypes) may change without notice.
  
          .. deprecated:: 2.0
  
      dtype_backend : {{"numpy_nullable", "pyarrow"}}, defaults to NumPy backed DataFrames
          Which dtype_backend to use, e.g. whether a DataFrame should have NumPy
          arrays, nullable dtypes are used for all dtypes that have a nullable
          implementation when "numpy_nullable" is set, pyarrow is used for all
          dtypes if "pyarrow" is set.
  
          The dtype_backends are still experimential.
  
          .. versionadded:: 2.0
  
      **kwargs
          Any additional kwargs are passed to the engine.
  
      Returns
      -------
      DataFrame
      """
      impl = get_engine(engine)
  
      if use_nullable_dtypes is not lib.no_default:
          msg = (
              "The argument 'use_nullable_dtypes' is deprecated and will be removed "
              "in a future version."
          )
          if use_nullable_dtypes is True:
              msg += "Use dtype_backend='numpy_nullable' instead of use_nullable_dtype=True."
          warnings.warn(
              msg, FutureWarning, stacklevel=find_stack_level()
          )
      else:
          use_nullable_dtypes = False
      check_dtype_backend(dtype_backend)
  
      return impl.read(
          path,
          columns=columns,
          storage_options=storage_options,
          use_nullable_dtypes=use_nullable_dtypes,
          dtype_backend=dtype_backend,
          **kwargs,
      )
  
imports: from __future__ import annotations
  import io
  import os
  from typing import Any, Literal
  import warnings
  from warnings import catch_warnings
  from pandas._libs import lib
  from pandas._typing import DtypeBackend, FilePath, ReadBuffer, StorageOptions, WriteBuffer
  from pandas.compat._optional import import_optional_dependency
  from pandas.errors import AbstractMethodError
  from pandas.util._decorators import doc
  from pandas.util._exceptions import find_stack_level
  from pandas.util._validators import check_dtype_backend
  import pandas
  from pandas import DataFrame, get_option
  from pandas.core.shared_docs import _shared_docs
  from pandas.util.version import Version
  from pandas.io.common import IOHandles, get_handle, is_fsspec_url, is_url, stringify_path
label_desc: Use image node_id 4 for calling a global function with example usage: read_parquet(path, engine, columns, storage_options, use_nullable_dtypes, dtype_backend) and returns: impl