code: def read_parquet(
      path,
      engine: str = "auto",
      columns: list = None,
      groups_as_chunks: bool = False,
      use_arrow_dtype: bool = None,
      incremental_index: bool = False,
      storage_options: dict = None,
      memory_scale: int = None,
      merge_small_files: bool = True,
      merge_small_file_options: dict = None,
      gpu: bool = None,
      **kwargs,
  ):
      """
      Load a parquet object from the file path, returning a DataFrame.
  
      Parameters
      ----------
      path : str, path object or file-like object
          Any valid string path is acceptable. The string could be a URL.
          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``.
          By file-like object, we refer to objects with a ``read()`` method,
          such as a file handler (e.g. via builtin ``open`` function)
          or ``StringIO``.
      engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
          Parquet library to use. The default 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.
      groups_as_chunks : bool, default False
          if True, each row group correspond to a chunk.
          if False, each file correspond to a chunk.
          Only available for 'pyarrow' engine.
      incremental_index: bool, default False
          If index_col not specified, ensure range index incremental,
          gain a slightly better performance if setting False.
      use_arrow_dtype: bool, default None
          If True, use arrow dtype to store columns.
      storage_options: dict, optional
          Options for storage connection.
      memory_scale: int, optional
          Scale that real memory occupation divided with raw file size.
      merge_small_files: bool, default True
          Merge small files whose size is small.
      merge_small_file_options: dict
          Options for merging small files
      **kwargs
          Any additional kwargs are passed to the engine.
  
      Returns
      -------
      Mars DataFrame
      """
  
      engine_type = check_engine(engine)
      engine = get_engine(engine_type)
  
      single_path = path[0] if isinstance(path, list) else path
      fs = get_fs(single_path, storage_options)
      is_partitioned = False
      if fs.isdir(single_path):
          paths = fs.ls(path)
          if all(fs.isdir(p) for p in paths):
              # If all are directories, it is read as a partitioned dataset.
              dtypes = engine.read_partitioned_dtypes(
                  fs, path, storage_options
              )
              is_partitioned = True
          else:
              with fs.open(paths[0], mode="rb") as f:
                  dtypes = engine.read_dtypes(f)
      else:
          if not isinstance(path, list):
              file_path = glob(path, storage_options=storage_options)[0]
          else:
              file_path = path[0]
  
          with open_file(
              file_path, storage_options=storage_options
          ) as f:
              dtypes = engine.read_dtypes(f)
  
      if columns:
          dtypes = dtypes[columns]
  
      if use_arrow_dtype is None:
          use_arrow_dtype = options.dataframe.use_arrow_dtype
      if use_arrow_dtype:
          dtypes = to_arrow_dtypes(dtypes)
  
      index_value = parse_index(pd.RangeIndex(-1))
      columns_value = parse_index(dtypes.index, store_data=True)
      op = DataFrameReadParquet(
          path=path,
          engine=engine_type,
          columns=columns,
          groups_as_chunks=groups_as_chunks,
          use_arrow_dtype=use_arrow_dtype,
          read_kwargs=kwargs,
          incremental_index=incremental_index,
          storage_options=storage_options,
          is_partitioned=is_partitioned,
          memory_scale=memory_scale,
          merge_small_files=merge_small_files,
          merge_small_file_options=merge_small_file_options,
          gpu=gpu,
      )
      return op(
          index_value=index_value,
          columns_value=columns_value,
          dtypes=dtypes,
      )
  
imports: import os
  from typing import Dict
  from urllib.parse import urlparse
  import numpy
  import pandas
  from ...None import opcodes
  from ...config import options
  from ...lib.filesystem import file_size, get_fs, glob, open_file, FileSystem
  from ...serialization.serializables import AnyField, BoolField, DictField, ListField, StringField, Int32Field, Int64Field
  from ...utils import is_object_dtype, lazy_import
  from ..arrays import ArrowStringDtype
  from ..operands import OutputType
  from ..utils import parse_index, to_arrow_dtypes, contain_arrow_dtype
  from .core import IncrementalIndexDatasource, ColumnPruneSupportedDataSourceMixin, IncrementalIndexDataSourceMixin, merge_small_files
label_desc: Use image node_id 4 for calling a global function with example usage: read_parquet(path, engine, columns, groups_as_chunks, use_arrow_dtype, incremental_index, storage_options, memory_scale, merge_small_files, merge_small_file_options, gpu) and returns: op