code: def read_orc(
      path: str,
      columns: Optional[List[str]] = None,
      index_col: Optional[Union[str, List[str]]] = None,
      **options: Any,
  ) -> "DataFrame":
      """
      Load an ORC object from the file path, returning a DataFrame.
  
      Parameters
      ----------
      path : str
          The path string storing the ORC file to be read.
      columns : list, default None
          If not None, only these columns will be read from the file.
      index_col : str or list of str, optional, default: None
          Index column of table in Spark.
      options : dict
          All other options passed directly into Spark's data source.
  
      Returns
      -------
      DataFrame
  
      Examples
      --------
      >>> ps.range(1).to_orc('%s/read_spark_io/data.orc' % path)
      >>> ps.read_orc('%s/read_spark_io/data.orc' % path, columns=['id'])
         id
      0   0
  
      You can preserve the index in the roundtrip as below.
  
      >>> ps.range(1).to_orc('%s/read_spark_io/data.orc' % path, index_col="index")
      >>> ps.read_orc('%s/read_spark_io/data.orc' % path, columns=['id'], index_col="index")
      ... # doctest: +NORMALIZE_WHITESPACE
             id
      index
      0       0
      """
      if (
          "options" in options
          and isinstance(options.get("options"), dict)
          and len(options) == 1
      ):
          options = options.get("options")
  
      psdf = read_spark_io(
          path, format="orc", index_col=index_col, **options
      )
  
      if columns is not None:
          psdf_columns = psdf.columns
          new_columns = list()
          for column in list(columns):
              if column in psdf_columns:
                  new_columns.append(column)
              else:
                  raise ValueError(
                      "Unknown column name '{}'".format(column)
                  )
          psdf = psdf[new_columns]
  
      return psdf
  
imports: from typing import Any, Callable, Dict, List, Optional, Set, Sized, Tuple, Type, Union, cast, no_type_check
  from collections.abc import Iterable
  from datetime import tzinfo
  from functools import reduce
  from io import BytesIO
  import json
  import warnings
  import numpy
  import pandas
  from pandas.api.types import is_datetime64_dtype, is_list_like
  from pandas.tseries.offsets import DateOffset
  import pyarrow
  import pyarrow.parquet
  from pyspark.sql import functions, Column
  from pyspark.sql.functions import pandas_udf
  from pyspark.sql.types import ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType, BooleanType, TimestampType, TimestampNTZType, DecimalType, StringType, DateType, StructType, DataType
  from pyspark.sql.dataframe import DataFrame
  from pyspark import pandas
  from pyspark.pandas._typing import Axis, Dtype, Label, Name
  from pyspark.pandas.base import IndexOpsMixin
  from pyspark.pandas.utils import align_diff_frames, default_session, is_name_like_tuple, is_name_like_value, name_like_string, same_anchor, scol_for, validate_axis, log_advice
  from pyspark.pandas.frame import DataFrame, _reduce_spark_multi
  from pyspark.pandas.internal import InternalFrame, DEFAULT_SERIES_NAME, HIDDEN_COLUMNS, SPARK_INDEX_NAME_FORMAT
  from pyspark.pandas.series import Series, first_series
  from pyspark.pandas.spark.utils import as_nullable_spark_type, force_decimal_precision_scale
  from pyspark.pandas.indexes import Index, DatetimeIndex, TimedeltaIndex
  from pyspark.pandas.indexes.multi import MultiIndex
  from pyspark.sql.utils import get_column_class
label_desc: Use image node_id 28 for calling a global function with example usage: read_orc(path, columns, index_col) and returns: psdf