code: def read_delta(
      path: str,
      version: Optional[str] = None,
      timestamp: Optional[str] = None,
      index_col: Optional[Union[str, List[str]]] = None,
      **options: Any,
  ) -> DataFrame:
      """
      Read a Delta Lake table on some file system and return a DataFrame.
  
      If the Delta Lake table is already stored in the catalog (aka the metastore), use 'read_table'.
  
      Parameters
      ----------
      path : string
          Path to the Delta Lake table.
      version : string, optional
          Specifies the table version (based on Delta's internal transaction version) to read from,
          using Delta's time travel feature. This sets Delta's 'versionAsOf' option. Note that
          this parameter and `timestamp` parameter cannot be used together, otherwise it will raise a
          `ValueError`.
      timestamp : string, optional
          Specifies the table version (based on timestamp) to read from,
          using Delta's time travel feature. This must be a valid date or timestamp string in Spark,
          and sets Delta's 'timestampAsOf' option. Note that this parameter and `version` parameter
          cannot be used together, otherwise it will raise a `ValueError`.
      index_col : str or list of str, optional, default: None
          Index column of table in Spark.
      options
          Additional options that can be passed onto Delta.
  
      Returns
      -------
      DataFrame
  
      See Also
      --------
      DataFrame.to_delta
      read_table
      read_spark_io
      read_parquet
  
      Examples
      --------
      >>> ps.range(1).to_delta('%s/read_delta/foo' % path)  # doctest: +SKIP
      >>> ps.read_delta('%s/read_delta/foo' % path)  # doctest: +SKIP
         id
      0   0
  
      >>> ps.range(10, 15, num_partitions=1).to_delta('%s/read_delta/foo' % path,
      ...                                             mode='overwrite')  # doctest: +SKIP
      >>> ps.read_delta('%s/read_delta/foo' % path)  # doctest: +SKIP
         id
      0  10
      1  11
      2  12
      3  13
      4  14
  
      >>> ps.read_delta('%s/read_delta/foo' % path, version=0)  # doctest: +SKIP
         id
      0   0
  
      You can preserve the index in the roundtrip as below.
  
      >>> ps.range(10, 15, num_partitions=1).to_delta(
      ...     '%s/read_delta/bar' % path, index_col="index")  # doctest: +SKIP
      >>> ps.read_delta('%s/read_delta/bar' % path, index_col="index")  # doctest: +SKIP
             id
      index
      0      10
      1      11
      2      12
      3      13
      4      14
      """
      if index_col is None:
          log_advice(
              "If `index_col` is not specified for `read_delta`, "
              "the default index is attached which can cause additional overhead."
          )
      if version is not None and timestamp is not None:
          raise ValueError(
              "version and timestamp cannot be used together."
          )
      if (
          "options" in options
          and isinstance(options.get("options"), dict)
          and len(options) == 1
      ):
          options = options.get("options")
  
      if version is not None:
          options["versionAsOf"] = version
      if timestamp is not None:
          options["timestampAsOf"] = timestamp
      return read_spark_io(
          path, format="delta", index_col=index_col, **options
      )
  
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 5 for calling a global function with example usage: read_delta(path, version, timestamp, index_col) and returns: read_spark_io