code: def read_sql(
      sql: str,
      con: str,
      index_col: Optional[Union[str, List[str]]] = None,
      columns: Optional[Union[str, List[str]]] = None,
      **options: Any,
  ) -> DataFrame:
      """
      Read SQL query or database table into a DataFrame.
  
      This function is a convenience wrapper around ``read_sql_table`` and
      ``read_sql_query`` (for backward compatibility). It will delegate
      to the specific function depending on the provided input. A SQL query
      will be routed to ``read_sql_query``, while a database table name will
      be routed to ``read_sql_table``. Note that the delegated function might
      have more specific notes about their functionality not listed here.
  
      .. note:: Some database might hit the issue of Spark: SPARK-27596
  
      Parameters
      ----------
      sql : string
          SQL query to be executed or a table name.
      con : str
          A JDBC URI could be provided as str.
  
          .. note:: The URI must be JDBC URI instead of Python's database URI.
  
      index_col : string or list of strings, optional, default: None
          Column(s) to set as index(MultiIndex).
      columns : list, default: None
          List of column names to select from SQL table (only used when reading
          a table).
      options : dict
          All other options passed directly into Spark's JDBC data source.
  
      Returns
      -------
      DataFrame
  
      See Also
      --------
      read_sql_table : Read SQL database table into a DataFrame.
      read_sql_query : Read SQL query into a DataFrame.
  
      Examples
      --------
      >>> ps.read_sql('table_name', 'jdbc:postgresql:db_name')  # doctest: +SKIP
      >>> ps.read_sql('SELECT * FROM table_name', 'jdbc:postgresql:db_name')  # doctest: +SKIP
      """
      if (
          "options" in options
          and isinstance(options.get("options"), dict)
          and len(options) == 1
      ):
          options = options.get("options")
  
      striped = sql.strip()
      if (
          " " not in striped
      ):  # TODO: identify the table name or not more precisely.
          return read_sql_table(
              sql, con, index_col=index_col, columns=columns, **options
          )
      else:
          return read_sql_query(
              sql, con, 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 14 for calling a global function with example usage: read_sql(sql, con, index_col, columns) and returns: read_sql_table, read_sql_query