code: def read_sql_table(
      table_name: str,
      con: str,
      schema: Optional[str] = None,
      index_col: Optional[Union[str, List[str]]] = None,
      columns: Optional[Union[str, List[str]]] = None,
      **options: Any,
  ) -> DataFrame:
      """
      Read SQL database table into a DataFrame.
  
      Given a table name and a JDBC URI, returns a DataFrame.
  
      Parameters
      ----------
      table_name : str
          Name of SQL table in database.
      con : str
          A JDBC URI could be provided as str.
  
          .. note:: The URI must be JDBC URI instead of Python's database URI.
  
      schema : str, default None
          Name of SQL schema in database to query (if database flavor
          supports this). Uses default schema if None (default).
      index_col : str or list of str, optional, default: None
          Column(s) to set as index(MultiIndex).
      columns : list, default None
          List of column names to select from SQL table.
      options : dict
          All other options passed directly into Spark's JDBC data source.
  
      Returns
      -------
      DataFrame
          A SQL table is returned as two-dimensional data structure with labeled
          axes.
  
      See Also
      --------
      read_sql_query : Read SQL query into a DataFrame.
      read_sql : Read SQL query or database table into a DataFrame.
  
      Examples
      --------
      >>> ps.read_sql_table('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")
  
      reader = default_session().read
      reader.option("dbtable", table_name)
      reader.option("url", con)
      if schema is not None:
          reader.schema(schema)
      reader.options(**options)
      sdf = reader.format("jdbc").load()
      index_spark_columns, index_names = _get_index_map(sdf, index_col)
      psdf: DataFrame = DataFrame(
          InternalFrame(
              spark_frame=sdf,
              index_spark_columns=index_spark_columns,
              index_names=index_names,
          )
      )
      if columns is not None:
          if isinstance(columns, str):
              columns = [columns]
          psdf = psdf[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 12 for calling a global function with example usage: read_sql_table(table_name, con, schema, index_col, columns) and returns: psdf