code: def read_sql(
      sql,
      con,
      index_col=None,
      coerce_float=True,
      params=None,
      parse_dates=None,
      columns=None,
      chunksize=None,
      test_rows=5,
      chunk_size=None,
      engine_kwargs=None,
      incremental_index=True,
      partition_col=None,
      num_partitions=None,
      low_limit=None,
      high_limit=None,
  ):
      """
      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.
  
      Parameters
      ----------
      sql : str or SQLAlchemy Selectable (select or text object)
          SQL query to be executed or a table name.
      con : SQLAlchemy connectable (engine/connection) or database str URI
          or DBAPI2 connection (fallback mode)'
  
          Using SQLAlchemy makes it possible to use any DB supported by that
          library. If a DBAPI2 object, only sqlite3 is supported. The user is responsible
          for engine disposal and connection closure for the SQLAlchemy connectable. See
          `here <https://docs.sqlalchemy.org/en/13/core/connections.html>`_
      index_col : str or list of strings, optional, default: None
          Column(s) to set as index(MultiIndex).
      coerce_float : bool, default True
          Attempts to convert values of non-string, non-numeric objects (like
          decimal.Decimal) to floating point, useful for SQL result sets.
      params : list, tuple or dict, optional, default: None
          List of parameters to pass to execute method.  The syntax used
          to pass parameters is database driver dependent. Check your
          database driver documentation for which of the five syntax styles,
          described in PEP 249's paramstyle, is supported.
          Eg. for psycopg2, uses %(name)s so use params={'name' : 'value'}.
      parse_dates : list or dict, default: None
          - List of column names to parse as dates.
          - Dict of ``{column_name: format string}`` where format string is
            strftime compatible in case of parsing string times, or is one of
            (D, s, ns, ms, us) in case of parsing integer timestamps.
          - Dict of ``{column_name: arg dict}``, where the arg dict corresponds
            to the keyword arguments of :func:`pandas.to_datetime`
            Especially useful with databases without native Datetime support,
            such as SQLite.
      columns : list, default: None
          List of column names to select from SQL table (only used when reading
          a table).
      chunksize : int, default None
          If specified, return an iterator where `chunksize` is the number of rows
          to include in each chunk. Note that this argument is only kept for
          compatibility. If a non-none value passed, an error will be reported.
      test_rows: int, default 5
          The number of rows to fetch for inferring dtypes.
      chunk_size: : int or tuple of ints, optional
          Specifies chunk size for each dimension.
      engine_kwargs: dict, default None
          Extra kwargs to pass to sqlalchemy.create_engine
      incremental_index: bool, default True
          If index_col not specified, ensure range index incremental,
          gain a slightly better performance if setting False.
      partition_col : str, default None
          Specify name of the column to split the result of the query. If
          specified, the range ``[low_limit, high_limit]`` will be divided
          into ``n_partitions`` chunks with equal lengths. We do not
          guarantee the sizes of chunks be equal. When the value is None,
          ``OFFSET`` and ``LIMIT`` clauses will be used to cut the result
          of the query.
      num_partitions : int, default None
          The number of chunks to divide the result of the query into,
          when ``partition_col`` is specified.
      low_limit : default None
          The lower bound of the range of column ``partition_col``. If not
          specified, a query will be executed to query the minimum of
          the column.
      high_limit : default None
          The higher bound of the range of column ``partition_col``. If not
          specified, a query will be executed to query the maximum of
          the column.
  
      Returns
      -------
      DataFrame
  
      See Also
      --------
      read_sql_table : Read SQL database table into a DataFrame.
      read_sql_query : Read SQL query into a DataFrame.
      """
      return _read_sql(
          table_or_sql=sql,
          con=con,
          index_col=index_col,
          coerce_float=coerce_float,
          params=params,
          parse_dates=parse_dates,
          columns=columns,
          engine_kwargs=engine_kwargs,
          incremental_index=incremental_index,
          chunksize=chunksize,
          test_rows=test_rows,
          chunk_size=chunk_size,
          partition_col=partition_col,
          num_partitions=num_partitions,
          low_limit=low_limit,
          high_limit=high_limit,
      )
  
imports: import binascii
  import datetime
  import pickle
  import uuid
  from typing import List, Union
  import numpy
  import pandas
  import cloudpickle
  from ...None import opcodes
  from ...config import options
  from ...core.context import Context
  from ...core.operand import OperatorLogicKeyGeneratorMixin
  from ...serialization.serializables import StringField, AnyField, BoolField, ListField, Int64Field, Float64Field, BytesField
  from ...tensor.utils import normalize_chunk_sizes
  from ...typing import OperandType, TileableType
  from ..arrays import ArrowStringDtype
  from ..utils import parse_index, create_sa_connection, to_arrow_dtypes, patch_sa_engine_execute
  from .core import IncrementalIndexDatasource, ColumnPruneSupportedDataSourceMixin, IncrementalIndexDataSourceMixin
label_desc: Use image node_id 2 for calling a global function with example usage: read_sql(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize, test_rows, chunk_size, engine_kwargs, incremental_index, partition_col, num_partitions, low_limit, high_limit) and returns: _read_sql