code: def execute(cls, ctx, op: "DataFrameReadSQL"):
      import sqlalchemy as sa
  
      def _adapt_datetime(dt):
          if isinstance(dt, np.datetime64):
              return dt.astype("<M8[ms]").astype(datetime.datetime)
          elif isinstance(dt, pd.Timestamp):
              return dt.to_pydatetime()
          return dt
  
      out = op.outputs[0]
  
      patch_sa_engine_execute()
      engine = sa.create_engine(op.con, **(op.engine_kwargs or dict()))
      try:
          selectable = op._get_selectable(engine)
  
          columns = [selectable.columns[col] for col in op.columns]
          column_names = set(op.columns)
          if op.index_col:
              for icol in op.index_col:
                  if icol not in column_names:
                      columns.append(selectable.columns[icol])
  
          # convert to python timestamp in case np / pd time types not handled
          op.low_limit = _adapt_datetime(op.low_limit)
          op.high_limit = _adapt_datetime(op.high_limit)
  
          query = sa.sql.select(*columns)
          if op.method == "partition":
              part_col = selectable.columns[op.partition_col]
              if op.left_end:
                  query = query.where(part_col < op.high_limit)
              elif op.right_end:
                  query = query.where(part_col >= op.low_limit)
              else:
                  query = query.where(
                      (part_col >= op.low_limit)
                      & (part_col < op.high_limit)
                  )
  
          if (
              hasattr(selectable, "primary_key")
              and len(selectable.primary_key) > 0
          ):
              # if table has primary key, sort as the order
              query = query.order_by(*list(selectable.primary_key))
          elif op.index_col:
              # if no primary key, sort as the index_col
              query = query.order_by(
                  *[selectable.columns[col] for col in op.index_col]
              )
          else:
              # at last, we sort by all the columns
              query = query.order_by(*columns)
  
          if op.method == "offset":
              query = query.limit(out.shape[0])
              if op.offset > 0:
                  query = query.offset(op.offset)
  
          if op.nrows is not None:
              query = query.limit(op.nrows)
  
          df = pd.read_sql(
              query,
              engine,
              index_col=op.index_col,
              coerce_float=op.coerce_float,
              parse_dates=op.parse_dates,
          )
          if (
              op.method == "offset"
              and op.index_col is None
              and op.offset > 0
          ):
              index = pd.RangeIndex(op.offset, op.offset + out.shape[0])
              if op.nrows is not None:
                  index = index[: op.nrows]
              df.index = index
  
          use_arrow_dtype = op.use_arrow_dtype
          if use_arrow_dtype is None:
              use_arrow_dtype = options.dataframe.use_arrow_dtype
          if use_arrow_dtype:
              dtypes = to_arrow_dtypes(df.dtypes, test_df=df)
              for i in range(len(dtypes)):
                  dtype = dtypes.iloc[i]
                  if isinstance(dtype, ArrowStringDtype):
                      df.iloc[:, i] = df.iloc[:, i].astype(dtype)
  
          if out.ndim == 2:
              ctx[out.key] = df
          else:
              # this happens when column pruning results in one single series
              ctx[out.key] = df.iloc[:, 0]
      finally:
          engine.dispose()
  
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 11 for calling the DataFrameReadSQL obj's underlying member method code with example usage: obj.execute(cls, ctx, op) and returns: dt, dt, dt