code: def __call__(self, test_rows, chunk_size):
      import sqlalchemy as sa
      from sqlalchemy.sql import elements
  
      with create_sa_connection(
          self.con, **(self.engine_kwargs or dict())
      ) as con:
          self.con = str(con.engine.url)
          selectable = self._get_selectable(con)
  
          # process index_col
          index_col = self.index_col
          if index_col is not None:
              if not isinstance(index_col, (list, tuple)):
                  index_col = (index_col,)
              new_index_col = []
              for col in index_col:
                  if isinstance(col, (sa.Column, elements.Label)):
                      new_index_col.append(col.name)
                  elif isinstance(col, str):
                      new_index_col.append(col)
                  elif col is not None:
                      raise TypeError(
                          f"unknown index_col type: {type(col)}"
                      )
              self.index_col = new_index_col
  
          # process columns
          columns = self.columns or []
          new_columns = []
          for col in columns:
              if isinstance(col, str):
                  new_columns.append(col)
              else:
                  new_columns.append(col.name)
          self.columns = new_columns
  
          if self.columns:
              collect_cols = self.columns + (self.index_col or [])
          else:
              collect_cols = []
          test_df, shape = self._collect_info(
              con, selectable, collect_cols, test_rows
          )
  
          # reconstruct selectable using known column names
          if not collect_cols:
              self.columns = list(test_df.columns)
              if self.selectable is not None:
                  self.selectable = None
                  self._get_selectable(
                      con, columns=self.columns + (self.index_col or [])
                  )
  
          if self.method == "partition":
              if (
                  not self.index_col
                  or self.partition_col not in self.index_col
              ):
                  part_frame = test_df
              else:
                  part_frame = test_df.index.to_frame()
  
              if not issubclass(
                  part_frame[self.partition_col].dtype.type,
                  (np.number, np.datetime64),
              ):
                  raise TypeError(
                      "Type of partition column should be numeric or datetime, "
                      f"now it is {test_df[self.partition_col].dtype}"
                  )
  
          if isinstance(test_df.index, pd.RangeIndex):
              index_value = parse_index(
                  pd.RangeIndex(
                      shape[0] if not np.isnan(shape[0]) else -1
                  ),
                  str(selectable),
                  self.con,
              )
          else:
              index_value = parse_index(test_df.index)
  
          columns_value = parse_index(test_df.columns, store_data=True)
  
          dtypes = test_df.dtypes
          use_arrow_dtype = self.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(dtypes, test_df=test_df)
  
          return self.new_dataframe(
              None,
              shape=shape,
              dtypes=dtypes,
              index_value=index_value,
              columns_value=columns_value,
              raw_chunk_size=chunk_size,
          )
  
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 5 for calling the DataFrameReadSQL obj's underlying member method code with example usage: obj.__call__(test_rows, chunk_size) and returns: self