code: def retrieval_row(
      df: "DataFrame", indexer: Union["DTensor", "DataFrame"]
  ):
      if isinstance(indexer, DTensor) and indexer.shape[1] != 1:
          raise ValueError(
              "Row indexing by DTensor should have only one column filling with True/False"
          )
      elif isinstance(indexer, DataFrame):
          operable_field_len = len(
              indexer.data_manager.infer_operable_field_names()
          )
          if operable_field_len != 1:
              raise ValueError(
                  "Row indexing by DataFrame should have only one column filling with True/False"
              )
  
      data_manager = df.data_manager.duplicate()
  
      def _block_counter(kvs, value_type="tensor"):
          size = 0
          first_block_id = None
          for k, value in kvs:
              if first_block_id is None:
                  first_block_id = k
  
              if value_type == "tensor":
                  size += value.sum().item()
              else:
                  size += len(value[0])
  
          return first_block_id, size
  
      if isinstance(indexer, DataFrame):
          _block_counter_func = functools.partial(
              _block_counter, value_type="dataframe"
          )
          block_info = sorted(
              [
                  summary[1]
                  for summary in indexer.block_table.applyPartitions(
                      _block_counter_func
                  ).collect()
              ]
          )
      else:
          block_info = sorted(
              [
                  summary[1]
                  for summary in indexer.shardings._data.applyPartitions(
                      _block_counter
                  ).collect()
              ]
          )
      block_order_mappings = dict()
      start_index = 0
      acc_block_num = 0
      for block_id, block_size in block_info:
          block_num = (
              block_size + data_manager.block_row_size - 1
          ) // data_manager.block_row_size
          block_order_mappings[block_id] = dict(
              start_index=start_index,
              end_index=start_index + block_size - 1,
              start_block_id=acc_block_num,
              end_block_id=acc_block_num + block_num - 1,
          )
          start_index += block_size
          acc_block_num += block_num
  
      if start_index == 0:
          return df.empty_frame()
  
      if isinstance(indexer, DataFrame):
          bid = indexer.data_manager.infer_operable_blocks()[0]
          block_table = df.block_table.join(
              indexer.block_table, lambda v1, v2: (v1, v2[bid])
          )
      else:
          block_table = df.block_table.join(
              indexer.shardings._data, lambda v1, v2: (v1, v2)
          )
  
      _balance_block_func = functools.partial(
          _balance_blocks_with_index,
          partition_order_mappings=block_order_mappings,
          data_manager=data_manager,
      )
      block_table = block_table.mapPartitions(
          _balance_block_func, use_previous_behavior=False
      )
      block_table, data_manager = compress_blocks(
          block_table, data_manager
      )
      partition_order_mappings = (
          get_partition_order_mappings_by_block_table(
              block_table, data_manager.block_row_size
          )
      )
  
      return DataFrame(
          df._ctx, block_table, partition_order_mappings, data_manager
      )
  
imports: import copy
  import functools
  from typing import List, Union
  import torch
  from sklearn.utils import resample
  from .._dataframe import DataFrame
  from ..manager.data_manager import DataManager
  from ..manager.block_manager import Block
  from ._compress_block import compress_blocks
  from ._indexer import get_partition_order_by_raw_table, get_partition_order_mappings_by_block_table
  from ._promote_types import promote_partial_block_types
  from ._set_item import set_item
  from fate.arch.tensor import DTensor
label_desc: Use image node_id 5 for calling a global function with example usage: retrieval_row(df, indexer) and returns: DataFrame, first_block_id,  size, df