code: def InferDestinationFromSourceRanks(
      src_ranks: List[int],
      construct_adjacency_matrix: bool = False,
  ) -> Union[List[int], np.array]:
      """Infer the destination ranks from source ranks. This is collective communication call.
  
      Args:
          src_ranks: A list of destination ranks.
          construct_adjacency_matrix: If true, adjacency matrix will be return instead.
              Element w_{ij} represents the weights sending from node i to node j.
              We use column normalized style, i.e. the sum of receiving weight is 1.
  
      Raises:
          ValueError: If dst_ranks or src_ranks does not contain integer from 0 to size-1.
  
      Returns:
          If construct_adjacency_matrix is false, returns the destination ranks list.
          If construct_adjacency_matrix is true, returns the the sodestinationrce ranks
          list and a 2-D numpy array.
      """
      is_valid, error_msg = _check_ranks(
          src_ranks, bf.rank(), bf.size()
      )
      assert is_valid, f"The format of src_ranks is wrong: {error_msg}"
      return _infer_topo(
          src_ranks,
          transpose=True,
          construct_adjacency_matrix=construct_adjacency_matrix,
      )
  
file_path: bluefog/bluefog/torch/topology_util.py
imports: from typing import Any, List, Optional, Tuple, Union
  import collections
  import numpy
  import torch
  import bluefog.torch
label_desc: Use image node_id 3 for calling a global function with example usage: InferDestinationFromSourceRanks(src_ranks, construct_adjacency_matrix) and returns: _infer_topo