class_docstr: Really simple unweighted directed graph data structure to track dependencies.
  
  The API is pretty much the same as networkx so if you add something just
  copy their API.
class_docstr_tok: ["Really","simple","unweighted","directed","graph","data","structure","to","track","dependencies",".","The","API","is","pretty","much","the","same","as","networkx","so","if","you","add","something","just","copy","their","API","."]
docstr: Returns an iterator over successor nodes of n.
docstr_tok: ["Returns","an","iterator","over","successor","nodes","of","n","."]
code: def successors(self, n):
      """Returns an iterator over successor nodes of n."""
      try:
          return iter(self._succ[n])
      except KeyError as e:
          raise ValueError(
              f"The node {n} is not in the digraph."
          ) from e
  
file_path: pytorch/torch/package/_digraph.py
imports: from collections import deque
  from typing import List, Set
label_desc: Use image node_id 4 for calling the DiGraph obj's underlying member method code with example usage: obj.successors(n) and returns: iter