The implementation of breadth-first search that I converged to after X0 job interviews.

bfs.py

from typing import Dict, Set, Hashable

def shortest_path_bfs(graph: Dict[Hashable, Set[Hashable]] root: Hashable
                     ) -> Dict[Hashable, int]:
  """Finds the shortest path between all nodes in |graph| time.
  
  Args: 
    graph: A dict mapping nodes to connected nodes.
    root: The node our search begins at.
    
  Returns:
   A dictionary containing the shortest paths from root to each node.
  """
  queue = [root]
  visited = set()
  distances = {node: float("inf") for node in graph}
  distances[root] = 0
  while queue:
    curr = queue.pop()
    queue.extend(list(graph[curr] - visited))
    for neighbour in graph[curr]:
      distances[neighbour] = distances[curr] + 1
  return distances

graph = {0: [1, 2, 3],
         1: [0, 2],
         2: [3],
         3: []}

assert shortest_paths(graph, 0) == {0: 0, 1: 1, 2: 1, 3: 1}