code: def _compute_cover(
      state: Dict[str, jnp.ndarray], assignment: jnp.ndarray
  ) -> Tuple[jnp.ndarray, jnp.ndarray]:
      """Computes a cover for the bipartite graph.
  
      We compute a cover using the construction provided at
      https://en.wikipedia.org/wiki/K%C5%91nig%27s_theorem_(graph_theory)#Proof
      which uses the outcome from the alternating path search.
  
      Args:
        state: A state dict, which represents the outcome of running an augmenting
          path search on the graph given the assignment.
        assignment: An optional bool [b, n, m] array, where each element of the
          inner matrix represents whether the worker has been matched to the job.
          This may be a partial assignment. If specified, this assignment will be
          used to seed the iterative algorithm.
  
      Returns:
        Row and column covers for the bipartite graph: workers_cover is a boolean
        array of shape [b, n, 1] and jobs_cover is a array array of shape
        [b, 1, m].
      """
      assigned_workers = jnp.any(assignment, axis=2, keepdims=True)
      assigned_jobs = jnp.any(assignment, axis=1, keepdims=True)
  
      workers_cover = jnp.logical_and(
          assigned_workers, state["workers"] <= 0
      )
      jobs_cover = jnp.logical_and(assigned_jobs, state["jobs"] > 0)
  
      return workers_cover, jobs_cover
  
file_path: scenic/scenic/model_lib/matchers/hungarian_cover.py
imports: from typing import Tuple, Optional, Dict
  import jax
  import jax.numpy
label_desc: Use image node_id 6 for calling a global function with example usage: _compute_cover(state, assignment) and returns: workers_cover,  jobs_cover