code: def _greedy_assignment(adj_matrix: jnp.ndarray) -> jnp.ndarray:
      """Greedily assigns workers to jobs based on an adjaceny matrix.
  
      Starting with an adjacency matrix representing the available connections in
      the bi-partite graph, this function greedily chooses elements such that each
      worker is matched to at most one job (or each job is assigned to at most one
      worker). Note, if the adjacency matrix has no available values for a
      particular row/column, the corresponding job/worker may go unassigned.
  
      Args:
        adj_matrix: A bool [b, n, m] array, where each element of the inner matrix
          represents whether the worker (row) can be matched to the job (column).
  
      Returns:
        A bool [b, n, m] array, where each element of the inner matrix represents
        whether the worker has been matched to the job. Each row and column can have
        at most one true element. Some of the rows and columns may not be matched.
      """
      b, _, m = adj_matrix.shape
  
      # To (n, b, m)
      adj_matrix = jnp.transpose(adj_matrix, axes=(1, 0, 2))
  
      # Iteratively assign each row using jax.lax.scan. Intuitively, this is a loop
      # over rows, where we incrementally assign each row.
      def _assign_row(col_assigned, row_adj):
          # Viable candidates cannot already be assigned to another job.
          candidates = jnp.logical_and(
              row_adj, jnp.logical_not(col_assigned)
          )
  
          # Deterministically assign to the candidates of the highest index count.
          max_candidate_idx = jnp.argmax(candidates, axis=1)
          candidates_indicator = jax.nn.one_hot(
              max_candidate_idx, m, dtype=jnp.bool_
          )
          candidates_indicator = jnp.logical_and(
              candidates_indicator, candidates
          )
  
          # Make assignment to the column.
          col_assigned = jnp.logical_or(
              candidates_indicator, col_assigned
          )
  
          return col_assigned, candidates_indicator
  
      # Store the elements assigned to each column to update each iteration.
      col_assigned = jnp.zeros((b, m), dtype=jnp.bool_)
      _, assignment = jax.lax.scan(
          _assign_row, col_assigned, adj_matrix
      )
  
      # To (b, n, m)
      assignment = jnp.transpose(assignment, axes=(1, 0, 2))
      return assignment
  
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 2 for calling a global function with example usage: _greedy_assignment(adj_matrix) and returns: assignment, col_assigned,  candidates_indicator