parameters: (state)
return_statement: return _step4
docstring: Add the value found in Step 4 to every element of each covered row,
      and subtract it from every element of each uncovered column.
      Return to Step 4 without altering any stars, primes, or covered lines.
docstring_summary: Add the value found in Step 4 to every element of each covered row,
      and subtract it from every element of each uncovered column.
      Return to Step 4 without altering any stars, primes, or covered lines.
docstring_tokens: Add the value found in Step 4 to every element of each covered row and subtract it from every element of each uncovered column . Return to Step 4 without altering any stars primes or covered lines .
function: def _step6(state):
      """
      Add the value found in Step 4 to every element of each covered row,
      and subtract it from every element of each uncovered column.
      Return to Step 4 without altering any stars, primes, or covered lines.
      """
      # the smallest uncovered value in the matrix
      if np.any(state.row_uncovered) and np.any(state.col_uncovered):
          minval = np.min(state.C[state.row_uncovered], axis=0)
          minval = np.min(minval[state.col_uncovered])
          state.C[np.logical_not(state.row_uncovered)] += minval
          state.C[:, state.col_uncovered] -= minval
      return _step4
function_tokens: def _step6 ( state ) : # the smallest uncovered value in the matrix if np . any ( state . row_uncovered ) and np . any ( state . col_uncovered ) : minval = np . min ( state . C [ state . row_uncovered ] , axis = 0 ) minval = np . min ( minval [ state . col_uncovered ] ) state . C [ np . logical_not ( state . row_uncovered ) ] += minval state . C [ : , state . col_uncovered ] -= minval return _step4