parameters: (score, transition_params)
return_statement: return viterbi, viterbi_score
docstring: Decode the highest scoring sequence of tags outside of TensorFlow.
  
    This should only be used at test time.
  
    Args:
      score: A [seq_len, num_tags] matrix of unary potentials.
      transition_params: A [num_tags, num_tags] matrix of binary potentials.
  
    Returns:
      viterbi: A [seq_len] list of integers containing the highest scoring tag
          indices.
      viterbi_score: A float containing the score for the Viterbi sequence.
docstring_summary: Decode the highest scoring sequence of tags outside of TensorFlow.
docstring_tokens: Decode the highest scoring sequence of tags outside of TensorFlow .
function: def viterbi_decode(score, transition_params):
    """Decode the highest scoring sequence of tags outside of TensorFlow.
  
    This should only be used at test time.
  
    Args:
      score: A [seq_len, num_tags] matrix of unary potentials.
      transition_params: A [num_tags, num_tags] matrix of binary potentials.
  
    Returns:
      viterbi: A [seq_len] list of integers containing the highest scoring tag
          indices.
      viterbi_score: A float containing the score for the Viterbi sequence.
    """
    trellis = np.zeros_like(score)
    backpointers = np.zeros_like(score, dtype=np.int32)
    trellis[0] = score[0]
  
    for t in range(1, score.shape[0]):
      v = np.expand_dims(trellis[t - 1], 1) + transition_params
      trellis[t] = score[t] + np.max(v, 0)
      backpointers[t] = np.argmax(v, 0)
  
    viterbi = [np.argmax(trellis[-1])]
    for bp in reversed(backpointers[1:]):
      viterbi.append(bp[viterbi[-1]])
    viterbi.reverse()
  
    viterbi_score = np.max(trellis[-1])
    return viterbi, viterbi_score
function_tokens: def viterbi_decode ( score , transition_params ) : trellis = np . zeros_like ( score ) backpointers = np . zeros_like ( score , dtype = np . int32 ) trellis [ 0 ] = score [ 0 ] for t in range ( 1 , score . shape [ 0 ] ) : v = np . expand_dims ( trellis [ t - 1 ] , 1 ) + transition_params trellis [ t ] = score [ t ] + np . max ( v , 0 ) backpointers [ t ] = np . argmax ( v , 0 ) viterbi = [ np . argmax ( trellis [ - 1 ] ) ] for bp in reversed ( backpointers [ 1 : ] ) : viterbi . append ( bp [ viterbi [ - 1 ] ] ) viterbi . reverse ( ) viterbi_score = np . max ( trellis [ - 1 ] ) return viterbi , viterbi_score