code: def postprocess_predictions(
      self,
      result,
      metadata,
  ):
      predictions_col = f"{self.feature_name}_{PREDICTIONS}"
      lengths_col = f"{self.feature_name}_{LENGTHS}"
      if predictions_col in result:
          if "idx2str" in metadata:
  
              def idx2str(row):
                  pred = row[predictions_col]
                  length = metadata["max_sequence_length"]
                  return [
                      metadata["idx2str"][token]
                      if token < len(metadata["idx2str"])
                      else UNKNOWN_SYMBOL
                      for token in [pred[i] for i in range(length)]
                  ]
  
              result[predictions_col] = result.apply(idx2str, axis=1)
  
      last_preds_col = f"{self.feature_name}_{LAST_PREDICTIONS}"
      if last_preds_col in result:
          if "idx2str" in metadata:
  
              def last_idx2str(last_pred):
                  if last_pred < len(metadata["idx2str"]):
                      return metadata["idx2str"][last_pred]
                  return UNKNOWN_SYMBOL
  
              result[last_preds_col] = result[last_preds_col].map(
                  last_idx2str
              )
  
      probs_col = f"{self.feature_name}_{PROBABILITIES}"
      prob_col = f"{self.feature_name}_{PROBABILITY}"
      if probs_col in result:
          # currently does not return full probabilties because usually it is huge:
          # dataset x length x classes
          # TODO: add a mechanism for letting the user decide to save it
          result[probs_col] = result[probs_col].map(
              compute_token_probabilities
          )
          result[prob_col] = result[probs_col].map(
              partial(
                  compute_sequence_probability,
                  max_sequence_length=metadata["max_sequence_length"],
                  return_log_prob=True,
              )
          )
  
      if lengths_col in result:
          del result[lengths_col]
  
      return result
  
imports: import logging
  from functools import partial
  from typing import Dict, List, Union
  import numpy
  import torch
  from ludwig.constants import COLUMN, LAST_PREDICTIONS, LENGTHS, NAME, PREDICTIONS, PROBABILITIES, PROBABILITY, PROC_COLUMN, SEQUENCE
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature, OutputFeature, PredictModule
  from ludwig.features.feature_utils import compute_sequence_probability, compute_token_probabilities
  from ludwig.schema.features.sequence_feature import SequenceInputFeatureConfig, SequenceOutputFeatureConfig
  from ludwig.types import FeatureMetadataDict, FeaturePostProcessingOutputDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils import output_feature_utils
  from ludwig.utils.math_utils import softmax
  from ludwig.utils.strings_utils import build_sequence_matrix, create_vocabulary, SpecialSymbol, START_SYMBOL, STOP_SYMBOL, UNKNOWN_SYMBOL
  from ludwig.utils.tokenizers import get_tokenizer_from_registry
  from ludwig.utils.types import TorchscriptPreprocessingInput
label_desc: Use image node_id 10 for calling the SequenceOutputFeature obj's underlying member method code with example usage: obj.postprocess_predictions(result, metadata) and returns: result, unknown, UNKNOWN_SYMBOL, metadata