code: def postprocess_predictions(
      self,
      result,
      metadata,
  ):
      # todo: refactor to reuse SequenceOutputFeature.postprocess_predictions
      predictions_col = f"{self.feature_name}_{PREDICTIONS}"
  
      tokenizer = None
      if metadata["preprocessing"]["tokenizer"] == "hf_tokenizer":
          tokenizer = get_tokenizer(
              metadata["preprocessing"]["tokenizer"],
              metadata["preprocessing"]["vocab_file"],
              metadata["preprocessing"][
                  "pretrained_model_name_or_path"
              ],
          )
  
      if predictions_col in result:
          token_col = result[predictions_col]
  
          def idx2str(pred):
              if tokenizer is None:
                  return [
                      metadata["idx2str"][token]
                      if token < len(metadata["idx2str"])
                      else UNKNOWN_SYMBOL
                      for token in pred
                  ]
              return tokenizer.tokenizer.batch_decode(
                  pred, skip_special_tokens=True
              )
  
          result[predictions_col] = token_col.map(idx2str)
  
          # Add additional response column that represents the predicted text output
          # as a single string instead of a list of tokens.
          def idx2response(pred):
              if tokenizer is None:
                  # This works because we treat each word as a token.
                  return " ".join(
                      [
                          metadata["idx2str"][token]
                          if token < len(metadata["idx2str"])
                          else UNKNOWN_SYMBOL
                          for token in pred
                      ]
                  )
              return tokenizer.tokenizer.batch_decode(
                  [pred], skip_special_tokens=True
              )
  
          result[f"{self.feature_name}_response"] = token_col.map(
              idx2response
          )
  
      last_preds_col = f"{self.feature_name}_{LAST_PREDICTIONS}"
      if last_preds_col in result:
  
          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}"
  
      # "Summarizes" the `result`'s probability-related output:
      # - result[probs_col]:
      #       Each row is now a list of "max" probabilities. Each element is the probability of the argmax token for
      #       the given time step.
      #
      #       Note that we intentionally do not return full list of probabilties for each time step because the output
      #       of postprocess_predictions is saved to disk and the full probability distribution can be huge,
      #       especially for large vocab sizes:
      #           dataset_size x sequence_length x vocab_size
      #
      #       TODO: Add a mechanism that lets the user save the full probability distribution if they want.
      # - result[prob_col]:
      #       Each row is the overall probability of the sequence. This is the product of the max probabilities over
      #       all time steps.
      if probs_col in result:
          # result[probs_col]: From PredictModule, each row has a list of size (sequence_length) of a list of
          # probabiltiies of (vocab_size). compute_token_probabilities gets the maximum probability per timestep.
          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,
              ),
          )
  
      lengths_col = f"{self.feature_name}_{LENGTHS}"
      if lengths_col in result:
          del result[lengths_col]
  
      return result
  
imports: import logging
  from functools import partial
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from torch import Tensor
  from transformers import PreTrainedTokenizer
  from ludwig.constants import COLUMN, IGNORE_INDEX_TOKEN_ID, LAST_PREDICTIONS, LENGTHS, NAME, PREDICTIONS, PREPROCESSING, PROBABILITIES, PROBABILITY, PROC_COLUMN, RESPONSE, TEXT
  from ludwig.features.base_feature import BaseFeatureMixin, OutputFeature
  from ludwig.features.feature_utils import compute_sequence_probability, compute_token_probabilities
  from ludwig.features.sequence_feature import _SequencePostprocessing, _SequencePreprocessing, SequenceInputFeature, SequenceOutputFeature
  from ludwig.modules.metric_registry import get_metric_tensor_input
  from ludwig.schema.features.text_feature import TextInputFeatureConfig, TextOutputFeatureConfig
  from ludwig.types import FeatureMetadataDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils.math_utils import softmax
  from ludwig.utils.strings_utils import build_sequence_matrix, create_vocabulary, get_tokenizer, SpecialSymbol, UNKNOWN_SYMBOL, Vocabulary
label_desc: Use image node_id 7 for calling the TextOutputFeature obj's underlying member method code with example usage: obj.postprocess_predictions(result, metadata) and returns: result, tokenizer, tokenizer, UNKNOWN_SYMBOL, unknown, str, metadata