code: def forward(
      self, preds: Dict[str, torch.Tensor], feature_name: str
  ) -> FeaturePostProcessingOutputDict:
      pred_predictions = output_feature_utils.get_output_feature_tensor(
          preds, feature_name, self.predictions_key
      )
      pred_probabilities = (
          output_feature_utils.get_output_feature_tensor(
              preds, feature_name, self.probabilities_key
          )
      )
  
      predictions: List[List[str]] = []
      for sequence in pred_predictions:
          sequence_predictions: List[str] = []
          for i in range(self.max_sequence_length):
              unit_id = int(sequence[i].item())
              if unit_id < len(self.idx2str):
                  unit_prediction = self.idx2str[unit_id]
              else:
                  unit_prediction = self.unknown_symbol
              sequence_predictions.append(unit_prediction)
          predictions.append(sequence_predictions)
  
      probabilities, _ = torch.max(pred_probabilities, dim=-1)
      probability = torch.sum(torch.log(probabilities), dim=-1)
  
      return {
          self.predictions_key: predictions,
          self.probabilities_key: probabilities,
          self.probability_key: probability,
      }
  
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 2 for calling the _SequencePostprocessing obj's underlying member method code with example usage: obj.forward(preds, feature_name) and returns: dict