code: def forward(
      self, inputs: Dict[str, torch.Tensor], feature_name: str
  ) -> Dict[str, torch.Tensor]:
      logits = output_feature_utils.get_output_feature_tensor(
          inputs, feature_name, self.logits_key
      )
      probabilities = torch.softmax(logits, -1)
      predictions = torch.argmax(logits, -1)
  
      # predictions: [batch_size, sequence_length]
      # probabilities: [batch_size, sequence_length, vocab_size]
      # logits: [batch_size, sequence_length, vocab_size]
      return {
          self.predictions_key: predictions,
          self.probabilities_key: probabilities,
          self.logits_key: logits,
      }
  
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 1 for calling the _SequencePredict obj's underlying member method code with example usage: obj.forward(inputs, feature_name) and returns: dict