code: def get_output_dtype(cls):
      return torch.int32
  
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 2 for calling the TextOutputFeature obj's underlying member method code with example usage: obj.get_output_dtype(cls) and returns: torch