code: def __init__(
      self,
      output_feature_config: Union[SetOutputFeatureConfig, Dict],
      output_features: Dict[str, OutputFeature],
      **kwargs,
  ):
      self.threshold = output_feature_config.threshold
      super().__init__(output_feature_config, output_features, **kwargs)
      self.decoder_obj = self.initialize_decoder(
          output_feature_config.decoder
      )
      self._setup_loss()
      self._setup_metrics()
  
imports: import logging
  from typing import Any, Dict, List, Union
  import numpy
  import torch
  from ludwig.constants import COLUMN, HIDDEN, LOGITS, NAME, PREDICTIONS, PROBABILITIES, PROC_COLUMN, SET
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature, OutputFeature, PredictModule
  from ludwig.features.feature_utils import set_str_to_idx
  from ludwig.schema.features.set_feature import SetInputFeatureConfig, SetOutputFeatureConfig
  from ludwig.types import FeatureMetadataDict, FeaturePostProcessingOutputDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils import output_feature_utils
  from ludwig.utils.strings_utils import create_vocabulary, UNKNOWN_SYMBOL
  from ludwig.utils.tokenizers import get_tokenizer_from_registry, TORCHSCRIPT_COMPATIBLE_TOKENIZERS
  from ludwig.utils.types import TorchscriptPreprocessingInput
label_desc: Use image node_id 1 to create a new SetOutputFeature object from inherited base classes: SetFeatureMixin, OutputFeature with example: obj = SetOutputFeature(output_feature_config, output_features)