code: def __init__(
      self,
      output_feature_config: Union[VectorOutputFeatureConfig, Dict],
      output_features: Dict[str, OutputFeature],
      **kwargs,
  ):
      self.vector_size = output_feature_config.vector_size
      super().__init__(output_feature_config, output_features, **kwargs)
      output_feature_config.decoder.output_size = self.vector_size
  
      self.decoder_obj = self.initialize_decoder(
          output_feature_config.decoder
      )
      self._setup_loss()
      self._setup_metrics()
  
imports: import logging
  from typing import Dict, List, Union
  import numpy
  import torch
  from ludwig.constants import COLUMN, HIDDEN, LOGITS, NAME, PREDICTIONS, PROC_COLUMN, VECTOR
  from ludwig.features.base_feature import InputFeature, OutputFeature, PredictModule
  from ludwig.schema.features.vector_feature import VectorInputFeatureConfig, VectorOutputFeatureConfig
  from ludwig.types import FeatureMetadataDict, FeaturePostProcessingOutputDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils import output_feature_utils
  from ludwig.utils.types import TorchscriptPreprocessingInput
label_desc: Use image node_id 1 to create a new VectorOutputFeature object from inherited base classes: VectorFeatureMixin, OutputFeature with example: obj = VectorOutputFeature(output_feature_config, output_features)