code: def postprocess_predictions(
      self,
      result,
      metadata,
  ):
      predictions_col = f"{self.feature_name}_{PREDICTIONS}"
      if predictions_col in result:
          result[predictions_col] = result[predictions_col].map(
              lambda pred: pred.tolist()
          )
      return result
  
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 11 for calling the VectorOutputFeature obj's underlying member method code with example usage: obj.postprocess_predictions(result, metadata) and returns: result