code: def forward(
      self, preds: Dict[str, torch.Tensor], feature_name: str
  ) -> FeaturePostProcessingOutputDict:
      predictions = output_feature_utils.get_output_feature_tensor(
          preds, feature_name, self.predictions_key
      )
      logits = output_feature_utils.get_output_feature_tensor(
          preds, feature_name, self.logits_key
      )
  
      return {
          self.predictions_key: predictions,
          self.logits_key: logits,
      }
  
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 2 for calling the _VectorPostprocessing obj's underlying member method code with example usage: obj.forward(preds, feature_name) and returns: dict