code: def __init__(
      self,
      input_feature_config: VectorInputFeatureConfig,
      encoder_obj=None,
      **kwargs,
  ):
      super().__init__(input_feature_config, **kwargs)
  
      # input_feature_config.encoder.input_size = input_feature_config.encoder.vector_size
      if encoder_obj:
          self.encoder_obj = encoder_obj
      else:
          self.encoder_obj = self.initialize_encoder(
              input_feature_config.encoder
          )
  
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 VectorInputFeature object from inherited base classes: VectorFeatureMixin, InputFeature with example: obj = VectorInputFeature(input_feature_config, encoder_obj)