code: def forward(self, inputs: torch.Tensor) -> torch.Tensor:
      assert isinstance(inputs, torch.Tensor)
      assert inputs.dtype in [torch.float32, torch.float64]
      assert len(inputs.shape) == 2
  
      inputs_encoded = self.encoder_obj(inputs)
  
      return inputs_encoded
  
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 VectorInputFeature obj's underlying member method code with example usage: obj.forward(inputs) and returns: inputs_encoded