code: def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor:
      if torch.jit.isinstance(v, torch.Tensor):
          out = v
      elif torch.jit.isinstance(v, List[torch.Tensor]):
          out = torch.stack(v)
      elif torch.jit.isinstance(v, List[str]):
          vectors = []
          for sample in v:
              vector = torch.tensor(
                  [float(x) for x in sample.split()],
                  dtype=torch.float32,
              )
              vectors.append(vector)
          out = torch.stack(vectors)
      else:
          raise ValueError(f"Unsupported input: {v}")
  
      if out.isnan().any():
          raise ValueError(
              "Scripted NaN handling not implemented for Vector feature"
          )
      return out
  
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 for calling the _VectorPreprocessing obj's underlying member method code with example usage: obj.forward(v) and returns: out