code: def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor:
      if torch.jit.isinstance(v, List[torch.Tensor]):
          v = torch.stack(v)
  
      if torch.jit.isinstance(v, torch.Tensor):
          return v.to(dtype=torch.int)
      else:
          raise ValueError(f"Unsupported input: {v}")
  
imports: import logging
  from datetime import date, datetime
  from typing import Dict, List
  import numpy
  import torch
  from ludwig.constants import COLUMN, DATE, PROC_COLUMN
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature
  from ludwig.schema.features.date_feature import DateInputFeatureConfig
  from ludwig.types import FeatureConfigDict, FeatureMetadataDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils.date_utils import create_vector_from_datetime_obj, parse_datetime
  from ludwig.utils.types import DataFrame, TorchscriptPreprocessingInput
label_desc: Use image node_id 2 for calling the _DatePreprocessing obj's underlying member method code with example usage: obj.forward(v) and returns: v