code: def create_sample_input(self, batch_size: int = 2):
      date = [2013, 2, 26, 1, 57, 0, 0, 0, 0]
      return torch.Tensor([date for _ in range(batch_size)]).type(
          torch.int32
      )
  
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 7 for calling the DateInputFeature obj's underlying member method code with example usage: obj.create_sample_input(batch_size) and returns: torch