code: def __init__(
      self,
      input_feature_config: DateInputFeatureConfig,
      encoder_obj=None,
      **kwargs,
  ):
      super().__init__(input_feature_config, **kwargs)
  
      if encoder_obj:
          self.encoder_obj = encoder_obj
      else:
          self.encoder_obj = self.initialize_encoder(
              input_feature_config.encoder
          )
  
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 1 to create a new DateInputFeature object from inherited base classes: DateFeatureMixin, InputFeature with example: obj = DateInputFeature(input_feature_config, encoder_obj)