code: def test_feature_use_previous_pd_timedelta(es):
      value = IdentityFeature(es["log"].ww["id"])
      td = pd.Timedelta(12, "W")
      count_primitive = Count()
      count_feature = AggregationFeature(
          value,
          "customers",
          count_primitive,
          use_previous=td,
      )
      dictionary = {
          "ft_version": __version__,
          "schema_version": FEATURES_SCHEMA_VERSION,
          "entityset": es.to_dictionary(),
          "feature_list": [
              count_feature.unique_name(),
              value.unique_name(),
          ],
          "feature_definitions": {
              count_feature.unique_name(): count_feature.to_dictionary(),
              value.unique_name(): value.to_dictionary(),
          },
      }
      dictionary["primitive_definitions"] = {
          "0": serialize_primitive(count_primitive)
      }
      dictionary["feature_definitions"][count_feature.unique_name()][
          "arguments"
      ]["primitive"] = "0"
      deserializer = FeaturesDeserializer(dictionary)
  
      expected = [count_feature, value]
      assert expected == deserializer.to_list()
  
imports: import logging
  from unittest.mock import patch
  import pandas
  import pytest
  from featuretools import AggregationFeature, Feature, IdentityFeature, TransformFeature, __version__
  from featuretools.feature_base.features_deserializer import FeaturesDeserializer
  from featuretools.primitives import Count, Max, MultiplyNumericScalar, NMostCommon, NumberOfCommonWords, NumUnique
  from featuretools.primitives.utils import serialize_primitive
  from featuretools.utils.schema_utils import FEATURES_SCHEMA_VERSION
label_desc: Use image node_id 10 for calling a global function with example usage: test_feature_use_previous_pd_timedelta(es) without return types