class_docstr: Takes care of preprocessing for static_features and past/known covariates.
  
  Covariates are all converted to float dtype. Static features, if present, are all converted to categorical & float
  dtypes.
class_docstr_tok: ["Takes","care","of","preprocessing","for","static_features","and","past\/known","covariates",".","Covariates","are","all","converted","to","float","dtype",".","Static","features",",","if","present",",","are","all","converted","to","categorical","&","float","dtypes","."]
code: def transform_future_known_covariates(
      self, known_covariates: Optional[TimeSeriesDataFrame]
  ) -> Optional[TimeSeriesDataFrame]:
      assert (
          self._is_fit
      ), f"{self.__class__.__name__} has not been fit yet"
      if len(self.known_covariates_names) > 0:
          assert (
              known_covariates is not None
          ), "known_covariates must be provided at prediction time"
          return self._check_and_prepare_covariates(
              known_covariates,
              required_column_names=self.known_covariates_names,
              data_frame_name="known_covariates",
          )
      else:
          return None
  
imports: import logging
  import reprlib
  from dataclasses import dataclass, field
  from typing import List, Optional
  import numpy
  import pandas
  from autogluon.common.features.types import R_FLOAT, R_INT
  from autogluon.features.generators import AsTypeFeatureGenerator, CategoryFeatureGenerator, IdentityFeatureGenerator, PipelineFeatureGenerator
  from autogluon.timeseries import TimeSeriesDataFrame
label_desc: Use image node_id 7 for calling the TimeSeriesFeatureGenerator obj's underlying member method code with example usage: obj.transform_future_known_covariates(known_covariates) and returns: self, None