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 __init__(self, target: str, known_covariates_names: List[str]):
      self.target = target
      self._is_fit = False
      self.known_covariates_names = list(known_covariates_names)
      self.past_covariates_names = []
      self.static_feature_pipeline = (
          ContinuousAndCategoricalFeatureGenerator()
      )
      self.covariate_metadata: CovariateMetadata = 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 1 to create a new TimeSeriesFeatureGenerator object  with example: obj = TimeSeriesFeatureGenerator(target, known_covariates_names)