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","."]
docstr: Transform static features and past/known covariates.
  
  Transformed data is guaranteed to match the specification (same columns / dtypes) of the data seen during fit.
  Extra columns not seen during fitting will be removed.
  
  If some columns are missing or are incompatible, an exception will be raised.
docstr_tok: ["Transform","static","features","and","past\/known","covariates",".","Transformed","data","is","guaranteed","to","match","the","specification","(","same","columns","\/","dtypes",")","of","the","data","seen","during","fit",".","Extra","columns","not","seen","during","fitting","will","be","removed",".","If","some","columns","are","missing","or","are","incompatible",",","an","exception","will","be","raised","."]
code: def transform(
      self, data: TimeSeriesDataFrame, data_frame_name: str = "data"
  ) -> TimeSeriesDataFrame:
      """Transform static features and past/known covariates.
  
      Transformed data is guaranteed to match the specification (same columns / dtypes) of the data seen during fit.
      Extra columns not seen during fitting will be removed.
  
      If some columns are missing or are incompatible, an exception will be raised.
      """
      assert (
          self._is_fit
      ), f"{self.__class__.__name__} has not been fit yet"
      # Avoid modifying inplace
      data = data.copy(deep=False)
  
      data = self._check_and_prepare_covariates(
          data=data,
          required_column_names=self.required_column_names,
          data_frame_name=data_frame_name,
      )
  
      if self.static_feature_pipeline.is_fit():
          if data.static_features is None:
              raise ValueError(
                  f"Provided {data_frame_name} must contain static_features"
              )
          static_features = self.static_feature_pipeline.transform(
              data.static_features
          )
          data.static_features = (
              self._convert_numerical_features_to_float(static_features)
          )
      else:
          data.static_features = None
  
      return data
  
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 6 for calling the TimeSeriesFeatureGenerator obj's underlying member method code with example usage: obj.transform(data, data_frame_name) and returns: data