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 fit_transform(
      self, data: TimeSeriesDataFrame, data_frame_name: str = "data"
  ) -> TimeSeriesDataFrame:
      self.fit(data)
      return self.transform(data, data_frame_name=data_frame_name)
  
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 8 for calling the TimeSeriesFeatureGenerator obj's underlying member method code with example usage: obj.fit_transform(data, data_frame_name) and returns: self