class_docstr: Scikit-learn compatible interface for TabularPredictor.
class_docstr_tok: ["Scikit-learn","compatible","interface","for","TabularPredictor","."]
code: def __init__(
      self,
      predictor_init_kwargs: Optional[dict] = None,
      predictor_fit_kwargs: Optional[dict] = None,
  ):
      self.predictor_init_kwargs = (
          predictor_init_kwargs
          if predictor_init_kwargs is not None
          else {}
      )
      self.predictor_fit_kwargs = (
          predictor_fit_kwargs
          if predictor_fit_kwargs is not None
          else {}
      )
  
imports: import logging
  import math
  import os
  import time
  from typing import Any, Dict, List, Optional, Tuple
  import numpy
  import pandas
  from sklearn.base import BaseEstimator
  import autogluon.core
  from autogluon.common.utils.log_utils import set_logger_verbosity
  from autogluon.tabular import TabularPredictor
  from autogluon.timeseries.dataset.ts_dataframe import ITEMID, TIMESTAMP, TimeSeriesDataFrame
  from autogluon.timeseries.models.abstract import AbstractTimeSeriesModel
  from autogluon.timeseries.models.local import SeasonalNaiveModel
  from autogluon.timeseries.utils.datetime import get_lags_for_frequency, get_seasonality, get_time_features_for_frequency
  from autogluon.timeseries.utils.forecast import get_forecast_horizon_index_ts_dataframe
  from autogluon.timeseries.utils.warning_filters import warning_filter
label_desc: Use image node_id 1 to create a new TabularEstimator object from inherited base classes: BaseEstimator with example: obj = TabularEstimator(predictor_init_kwargs, predictor_fit_kwargs)