code: def __init__(self) -> None:
      super().__init__()
      self._ml_usecase = MLUsecase.REGRESSION
      self.exp_name_log = "reg-default-name"
      self._variable_keys = self._variable_keys.union(
          {
              "transform_target_param",
          }
      )
      self._available_plots = {
          "pipeline": "Pipeline Plot",
          "parameter": "Hyperparameters",
          "residuals": "Residuals",
          "error": "Prediction Error",
          "cooks": "Cooks Distance",
          "rfe": "Feature Selection",
          "learning": "Learning Curve",
          "manifold": "Manifold Learning",
          "vc": "Validation Curve",
          "feature": "Feature Importance",
          "feature_all": "Feature Importance (All)",
          "tree": "Decision Tree",
          "residuals_interactive": "Interactive Residuals",
      }
  
imports: import logging
  import re
  import time
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from joblib.memory import Memory
  from pycaret.containers.metrics.regression import get_all_metric_containers
  from pycaret.containers.models.regression import ALL_ALLOWED_ENGINES, get_all_model_containers, get_container_default_engines
  from pycaret.internal.display import CommonDisplay
  from pycaret.internal.logging import get_logger
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.internal.pipeline import Pipeline
  from pycaret.internal.preprocess.preprocessor import Preprocessor
  from pycaret.internal.pycaret_experiment.non_ts_supervised_experiment import _NonTSSupervisedExperiment
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE, TARGET_LIKE
  from pycaret.utils.generic import MLUsecase, highlight_setup
label_desc: Use image node_id 1 to create a new RegressionExperiment object from inherited base classes: _NonTSSupervisedExperiment, Preprocessor with example: obj = RegressionExperiment()