code: def __init__(self, **kwargs):
      if self.seed:
          # TF seed can't be set to None, just to be consistent with torch.
          warnings.warn(
              "If users want to set the random seed for training, it's better to "
              f"call 'tf.keras.utils.set_random_seed({self.seed})' manually."
          )
          from tensorflow.keras.utils import set_random_seed
  
          set_random_seed(seed=self.seed)
      if self.distributed:
          from bigdl.orca.learn.tf2.estimator import Estimator
  
          self.internal = Estimator.from_keras(
              model_creator=self.model_creator,
              config=self.model_config,
              workers_per_node=self.workers_per_node,
              backend=self.remote_distributed_backend,
          )
      else:
          self.internal = self.model_creator({**self.model_config})
          self.accelerated_model = None  # accelerated model obtained from various accelerators
          self.accelerate_method = (
              None  # str indicates current accelerate method
          )
  
      self.fitted = False
  
imports: import warnings
  import keras
  import tensorflow
  import numpy
  from bigdl.chronos.forecaster.abstract import Forecaster
  from bigdl.chronos.data import TSDataset
  from bigdl.chronos.metric.forecast_metrics import Evaluator
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.chronos.forecaster.tf.utils import np_to_data_creator, tsdata_to_data_creator, np_to_xshards, xshard_to_np, np_to_tfdataset
label_desc: Use image node_id 1 to create a new BaseTF2Forecaster object from inherited base classes: Forecaster with example: obj = BaseTF2Forecaster()