class_docstr: Forecaster base model for lstm, mtnet, seq2seq and tcn forecasters.
class_docstr_tok: ["Forecaster","base","model","for","lstm",",","mtnet",",","seq2seq","and","tcn","forecasters","."]
code: def __init__(self, **kwargs):
      if self.distributed:
  
          def model_creator_orca(config):
              set_pytorch_seed(self.seed)
              model = self.model_creator(
                  {**self.config, **self.data_config}
              )
              model.train()
              return model
  
          self.internal = Estimator.from_torch(
              model=model_creator_orca,
              optimizer=self.optimizer_creator,
              loss=self.loss_creator,
              metrics=[ORCA_METRICS[name]() for name in self.metrics],
              backend=self.distributed_backend,
              use_tqdm=True,
              config={"lr": self.lr},
              workers_per_node=self.workers_per_node,
          )
      else:
          set_pytorch_seed(self.seed)
          self.internal = self.local_model(check_optional_config=False)
  
file_path: analytics-zoo/pyzoo/zoo/chronos/forecaster/base_forecaster.py
imports: from zoo.chronos.forecaster.abstract import Forecaster
  from zoo.orca.data.shard import SparkXShards
  from zoo.chronos.forecaster.utils import np_to_creator, set_pytorch_seed, check_data, xshard_to_np, np_to_xshard
  import numpy
  from zoo.orca.learn.pytorch.estimator import Estimator
  from zoo.orca.learn.metrics import MSE, MAE
label_desc: Use image node_id 1 to create a new BasePytorchForecaster object from inherited base classes: Forecaster with example: obj = BasePytorchForecaster()