class_docstr: This ensemble performs no aggregation. User can define an "active mixer" and calling the ensemble will call said mixer. 
  
  Ideal for use cases with single mixers where (potentially expensive) evaluation runs are done internally, as in `BestOf`.
class_docstr_tok: ["This","ensemble","performs","no","aggregation",".","User","can","define","an","``","active","mixer","''","and","calling","the","ensemble","will","call","said","mixer",".","Ideal","for","use","cases","with","single","mixers","where","(","potentially","expensive",")","evaluation","runs","are","done","internally",",","as","in","`","BestOf","`","."]
code: def __init__(
      self,
      target,
      mixers: List[BaseMixer],
      data: EncodedDs,
      args: PredictionArguments,
  ) -> None:
      super().__init__(target, mixers, data=data)
      self._active_mixer = 0
      single_row_ds = EncodedDs(
          data.encoders, data.data_frame.iloc[[0]], data.target
      )
      _ = self.mixers[self._active_mixer](single_row_ds, args)[
          "prediction"
      ]  # prime mixer for storage, needed because NHitsMixer.model (neuralforecast.NHITS) is not serializable without this, oddly enough. Eventually, check this again and remove if possible!  # noqa
      self.prepared = True
  
file_path: lightwood/lightwood/ensemble/identity.py
imports: from typing import List
  import pandas
  from lightwood.mixer.base import BaseMixer
  from lightwood.ensemble.base import BaseEnsemble
  from lightwood.api.types import PredictionArguments
  from lightwood.data.encoded_ds import EncodedDs
label_desc: Use image node_id 1 to create a new IdentityEnsemble object from inherited base classes: BaseEnsemble with example: obj = IdentityEnsemble(target, mixers, data, args)