class_docstr: Stores the explainability results of a :class:`TFTExplainer <darts.explainability.tft_explainer.TFTExplainer>`
  with convenient access to the results. It extends the :class:`ComponentBasedExplainabilityResult` and carries
  information specific to the TFT explainer.
  
  - :func:`get_attention() <TFTExplainabilityResult.get_attention>`: self attention over the encoder and decoder
  - :func:`get_encoder_importance() <TFTExplainabilityResult.get_encoder_importance>`: encoder feature importances
    including past target, past covariates, and historic part of future covariates.
  - :func:`get_decoder_importance() <TFTExplainabilityResult.get_decoder_importance>`: decoder feature importances
    including future part of future covariates.
  - :func:`get_static_covariates_importance() <TFTExplainabilityResult.get_static_covariates_importance>`: static
    covariates importances.
  - :func:`get_feature_importances() <TFTExplainabilityResult.get_feature_importances>`: get all feature importances
    at once.
  
  Examples
  --------
  >>> explainer = TFTExplainer(model)  # requires `background` if model was trained on multiple series
  >>> explain_results = explainer.explain()
  >>> attention = explain_results.get_attention()
  >>> importances = explain_results.get_feature_importances()
  >>> encoder_importance = explain_results.get_encoder_importance()
  >>> decoder_importance = explain_results.get_decoder_importance()
  >>> static_covariates_importance = explain_results.get_static_covariates_importance()
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docstr: Returns the time-dependent encoder importances as a pd.DataFrames.
  If multiple series were used in :func:`TFTExplainer.explain()
  <darts.explainability.tft_explainer.TFTExplainer.explain>`, returns a list of pd.DataFrames.
docstr_tok: ["Returns","the","time-dependent","encoder","importances","as","a","pd.DataFrames",".","If","multiple","series","were","used","in",":","func",":","`","TFTExplainer.explain","(",")","<","darts.explainability.tft_explainer.TFTExplainer.explain",">","`",",","returns","a","list","of","pd.DataFrames","."]
code: def get_encoder_importance(
      self,
  ) -> Union[pd.DataFrame, List[pd.DataFrame]]:
      """
      Returns the time-dependent encoder importances as a pd.DataFrames.
      If multiple series were used in :func:`TFTExplainer.explain()
      <darts.explainability.tft_explainer.TFTExplainer.explain>`, returns a list of pd.DataFrames.
      """
      return self.get_explanation("encoder_importance")
  
imports: from abc import ABC, abstractmethod
  from typing import Any, Dict, List, Optional, Union
  import pandas
  import shap
  from darts import TimeSeries
  from darts.logging import get_logger, raise_if, raise_if_not, raise_log
label_desc: Use image node_id 4 for calling the TFTExplainabilityResult obj's underlying member method code with example usage: obj.get_encoder_importance() and returns: self