docstr: Returns the encoder variable importance of the TFT model.
  
  The encoder_weights are calculated for the past inputs of the model.
  The encoder_importance contains the weights of the encoder variable selection network.
  The encoder variable selection network is used to select the most important static and time dependent
  covariates. It provides insights which variable are most significant for the prediction problem.
  See section 4.2 of the paper for more details.
  
  Returns
  -------
  pd.DataFrame
      The encoder variable importance.
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code: def _encoder_importance(self) -> pd.DataFrame:
      """Returns the encoder variable importance of the TFT model.
  
      The encoder_weights are calculated for the past inputs of the model.
      The encoder_importance contains the weights of the encoder variable selection network.
      The encoder variable selection network is used to select the most important static and time dependent
      covariates. It provides insights which variable are most significant for the prediction problem.
      See section 4.2 of the paper for more details.
  
      Returns
      -------
      pd.DataFrame
          The encoder variable importance.
      """
      return self._get_importance(
          weight=self.model.model._encoder_sparse_weights,
          names=self.model.model.encoder_variables,
      )
  
imports: from typing import Dict, List, Optional, Sequence, Union
  import matplotlib.axes
  import matplotlib.pyplot
  import numpy
  import pandas
  from torch import Tensor
  from darts import TimeSeries
  from darts.explainability import TFTExplainabilityResult
  from darts.explainability.explainability import _ForecastingModelExplainer
  from darts.logging import get_logger, raise_log
  from darts.models import TFTModel
  from darts.utils.timeseries_generation import generate_index
label_desc: Use image node_id 5 for calling the TFTExplainer obj's underlying member method code with example usage: obj._encoder_importance() and returns: self