docstr: Returns the feature name mapping of the TFT model.
  
  Returns
  -------
  Dict[str, str]
      The feature name mapping. For example
      {
          'target_0': 'ice cream',
          'past_covariate_0': 'heater',
          'past_covariate_1': 'year',
          'past_covariate_2': 'month',
          'future_covariate_0': 'darts_enc_fc_cyc_month_sin',
          'future_covariate_1': 'darts_enc_fc_cyc_month_cos',
       }
docstr_tok: ["Returns","the","feature","name","mapping","of","the","TFT","model",".","Returns","--","--","--","-","Dict","[","str",",","str","]","The","feature","name","mapping",".","For","example","{","'target_0","'",":","'ice","cream","'",",","'past_covariate_0","'",":","'heater","'",",","'past_covariate_1","'",":","'year","'",",","'past_covariate_2","'",":","'month","'",",","'future_covariate_0","'",":","'darts_enc_fc_cyc_month_sin","'",",","'future_covariate_1","'",":","'darts_enc_fc_cyc_month_cos","'",",","}"]
code: def _name_mapping(self) -> Dict[str, str]:
      """Returns the feature name mapping of the TFT model.
  
      Returns
      -------
      Dict[str, str]
          The feature name mapping. For example
          {
              'target_0': 'ice cream',
              'past_covariate_0': 'heater',
              'past_covariate_1': 'year',
              'past_covariate_2': 'month',
              'future_covariate_0': 'darts_enc_fc_cyc_month_sin',
              'future_covariate_1': 'darts_enc_fc_cyc_month_cos',
           }
      """
  
      def map_cols(comps, name, suffix):
          comps = comps if comps is not None else []
          return {
              f"{name}_{i}": colname + f"_{suffix}"
              for i, colname in enumerate(comps)
          }
  
      return {
          **map_cols(self.target_components, "target", "target"),
          **map_cols(
              self.static_covariates_components,
              "static_covariate",
              "statcov",
          ),
          **map_cols(
              self.past_covariates_components,
              "past_covariate",
              "pastcov",
          ),
          **map_cols(
              self.future_covariates_components,
              "future_covariate",
              "futcov",
          ),
      }
  
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 9 for calling the TFTExplainer obj's underlying member method code with example usage: obj._name_mapping() and returns: dict, unknown