class_docstr: Class for generating a list of covariance matrices from time series.
  
  Prepare a TimeSeriesCovariance Object from time series in dictionary.
  
  Parameters
  ----------
  data_dict : dict
      Dictionary with 'time', 'raw_data', 'label' as key
      and the corresponding array as values.
  n_steps : int
      Size of the batches.
  n_timeseries : int
      The number of electrodes used for the recording.
  label_map : dictionary
      Encode the label into digits.
  margin : int
      Number of index to remove before and after a sign change (Can
      help getting a stationary signal).
  
  Attributes
  ----------
  label_map : dictionary
      Encode the label into digits.
  data_dict : dict
      Dictionary with 'time', 'raw_data', 'label' as key
      and the corresponding array as values.
  n_steps : int
      Size of the batches.
  n_timeseries : int
      The number of electrodes used for the recording.
  batches : array
      The start indexes of the batches to use to compute covariance matrices.
  margin : int
      Number of index to remove before and after a sign change (Can
      help getting a stationary signal).
  covs : array
      The covariance matrices.
  labels : array
      The digit labels corresponding to each batch.
  covec : array
      The vectorized version of the covariance matrices.
  diags : array
      The covariance matrices diagonals.
docstr: Convert the labels into digits.
docstr_tok: ["Convert","the","labels","into","digits","."]
code: def _format_labels(self):
      """Convert the labels into digits."""
      self.data["y"] = gs.array(
          [self.label_map[x] for x in self.data["label"]]
      )
  
imports: import numpy
  import geomstats.backend
  from geomstats.geometry.symmetric_matrices import SymmetricMatrices
label_desc: Use image node_id 2 for calling the TimeSeriesCovariance obj's underlying member method code with example usage: obj._format_labels() without return types