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.
code: def __init__(self, data, n_steps, n_timeseries, label_map, margin=0):
      self.label_map = label_map
      self.data = data
      self.n_steps = n_steps
      self.n_timeseries = n_timeseries
      self.batches = gs.array([])
      self.margin = margin
      self.covs = gs.array([])
      self.labels = gs.array([])
      self.covecs = gs.array([])
      self.diags = gs.array([])
  
imports: import numpy
  import geomstats.backend
  from geomstats.geometry.symmetric_matrices import SymmetricMatrices
label_desc: Use image node_id 1 to create a new TimeSeriesCovariance object  with example: obj = TimeSeriesCovariance(data, n_steps, n_timeseries, label_map, margin)