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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def lyap_e_len(**kwargs):
 """ Helper function that calculates the minimum number of data points required to use lyap_e. Note that none of the required parameters may be set to None. Kwargs: kwargs(dict):
 arguments used for lyap_e (required: emb_dim, matrix_dim, min_nb and min_tsep) Returns: minimum number of data points required to call lyap_e with the given parameters """

  m = (kwargs['emb_dim'] - 1) // (kwargs['matrix_dim'] - 1)
  # minimum length required to find single orbit vector
  min_len = kwargs['emb_dim']
  # we need to follow each starting point of an orbit vector for m more steps
  min_len += m
  # we need min_tsep * 2 + 1 orbit vectors to find neighbors for each
  min_len += kwargs['min_tsep'] * 2
  # we need at least min_nb neighbors for each orbit vector
  min_len += kwargs['min_nb']
  return min_len