code: def _get_n_fit_scalars_per_param(self):
      nc = self.n_components
      nf = self.n_features
      return {
          "s": nc - 1,
          "t": nc * (nc - 1),
          "e": nc * (nf - 1),
      }
  
imports: import functools
  import inspect
  import warnings
  import numpy
  from scipy import special
  from scipy.stats import multinomial, poisson
  from sklearn.utils import check_random_state
  from .base import BaseHMM, _AbstractHMM
  from .stats import log_multivariate_normal_density
  from .utils import fill_covars, log_normalize
label_desc: Use image node_id 3 for calling the BaseCategoricalHMM obj's underlying member method code with example usage: obj._get_n_fit_scalars_per_param() and returns: dict