code: def fit(self, stan_init, stan_data, **kwargs):
      if "inits" not in kwargs and "init" in kwargs:
          stan_init = self.sanitize_custom_inits(
              stan_init, kwargs["init"]
          )
          del kwargs["init"]
  
      inits_list, data_list = self.prepare_data(stan_init, stan_data)
      args = dict(
          data=data_list,
          inits=inits_list,
          algorithm="Newton" if data_list["T"] < 100 else "LBFGS",
          iter=int(1e4),
      )
      args.update(kwargs)
  
      try:
          self.stan_fit = self.model.optimize(**args)
      except RuntimeError as e:
          # Fall back on Newton
          if not self.newton_fallback or args["algorithm"] == "Newton":
              raise e
          logger.warning(
              "Optimization terminated abnormally. Falling back to Newton."
          )
          args["algorithm"] = "Newton"
          self.stan_fit = self.model.optimize(**args)
      params = self.stan_to_dict_numpy(
          self.stan_fit.column_names, self.stan_fit.optimized_params_np
      )
      for par in params:
          params[par] = params[par].reshape((1, -1))
      return params
  
imports: from __future__ import absolute_import, division, print_function
  from abc import abstractmethod, ABC
  from dataclasses import dataclass
  from typing import Sequence, Tuple
  from collections import OrderedDict
  from enum import Enum
  import importlib_resources
  import platform
  import logging
label_desc: Use image node_id 4 for calling the CmdStanPyBackend obj's underlying member method code with example usage: obj.fit(stan_init, stan_data) and returns: params