code: def _get_fit_params(self, X, y, **fit_params):
      fit_params_steps = {
          name: {} for name, step in self.steps if step is not None
      }
      for pname, pval in fit_params.items():
          if "__" not in pname:
              raise ValueError(
                  "Pipeline.fit does not accept the {} parameter. "
                  "You can pass parameters to specific steps of your "
                  "pipeline using the stepname__parameter format, e.g. "
                  "`Pipeline.fit(X, y, logisticregression__sample_weight"
                  "=sample_weight)`.".format(pname)
              )
          step, param = pname.split("__", 1)
          fit_params_steps[step][param] = pval
      return X, y, fit_params_steps[self.steps[-1][0]]
  
imports: import platform
  import warnings
  from copy import deepcopy
  from inspect import signature
  import imblearn.pipeline
  import sklearn.pipeline
  from sklearn.base import clone
  from sklearn.utils import _print_elapsed_time
  from sklearn.utils.metaestimators import available_if
  from sklearn.utils.validation import check_memory
  from pycaret.utils._show_versions import _get_deps_info
  from pycaret.utils.generic import get_all_object_vars_and_properties, variable_return
label_desc: Use image node_id 1 for calling the TimeSeriesPipeline obj's underlying member method code with example usage: obj._get_fit_params(X, y) and returns: X,  y,  fit_params_steps