code: def predict(self, X=None, fh=None, **predict_params):
      Xt = X
      if Xt is not None:
          Xt, _ = self._memory_full_transform(
              self, Xt, None, with_final=False
          )
      return self.steps[-1][-1].predict(fh=fh, X=Xt, **predict_params)
  
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 3 for calling the TimeSeriesPipeline obj's underlying member method code with example usage: obj.predict(X, fh) and returns: self