code: def fit(self, X=None, y=None, **fit_params):
      fit_params_steps = self._check_fit_params(**fit_params)
      X, y, _ = self._fit(X, y, **fit_params_steps)
  
      with _print_elapsed_time(
          "Pipeline", self._log_message(len(self.steps) - 1)
      ):
          if self._final_estimator != "passthrough":
              fit_params_last_step = fit_params_steps[self.steps[-1][0]]
              fitted_estimator = self._memory_fit(
                  clone(self.steps[-1][1]), X, y, **fit_params_last_step
              )
              # Hacky way to make sure that the state of the estimator
              # loaded from cache is carried over to the estimator
              # in steps
              _copy_estimator_state(fitted_estimator, self.steps[-1][1])
  
      return self
  
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 12 for calling the Pipeline obj's underlying member method code with example usage: obj.fit(X, y) and returns: self