docstr: Fit the model.
  
  Fit all the transforms/samplers one after the other and
  transform/sample the data, then fit the transformed/sampled
  data using the final estimator.
  
  Parameters
  ----------
  X : iterable
      Training data. Must fulfill input requirements of first step of the
      pipeline.
  
  y : iterable, default=None
      Training targets. Must fulfill label requirements for all steps of
      the pipeline.
  
  **fit_params : dict of str -> object
      Parameters passed to the ``fit`` method of each step, where
      each parameter name is prefixed such that parameter ``p`` for step
      ``s`` has key ``s__p``.
  
  Returns
  -------
  self : Pipeline
      This estimator.
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code: def partial_fit(self, X, y=None, classes=None, **fit_params):
      """Fit the model.
  
      Fit all the transforms/samplers one after the other and
      transform/sample the data, then fit the transformed/sampled
      data using the final estimator.
  
      Parameters
      ----------
      X : iterable
          Training data. Must fulfill input requirements of first step of the
          pipeline.
  
      y : iterable, default=None
          Training targets. Must fulfill label requirements for all steps of
          the pipeline.
  
      **fit_params : dict of str -> object
          Parameters passed to the ``fit`` method of each step, where
          each parameter name is prefixed such that parameter ``p`` for step
          ``s`` has key ``s__p``.
  
      Returns
      -------
      self : Pipeline
          This estimator.
      """
      try:
          self.Xt_
      except Exception:
          self.Xt_ = None
          self.yt_ = None
      if self.Xt_ is None or self.yt_ is None:
          Xt, yt, _ = self._fit(X, y)
          self.Xt_ = Xt
          self.yt_ = yt
      else:
          Xt = self.Xt_
          yt = self.yt_
      with _print_elapsed_time(
          "Pipeline", self._log_message(len(self.steps) - 1)
      ):
          if self._final_estimator != "passthrough":
              # the try...except block is a workaround until tune-sklearn updates
              try:
                  self._final_estimator.partial_fit(
                      Xt, yt, classes=classes, **fit_params
                  )
              except TypeError:
                  self._final_estimator.partial_fit(
                      Xt, yt, **fit_params
                  )
      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 25 for calling the Pipeline obj's underlying member method code with example usage: obj.partial_fit(X, y, classes) and returns: self