class_docstr: Compatibility layer from River to scikit-learn for regression.
  
  Parameters
  ----------
  river_estimator
class_docstr_tok: ["Compatibility","layer","from","River","to","scikit-learn","for","regression",".","Parameters","--","--","--","--","--","river_estimator"]
code: def _partial_fit(self, X, y):
      # Check the inputs
      X, y = utils.check_X_y(
          X, y, **SKLEARN_INPUT_X_PARAMS, **SKLEARN_INPUT_Y_PARAMS
      )
  
      # Store the number of features so that future inputs can be checked
      if (
          hasattr(self, "n_features_in_")
          and X.shape[1] != self.n_features_in_
      ):
          raise ValueError(
              f"Expected {self.n_features_in_} features, got {X.shape[1]}"
          )
      self.n_features_in_ = X.shape[1]
  
      # scikit-learn's convention is that fit shouldn't mutate the input parameters; we have to
      # deep copy the provided estimator in order to respect this convention
      if not hasattr(self, "instance_"):
          self.instance_ = copy.deepcopy(self.river_estimator)
  
      # Call learn_one for each observation
      for x, yi in STREAM_METHODS[type(X)](X, y):
          self.instance_.learn_one(x, yi)
  
      return self
  
imports: from __future__ import annotations
  import copy
  import typing
  import numpy
  from sklearn import base
  from sklearn import pipeline, preprocessing, utils
  from river import base, compose, stream
label_desc: Use image node_id 2 for calling the River2SKLRegressor obj's underlying member method code with example usage: obj._partial_fit(X, y) and returns: self