class_docstr: Compatibility layer from River to scikit-learn for transformation.
  
  Parameters
  ----------
  river_estimator
class_docstr_tok: ["Compatibility","layer","from","River","to","scikit-learn","for","transformation",".","Parameters","--","--","--","--","--","river_estimator"]
docstr: Fits to an entire dataset contained in memory.
  
  Parameters
  ----------
  X
      array-like of shape (n_samples, n_features).
  y
      array-like of shape n_samples.
  
  Returns
  -------
  self
docstr_tok: ["Fits","to","an","entire","dataset","contained","in","memory",".","Parameters","--","--","--","--","--","X","array-like","of","shape","(","n_samples",",","n_features",")",".","y","array-like","of","shape","n_samples",".","Returns","--","--","--","-","self"]
code: def fit(self, X, y=None):
      """Fits to an entire dataset contained in memory.
  
      Parameters
      ----------
      X
          array-like of shape (n_samples, n_features).
      y
          array-like of shape n_samples.
  
      Returns
      -------
      self
  
      """
  
      # Reset the state if already fitted
      for attr in ("instance_", "n_features_in_"):
          self.__dict__.pop(attr, None)
  
      # Fit with one pass of the dataset
      return self._partial_fit(X, y)
  
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 3 for calling the River2SKLTransformer obj's underlying member method code with example usage: obj.fit(X, y) and returns: self