docstr: Fits the model.
  
  Parameters
  ----------
  x : numpy array of shape [n_samples, n_features]
      Inputs of examples for fitting the model.
  
  y : numpy array of shape [n_samples]
      Outputs of examples for fitting the model.
  
  Returns
  -------
  None
docstr_tok: ["Fits","the","model",".","Parameters","--","--","--","--","--","x",":","numpy","array","of","shape","[","n_samples",",","n_features","]","Inputs","of","examples","for","fitting","the","model",".","y",":","numpy","array","of","shape","[","n_samples","]","Outputs","of","examples","for","fitting","the","model",".","Returns","--","--","--","-","None"]
code: def fit(self, x: np.array, y: np.array) -> None:
      """Fits the model.
  
      Parameters
      ----------
      x : numpy array of shape [n_samples, n_features]
          Inputs of examples for fitting the model.
  
      y : numpy array of shape [n_samples]
          Outputs of examples for fitting the model.
  
      Returns
      -------
      None
      """
  
      self.model.fit(x, y, **self.fit_params)
      self.clean = False
  
imports: import abc
  from typing import Dict
  import numpy
  from sklearn.base import BaseEstimator
  from lightwood.analysis.nc.util import t_softmax
label_desc: Use image node_id 2 for calling the BaseModelAdapter obj's underlying member method code with example usage: obj.fit(x, y) without return types