class_docstr: Base class for MLP classification and regression.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
  
  .. versionadded:: 0.18
docstr: Fit the model to data matrix X and target y.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape (n_samples, n_features)
      The input data.
  
  y : array-like, shape (n_samples,)
      The target values.
  
  Returns
  -------
  self : returns a trained MLP model.
docstr_tok: ["Fit","the","model","to","data","matrix","X","and","target","y.","Parameters","--","--","--","--","--","X",":","{","array-like",",","sparse","matrix","}",",","shape","(","n_samples",",","n_features",")","The","input","data",".","y",":","array-like",",","shape","(","n_samples",",",")","The","target","values",".","Returns","--","--","--","-","self",":","returns","a","trained","MLP","model","."]
code: def partial_fit(self):
      """Fit the model to data matrix X and target y.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape (n_samples, n_features)
          The input data.
  
      y : array-like, shape (n_samples,)
          The target values.
  
      Returns
      -------
      self : returns a trained MLP model.
      """
      if self.solver not in _STOCHASTIC_SOLVERS:
          raise AttributeError(
              "partial_fit is only available for stochastic"
              " optimizers. %s is not stochastic." % self.solver
          )
      return self._partial_fit
  
imports: import numpy
  from abc import ABCMeta, abstractmethod
  from scipy.optimize import fmin_l_bfgs_b
  import warnings
  from ..base import BaseEstimator, ClassifierMixin, RegressorMixin
  from ._base import ACTIVATIONS, DERIVATIVES, LOSS_FUNCTIONS
  from ._stochastic_optimizers import SGDOptimizer, AdamOptimizer
  from ..model_selection import train_test_split
  from ..externals import six
  from ..preprocessing import LabelBinarizer
  from ..utils import gen_batches, check_random_state
  from ..utils import shuffle
  from ..utils import check_array, check_X_y, column_or_1d
  from ..exceptions import ConvergenceWarning
  from ..utils.extmath import safe_sparse_dot
  from ..utils.validation import check_is_fitted
  from ..utils.multiclass import _check_partial_fit_first_call, unique_labels
  from ..utils.multiclass import type_of_target
label_desc: Use image node_id 15 for calling the BaseMultilayerPerceptron obj's underlying member method code with example usage: obj.partial_fit() and returns: self